xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

0acabac25e7f182a0cc9d197e74fb9a54f708fdd

629

py

Python

day10/samematrix.py

nikhilsamninan/python-files

15198459081097058a939b40b5e8ef754e578fe0

[ "Apache-2.0" ]

null

day10/samematrix.py

nikhilsamninan/python-files

15198459081097058a939b40b5e8ef754e578fe0

[ "Apache-2.0" ]

null

day10/samematrix.py

nikhilsamninan/python-files

15198459081097058a939b40b5e8ef754e578fe0

[ "Apache-2.0" ]

null

def matrix_form(): r = int(input("Enter the no of rows")) c = int(input("Enter the no of columns")) matrix=[] print("Enter the enteries") for i in range(r): a = [] for j in range(c): a.append(int(input())) matrix.append(a) return(matrix) def check_matrix(first_matrix,sec_matrix): if(first_matrix==sec_matrix): print("same") else: print("not same") print("Enter the 1st matrix") first_matrix = matrix_form() print(first_matrix) print("Enter the 2nd matrix") sec_matrix = matrix_form() print(sec_matrix) check_matrix(first_matrix,sec_matrix)

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0accac5244ae00b90c3dcaa313e0ad6674cf5f7f

5,284

py

Python

kepler.py

mdbernard/astrodynamics

cf98df6cd17086e3675c1f7c2fce342d5322ee51

[ "MIT" ]

null

kepler.py

mdbernard/astrodynamics

cf98df6cd17086e3675c1f7c2fce342d5322ee51

[ "MIT" ]

14

2020-11-10T02:37:15.000Z

2022-02-07T01:11:29.000Z

kepler.py

mdbernard/astrodynamics

cf98df6cd17086e3675c1f7c2fce342d5322ee51

[ "MIT" ]

null

import numpy as np from stumpff import C, S from CelestialBody import BODIES from numerical import newton, laguerre from lagrange import calc_f, calc_fd, calc_g, calc_gd def kepler_chi(chi, alpha, r0, vr0, mu, dt): ''' Kepler's Equation of the universal anomaly, modified for use in numerical solvers. ''' z = alpha*chi**2 return (r0*vr0/np.sqrt(mu))*chi**2*C(z) + \ (1 - alpha*r0)*chi**3*S(z) + \ r0*chi - np.sqrt(mu)*dt def dkepler_dchi(chi, alpha, r0, vr0, mu, dt): ''' Derivative of Kepler's Equation of the universal anomaly, modified for use in numerical solvers. ''' z = alpha*chi**2 return (r0*vr0/np.sqrt(mu))*chi*(1 - alpha*chi**2*S(z)) + \ (1 - alpha*r0)*chi**2*C(z) + r0 def d2kepler_dchi2(chi, alpha, r0, vr0, mu, dt): ''' Second derivative of Kepler's Equation of the universal anomaly, modified for use in numerical solvers. ''' z = alpha*chi**2 S_ = S(z) return (r0*vr0/np.sqrt(mu))*(1 - 3*z*S_ + z*(C(z) - 3*S_)) + \ chi*(1 - z*S_)*(1 - alpha*r0) def solve_kepler_chi(r_0, v_0, dt, body=BODIES['Earth'], method='laguerre', tol=1e-7, max_iters=100): ''' Solve Kepler's Equation of the universal anomaly chi using the specified numerical method. Applies Algorithm 3.4 from Orbital Mechanics for Engineering Students, 4 ed, Curtis. :param r_0: `iterable` (km) initial position 3-vector :param v_0: `iterable` (km/s) initial velocity 3-vector :param dt: `float` (s) time after initial state to solve for r, v as 3-vectors :param body: `CelestialBody` (--) the celestial body to use for orbital parameters :param method: `str` (--) which numerical method to use to solve Kepler's Equation :param tol: `float` (--) decimal tolerance for numerical method (default 1e-7 is IEEE 745 single precision) :param max_iters: `int` (--) maximum number of iterations in numerical method before breaking :return: (km) final position 3-vector, (km/s) final velocity 3-vector ''' VALID_METHODS = ('laguerre', 'newton') mu = body.mu # (km**3/s**2) gravitational parameter of the specified primary body r0 = np.linalg.norm(r_0) # (km) initial position magnitude v0 = np.linalg.norm(v_0) # (km/s) initial velocity magnitude vr0 = np.dot(v_0, r_0)/r0 # (km/s) initial radial velocity magnitude alpha = 2/r0 - v0**2/mu # (1/km) inverse of semi-major axis chi0 = np.sqrt(mu)*np.abs(alpha)*dt if method not in VALID_METHODS: print(f'Method \'{method}\' is not valid, must be one of {VALID_METHODS}.\nDefaulting to laguerre method.') chi, _, _ = laguerre(chi0, kepler_chi, dkepler_dchi, d2kepler_dchi2, alpha, r0, vr0, mu, dt) elif method == 'newton': chi, _, _ = newton(chi0, kepler_chi, dkepler_dchi, alpha, r0, vr0, mu, dt) else: # method == 'laguerre' chi, _, _ = laguerre(chi0, kepler_chi, dkepler_dchi, d2kepler_dchi2, alpha, r0, vr0, mu, dt) f = calc_f(chi, r0, alpha) g = calc_g(dt, mu, chi, alpha) r_1 = f*r_0 + g*v_0 r1 = np.linalg.norm(r_1) fd = calc_fd(mu, r1, r0, alpha, chi) gd = calc_gd(chi, r1, alpha) v_1 = fd*r_0 + gd*v_0 return r_1, v_1 def solve_kepler_E(e, Me, tol=1e-7, max_iters=100): ''' Solve Kepler's Equation in the form containing Eccentric Anomaly (E), eccentricity (e), and Mean Anomaly of Ellipse (Me). Uses Algorithm 3.1 from Orbital Mechanics for Engineering Students, 4 ed, Curtis. ''' # TODO: have this function make use of one of the numerical methods in numerical.py def f(E, e, Me): return E - e*np.sin(E) - Me def fp(E, e): return 1 - e*np.cos(E) E = Me + e/2 if Me < np.pi else Me - e/2 ratio = f(E, e, Me)/fp(E, e) iters = 0 while abs(ratio) > tol and iters < max_iters: E -= ratio ratio = f(E, e, Me)/fp(E, e) iters += 1 E -= ratio converged = np.abs(ratio) <= tol return E, iters, converged def test(): ''' Test the functionality of solve_kepler_chi and solve_kepler_laguerre using Problem 3.20 from Orbital Mechanics for Engineering Students, 4 ed, Curtis. ''' # given starting information Earth = BODIES['Earth'] # `CelestialBody` (--) Earth and all the Earth things r_0 = np.array([20000, -105000, -19000]) # (km) initial position vector v_0 = np.array([0.9, -3.4, -1.5]) # (km/s) initial velocity vector dt = 2*60*60 # (s) time of interest after initial time # given correct answer from textbook correct_r_1 = np.array([26338, -128750, -29656]) # (km) final position vector correct_v_1 = np.array([0.86280, -3.2116, -1.4613]) # (km/s) final velocity vector # solve using above methods r_n, v_n = solve_kepler_chi(r_0, v_0, dt, Earth, method='newton') r_l, v_l = solve_kepler_chi(r_0, v_0, dt, Earth, method='laguerre') # check correctness # tolerance based on significant figures of given answers newton_valid = np.allclose(r_n, correct_r_1, atol=1) and np.allclose(v_n, correct_v_1, atol=1e-4) laguerre_valid = np.allclose(r_l, correct_r_1, atol=1) and np.allclose(v_l, correct_v_1, atol=1e-4) return all([newton_valid, laguerre_valid]) if __name__ == '__main__': print(test())

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null

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null

0

1

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0acd26a6aeb9fbb21484a68cd667f26b74d856f7

952

py

Python

nicos_demo/vpgaa/setups/pgai.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

12

2019-11-06T15:40:36.000Z

2022-01-01T16:23:00.000Z

nicos_demo/vpgaa/setups/pgai.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

91

2020-08-18T09:20:26.000Z

2022-02-01T11:07:14.000Z

nicos_demo/vpgaa/setups/pgai.py

jkrueger1/nicos

5f4ce66c312dedd78995f9d91e8a6e3c891b262b

[ "CC-BY-3.0", "Apache-2.0", "CC-BY-4.0" ]

6

2020-01-11T10:52:30.000Z

2022-02-25T12:35:23.000Z

description = 'PGAA setup with XYZOmega sample table' group = 'basic' sysconfig = dict( datasinks = ['mcasink', 'chnsink', 'csvsink', 'livesink'] ) includes = [ 'system', 'reactor', 'nl4b', 'pressure', 'sampletable', 'pilz', 'detector', 'collimation', ] devices = dict( mcasink = device('nicos_mlz.pgaa.devices.MCASink', settypes = {'point'}, detectors = ['_60p', 'LEGe'], ), chnsink = device('nicos_mlz.pgaa.devices.CHNSink', settypes = {'point'}, detectors = ['_60p', 'LEGe'], ), csvsink = device('nicos_mlz.pgaa.devices.CSVDataSink', settypes = {'point'}, ), ) startupcode = """ SetDetectors('_60p', 'LEGe') SetEnvironment(chamber_pressure) printinfo("============================================================") printinfo("Welcome to the NICOS PGAI demo setup.") printinfo("============================================================") """

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0.196429

952

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74

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false

0

0.085714

0

null

0

null

0

1

0

0ace54f568ea92472966bb73d6fa4f6d624bebbf

6,859

py

Python

official/nlp/transformer/utils/tokenizer_test.py

hjkim-haga/TF-OD-API

22ac477ff4dfb93fe7a32c94b5f0b1e74330902b

[ "Apache-2.0" ]

1

2021-05-22T12:50:50.000Z

official/nlp/transformer/utils/tokenizer_test.py

DemonDamon/mask-detection-based-on-tf2odapi

192ae544169c1230c21141c033800aa1bd94e9b6

[ "MIT" ]

null

official/nlp/transformer/utils/tokenizer_test.py

DemonDamon/mask-detection-based-on-tf2odapi

192ae544169c1230c21141c033800aa1bd94e9b6

[ "MIT" ]

null

# Copyright 2021 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Test Subtokenizer and string helper methods.""" import collections import tempfile import tensorflow as tf from official.nlp.transformer.utils import tokenizer class SubtokenizerTest(tf.test.TestCase): def _init_subtokenizer(self, vocab_list): temp_file = tempfile.NamedTemporaryFile(delete=False) with tf.io.gfile.GFile(temp_file.name, "w") as w: for subtoken in vocab_list: w.write("'%s'" % subtoken) w.write("\n") return tokenizer.Subtokenizer(temp_file.name, reserved_tokens=[]) def test_encode(self): vocab_list = ["123_", "test", "ing_"] subtokenizer = self._init_subtokenizer(vocab_list) s = "testing 123" encoded_list = subtokenizer.encode(s) self.assertEqual([1, 2, 0], encoded_list) def test_decode(self): vocab_list = ["123_", "test", "ing_"] subtokenizer = self._init_subtokenizer(vocab_list) encoded_list = [1, 2, 0] # testing 123 decoded_str = subtokenizer.decode(encoded_list) self.assertEqual("testing 123", decoded_str) def test_subtoken_ids_to_tokens(self): vocab_list = ["123_", "test", "ing_"] subtokenizer = self._init_subtokenizer(vocab_list) encoded_list = [1, 2, 0] # testing 123 token_list = subtokenizer._subtoken_ids_to_tokens(encoded_list) self.assertEqual([u"testing", u"123"], token_list) class StringHelperTest(tf.test.TestCase): def test_split_string_to_tokens(self): text = "test? testing 123." tokens = tokenizer._split_string_to_tokens(text, tokenizer._ALPHANUMERIC_CHAR_SET) self.assertEqual(["test", "? ", "testing", "123", "."], tokens) def test_join_tokens_to_string(self): tokens = ["test", "? ", "testing", "123", "."] s = tokenizer._join_tokens_to_string(tokens, tokenizer._ALPHANUMERIC_CHAR_SET) self.assertEqual("test? testing 123.", s) def test_escape_token(self): token = u"abc_\\4" alphabet = set("abc_\\u;") escaped_token = tokenizer._escape_token(token, alphabet) self.assertEqual("abc\\u\\\\\\52;_", escaped_token) def test_unescape_token(self): escaped_token = u"Underline: \\u, Backslash: \\\\, Unicode: \\52;" unescaped_token = tokenizer._unescape_token(escaped_token) self.assertEqual("Underline: _, Backslash: \\, Unicode: 4", unescaped_token) def test_list_to_index_dict(self): lst = ["test", "strings"] d = tokenizer._list_to_index_dict(lst) self.assertDictEqual({"test": 0, "strings": 1}, d) def test_split_token_to_subtokens(self): token = "abc" subtoken_dict = {"a": 0, "b": 1, "c": 2, "ab": 3} max_subtoken_length = 2 subtokens = tokenizer._split_token_to_subtokens(token, subtoken_dict, max_subtoken_length) self.assertEqual(["ab", "c"], subtokens) def test_generate_alphabet_dict(self): s = ["testing", "123"] reserved_tokens = ["???"] alphabet = tokenizer._generate_alphabet_dict(s, reserved_tokens) self.assertIn("?", alphabet) self.assertIn("t", alphabet) self.assertIn("e", alphabet) self.assertIn("s", alphabet) self.assertIn("i", alphabet) self.assertIn("n", alphabet) self.assertIn("g", alphabet) self.assertIn("1", alphabet) self.assertIn("2", alphabet) self.assertIn("3", alphabet) def test_count_and_gen_subtokens(self): token_counts = {"abc": 5} alphabet = set("abc_") subtoken_dict = {"a": 0, "b": 1, "c": 2, "_": 3} max_subtoken_length = 2 subtoken_counts = tokenizer._count_and_gen_subtokens( token_counts, alphabet, subtoken_dict, max_subtoken_length) self.assertIsInstance(subtoken_counts, collections.defaultdict) self.assertDictEqual( { "a": 5, "b": 5, "c": 5, "_": 5, "ab": 5, "bc": 5, "c_": 5, "abc": 5, "bc_": 5, "abc_": 5 }, subtoken_counts) def test_filter_and_bucket_subtokens(self): subtoken_counts = collections.defaultdict(int, { "a": 2, "b": 4, "c": 1, "ab": 6, "ac": 3, "abbc": 5 }) min_count = 3 subtoken_buckets = tokenizer._filter_and_bucket_subtokens( subtoken_counts, min_count) self.assertEqual(len(subtoken_buckets[0]), 0) self.assertEqual(set("b"), subtoken_buckets[1]) self.assertEqual(set(["ab", "ac"]), subtoken_buckets[2]) self.assertEqual(len(subtoken_buckets[3]), 0) self.assertEqual(set(["abbc"]), subtoken_buckets[4]) def test_gen_new_subtoken_list(self): subtoken_counts = collections.defaultdict(int, { "translate": 10, "t": 40, "tr": 16, "tra": 12 }) min_count = 5 alphabet = set("translate") reserved_tokens = ["reserved", "tokens"] subtoken_list, max_token_length = tokenizer._gen_new_subtoken_list( subtoken_counts, min_count, alphabet, reserved_tokens) # Check that "tra" isn"t in the list (its count should be decremented to 2, # so it should not be added to the canddiate list). self.assertNotIn("tra", subtoken_list) self.assertIn("tr", subtoken_list) self.assertIn("t", subtoken_list) self.assertEqual(len("translate"), max_token_length) def test_generate_subtokens(self): token_counts = {"ab": 1, "bc": 3, "abc": 5} alphabet = set("abc_") min_count = 100 num_iterations = 1 reserved_tokens = ["reserved", "tokens"] vocab_list = tokenizer._generate_subtokens(token_counts, alphabet, min_count, num_iterations, reserved_tokens) # Check that reserved tokens are at the front of the list self.assertEqual(vocab_list[:2], reserved_tokens) # Check that each character in alphabet is in the vocab list for c in alphabet: self.assertIn(c, vocab_list) if __name__ == "__main__": tf.test.main()

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0acf3366802d8714bb15485c54ab7f3de9aac778

2,776

py

Python

Z - Tool Box/LaZagne/Windows/lazagne/softwares/windows/ppypykatz.py

dfirpaul/Active-Directory-Exploitation-Cheat-Sheet-1

1dcf54522e9d20711ff1114550dc2893ed3e9ed0

[ "MIT" ]

1,290

2020-05-28T21:24:43.000Z

2022-03-31T16:38:43.000Z

Z - Tool Box/LaZagne/Windows/lazagne/softwares/windows/ppypykatz.py

dfirpaul/Active-Directory-Exploitation-Cheat-Sheet-1

1dcf54522e9d20711ff1114550dc2893ed3e9ed0

[ "MIT" ]

1

2020-07-03T21:14:52.000Z

Z - Tool Box/LaZagne/Windows/lazagne/softwares/windows/ppypykatz.py

dfirpaul/Active-Directory-Exploitation-Cheat-Sheet-1

1dcf54522e9d20711ff1114550dc2893ed3e9ed0

[ "MIT" ]

280

2020-05-29T17:28:38.000Z

2022-03-31T13:54:15.000Z

# -*- coding: utf-8 -*- # Thanks to @skelsec for his awesome tool Pypykatz # Checks his project here: https://github.com/skelsec/pypykatz import codecs import traceback from lazagne.config.module_info import ModuleInfo from lazagne.config.constant import constant from pypykatz.pypykatz import pypykatz class Pypykatz(ModuleInfo): """ Pypykatz dumps all secrets from the lsass.exe memory It does not work if: - LSASS is running as a protected process - A security product blocks this access """ def __init__(self): ModuleInfo.__init__(self, 'pypykatz', 'windows', system_module=True) def run(self): mimi = None try: mimi = pypykatz.go_live() except Exception: self.debug(traceback.format_exc()) if mimi: results = {} logon_sessions = mimi.to_dict().get('logon_sessions', []) for logon_session in logon_sessions: # Right now kerberos_creds, dpapi_creds results are not used user = logon_sessions[logon_session] # Get cleartext password for i in ['credman_creds', 'ssp_creds', 'livessp_creds', 'tspkg_creds', 'wdigest_creds']: for data in user.get(i, []): if all((data['username'], data['password'])): login = data['username'] if login not in results: results[login] = {} results[login]['Type'] = i results[login]['Domain'] = data.get('domainname', 'N/A') results[login]['Password'] = data['password'] # msv_creds to get sha1 user hash for data in user.get('msv_creds', []): if data['username']: login = data['username'] else: login = user['username'] if login not in results: results[login] = {} if data['SHAHash']: results[login]['Shahash'] = codecs.encode(data['SHAHash'], 'hex') if data['LMHash']: results[login]['Lmhash'] = codecs.encode(data['LMHash'], 'hex') if data['NThash']: results[login]['Nthash'] = codecs.encode(data['NThash'], 'hex') constant.pypykatz_result = results pwd_found = [] for user in results: results[user]['Login'] = user pwd_found.append(results[user]) return pwd_found

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py

Python

test/test_discogs.py

mglukhovsky/beets

889e30c056a609cf71c8c8200259520230545222

[ "MIT" ]

null

test/test_discogs.py

mglukhovsky/beets

889e30c056a609cf71c8c8200259520230545222

[ "MIT" ]

null

test/test_discogs.py

mglukhovsky/beets

889e30c056a609cf71c8c8200259520230545222

[ "MIT" ]

null

# -*- coding: utf-8 -*- # This file is part of beets. # Copyright 2016, Adrian Sampson. # # Permission is hereby granted, free of charge, to any person obtaining # a copy of this software and associated documentation files (the # "Software"), to deal in the Software without restriction, including # without limitation the rights to use, copy, modify, merge, publish, # distribute, sublicense, and/or sell copies of the Software, and to # permit persons to whom the Software is furnished to do so, subject to # the following conditions: # # The above copyright notice and this permission notice shall be # included in all copies or substantial portions of the Software. """Tests for discogs plugin. """ from __future__ import division, absolute_import, print_function import unittest from test import _common from test._common import Bag from test.helper import capture_log from beetsplug.discogs import DiscogsPlugin class DGAlbumInfoTest(_common.TestCase): def _make_release(self, tracks=None): """Returns a Bag that mimics a discogs_client.Release. The list of elements on the returned Bag is incomplete, including just those required for the tests on this class.""" data = { 'id': 'ALBUM ID', 'uri': 'ALBUM URI', 'title': 'ALBUM TITLE', 'year': '3001', 'artists': [{ 'name': 'ARTIST NAME', 'id': 'ARTIST ID', 'join': ',' }], 'formats': [{ 'descriptions': ['FORMAT DESC 1', 'FORMAT DESC 2'], 'name': 'FORMAT', 'qty': 1 }], 'styles': [ 'STYLE1', 'STYLE2' ], 'labels': [{ 'name': 'LABEL NAME', 'catno': 'CATALOG NUMBER', }], 'tracklist': [] } if tracks: for recording in tracks: data['tracklist'].append(recording) return Bag(data=data, # Make some fields available as properties, as they are # accessed by DiscogsPlugin methods. title=data['title'], artists=[Bag(data=d) for d in data['artists']]) def _make_track(self, title, position='', duration='', type_=None): track = { 'title': title, 'position': position, 'duration': duration } if type_ is not None: # Test samples on discogs_client do not have a 'type_' field, but # the API seems to return it. Values: 'track' for regular tracks, # 'heading' for descriptive texts (ie. not real tracks - 12.13.2). track['type_'] = type_ return track def _make_release_from_positions(self, positions): """Return a Bag that mimics a discogs_client.Release with a tracklist where tracks have the specified `positions`.""" tracks = [self._make_track('TITLE%s' % i, position) for (i, position) in enumerate(positions, start=1)] return self._make_release(tracks) def test_parse_media_for_tracks(self): tracks = [self._make_track('TITLE ONE', '1', '01:01'), self._make_track('TITLE TWO', '2', '02:02')] release = self._make_release(tracks=tracks) d = DiscogsPlugin().get_album_info(release) t = d.tracks self.assertEqual(d.media, 'FORMAT') self.assertEqual(t[0].media, d.media) self.assertEqual(t[1].media, d.media) def test_parse_medium_numbers_single_medium(self): release = self._make_release_from_positions(['1', '2']) d = DiscogsPlugin().get_album_info(release) t = d.tracks self.assertEqual(d.mediums, 1) self.assertEqual(t[0].medium, 1) self.assertEqual(t[0].medium_total, 2) self.assertEqual(t[1].medium, 1) self.assertEqual(t[0].medium_total, 2) def test_parse_medium_numbers_two_mediums(self): release = self._make_release_from_positions(['1-1', '2-1']) d = DiscogsPlugin().get_album_info(release) t = d.tracks self.assertEqual(d.mediums, 2) self.assertEqual(t[0].medium, 1) self.assertEqual(t[0].medium_total, 1) self.assertEqual(t[1].medium, 2) self.assertEqual(t[1].medium_total, 1) def test_parse_medium_numbers_two_mediums_two_sided(self): release = self._make_release_from_positions(['A1', 'B1', 'C1']) d = DiscogsPlugin().get_album_info(release) t = d.tracks self.assertEqual(d.mediums, 2) self.assertEqual(t[0].medium, 1) self.assertEqual(t[0].medium_total, 2) self.assertEqual(t[0].medium_index, 1) self.assertEqual(t[1].medium, 1) self.assertEqual(t[1].medium_total, 2) self.assertEqual(t[1].medium_index, 2) self.assertEqual(t[2].medium, 2) self.assertEqual(t[2].medium_total, 1) self.assertEqual(t[2].medium_index, 1) def test_parse_track_indices(self): release = self._make_release_from_positions(['1', '2']) d = DiscogsPlugin().get_album_info(release) t = d.tracks self.assertEqual(t[0].medium_index, 1) self.assertEqual(t[0].index, 1) self.assertEqual(t[0].medium_total, 2) self.assertEqual(t[1].medium_index, 2) self.assertEqual(t[1].index, 2) self.assertEqual(t[1].medium_total, 2) def test_parse_track_indices_several_media(self): release = self._make_release_from_positions(['1-1', '1-2', '2-1', '3-1']) d = DiscogsPlugin().get_album_info(release) t = d.tracks self.assertEqual(d.mediums, 3) self.assertEqual(t[0].medium_index, 1) self.assertEqual(t[0].index, 1) self.assertEqual(t[0].medium_total, 2) self.assertEqual(t[1].medium_index, 2) self.assertEqual(t[1].index, 2) self.assertEqual(t[1].medium_total, 2) self.assertEqual(t[2].medium_index, 1) self.assertEqual(t[2].index, 3) self.assertEqual(t[2].medium_total, 1) self.assertEqual(t[3].medium_index, 1) self.assertEqual(t[3].index, 4) self.assertEqual(t[3].medium_total, 1) def test_parse_position(self): """Test the conversion of discogs `position` to medium, medium_index and subtrack_index.""" # List of tuples (discogs_position, (medium, medium_index, subindex) positions = [('1', (None, '1', None)), ('A12', ('A', '12', None)), ('12-34', ('12-', '34', None)), ('CD1-1', ('CD1-', '1', None)), ('1.12', (None, '1', '12')), ('12.a', (None, '12', 'A')), ('12.34', (None, '12', '34')), ('1ab', (None, '1', 'AB')), # Non-standard ('IV', ('IV', None, None)), ] d = DiscogsPlugin() for position, expected in positions: self.assertEqual(d.get_track_index(position), expected) def test_parse_tracklist_without_sides(self): """Test standard Discogs position 12.2.9#1: "without sides".""" release = self._make_release_from_positions(['1', '2', '3']) d = DiscogsPlugin().get_album_info(release) self.assertEqual(d.mediums, 1) self.assertEqual(len(d.tracks), 3) def test_parse_tracklist_with_sides(self): """Test standard Discogs position 12.2.9#2: "with sides".""" release = self._make_release_from_positions(['A1', 'A2', 'B1', 'B2']) d = DiscogsPlugin().get_album_info(release) self.assertEqual(d.mediums, 1) # 2 sides = 1 LP self.assertEqual(len(d.tracks), 4) def test_parse_tracklist_multiple_lp(self): """Test standard Discogs position 12.2.9#3: "multiple LP".""" release = self._make_release_from_positions(['A1', 'A2', 'B1', 'C1']) d = DiscogsPlugin().get_album_info(release) self.assertEqual(d.mediums, 2) # 3 sides = 1 LP + 1 LP self.assertEqual(len(d.tracks), 4) def test_parse_tracklist_multiple_cd(self): """Test standard Discogs position 12.2.9#4: "multiple CDs".""" release = self._make_release_from_positions(['1-1', '1-2', '2-1', '3-1']) d = DiscogsPlugin().get_album_info(release) self.assertEqual(d.mediums, 3) self.assertEqual(len(d.tracks), 4) def test_parse_tracklist_non_standard(self): """Test non standard Discogs position.""" release = self._make_release_from_positions(['I', 'II', 'III', 'IV']) d = DiscogsPlugin().get_album_info(release) self.assertEqual(d.mediums, 1) self.assertEqual(len(d.tracks), 4) def test_parse_tracklist_subtracks_dot(self): """Test standard Discogs position 12.2.9#5: "sub tracks, dots".""" release = self._make_release_from_positions(['1', '2.1', '2.2', '3']) d = DiscogsPlugin().get_album_info(release) self.assertEqual(d.mediums, 1) self.assertEqual(len(d.tracks), 3) release = self._make_release_from_positions(['A1', 'A2.1', 'A2.2', 'A3']) d = DiscogsPlugin().get_album_info(release) self.assertEqual(d.mediums, 1) self.assertEqual(len(d.tracks), 3) def test_parse_tracklist_subtracks_letter(self): """Test standard Discogs position 12.2.9#5: "sub tracks, letter".""" release = self._make_release_from_positions(['A1', 'A2a', 'A2b', 'A3']) d = DiscogsPlugin().get_album_info(release) self.assertEqual(d.mediums, 1) self.assertEqual(len(d.tracks), 3) release = self._make_release_from_positions(['A1', 'A2.a', 'A2.b', 'A3']) d = DiscogsPlugin().get_album_info(release) self.assertEqual(d.mediums, 1) self.assertEqual(len(d.tracks), 3) def test_parse_tracklist_subtracks_extra_material(self): """Test standard Discogs position 12.2.9#6: "extra material".""" release = self._make_release_from_positions(['1', '2', 'Video 1']) d = DiscogsPlugin().get_album_info(release) self.assertEqual(d.mediums, 2) self.assertEqual(len(d.tracks), 3) def test_parse_tracklist_subtracks_indices(self): """Test parsing of subtracks that include index tracks.""" release = self._make_release_from_positions(['', '', '1.1', '1.2']) # Track 1: Index track with medium title release.data['tracklist'][0]['title'] = 'MEDIUM TITLE' # Track 2: Index track with track group title release.data['tracklist'][1]['title'] = 'TRACK GROUP TITLE' d = DiscogsPlugin().get_album_info(release) self.assertEqual(d.mediums, 1) self.assertEqual(d.tracks[0].disctitle, 'MEDIUM TITLE') self.assertEqual(len(d.tracks), 1) self.assertEqual(d.tracks[0].title, 'TRACK GROUP TITLE') def test_parse_tracklist_subtracks_nested_logical(self): """Test parsing of subtracks defined inside a index track that are logical subtracks (ie. should be grouped together into a single track). """ release = self._make_release_from_positions(['1', '', '3']) # Track 2: Index track with track group title, and sub_tracks release.data['tracklist'][1]['title'] = 'TRACK GROUP TITLE' release.data['tracklist'][1]['sub_tracks'] = [ self._make_track('TITLE ONE', '2.1', '01:01'), self._make_track('TITLE TWO', '2.2', '02:02') ] d = DiscogsPlugin().get_album_info(release) self.assertEqual(d.mediums, 1) self.assertEqual(len(d.tracks), 3) self.assertEqual(d.tracks[1].title, 'TRACK GROUP TITLE') def test_parse_tracklist_subtracks_nested_physical(self): """Test parsing of subtracks defined inside a index track that are physical subtracks (ie. should not be grouped together). """ release = self._make_release_from_positions(['1', '', '4']) # Track 2: Index track with track group title, and sub_tracks release.data['tracklist'][1]['title'] = 'TRACK GROUP TITLE' release.data['tracklist'][1]['sub_tracks'] = [ self._make_track('TITLE ONE', '2', '01:01'), self._make_track('TITLE TWO', '3', '02:02') ] d = DiscogsPlugin().get_album_info(release) self.assertEqual(d.mediums, 1) self.assertEqual(len(d.tracks), 4) self.assertEqual(d.tracks[1].title, 'TITLE ONE') self.assertEqual(d.tracks[2].title, 'TITLE TWO') def test_parse_tracklist_disctitles(self): """Test parsing of index tracks that act as disc titles.""" release = self._make_release_from_positions(['', '1-1', '1-2', '', '2-1']) # Track 1: Index track with medium title (Cd1) release.data['tracklist'][0]['title'] = 'MEDIUM TITLE CD1' # Track 4: Index track with medium title (Cd2) release.data['tracklist'][3]['title'] = 'MEDIUM TITLE CD2' d = DiscogsPlugin().get_album_info(release) self.assertEqual(d.mediums, 2) self.assertEqual(d.tracks[0].disctitle, 'MEDIUM TITLE CD1') self.assertEqual(d.tracks[1].disctitle, 'MEDIUM TITLE CD1') self.assertEqual(d.tracks[2].disctitle, 'MEDIUM TITLE CD2') self.assertEqual(len(d.tracks), 3) def test_parse_minimal_release(self): """Test parsing of a release with the minimal amount of information.""" data = {'id': 123, 'tracklist': [self._make_track('A', '1', '01:01')], 'artists': [{'name': 'ARTIST NAME', 'id': 321, 'join': ''}], 'title': 'TITLE'} release = Bag(data=data, title=data['title'], artists=[Bag(data=d) for d in data['artists']]) d = DiscogsPlugin().get_album_info(release) self.assertEqual(d.artist, 'ARTIST NAME') self.assertEqual(d.album, 'TITLE') self.assertEqual(len(d.tracks), 1) def test_parse_release_without_required_fields(self): """Test parsing of a release that does not have the required fields.""" release = Bag(data={}, refresh=lambda *args: None) with capture_log() as logs: d = DiscogsPlugin().get_album_info(release) self.assertEqual(d, None) self.assertIn('Release does not contain the required fields', logs[0]) def suite(): return unittest.TestLoader().loadTestsFromName(__name__) if __name__ == '__main__': unittest.main(defaultTest='suite')

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py

Python

darknet2ncnn.py

nihui/gen-ncnn-models

18523f1920d9afc44ce3058087c07e09f28aa151

[ "BSD-2-Clause" ]

4

2019-12-24T15:16:18.000Z

2021-05-14T08:12:17.000Z

darknet2ncnn.py

nihui/gen-ncnn-models

18523f1920d9afc44ce3058087c07e09f28aa151

[ "BSD-2-Clause" ]

null

darknet2ncnn.py

nihui/gen-ncnn-models

18523f1920d9afc44ce3058087c07e09f28aa151

[ "BSD-2-Clause" ]

null

#! /usr/bin/env python # coding: utf-8 import configparser import numpy as np import re,sys,os from graph import MyGraph from collections import OrderedDict def unique_config_sections(config_file): """Convert all config sections to have unique names. Adds unique suffixes to config sections for compability with configparser. """ from collections import defaultdict import io section_counters = defaultdict(int) output_stream = io.StringIO() with open(config_file) as fin: for line in fin: if line.startswith('['): section = line.strip().strip('[]') _section = section + '_' + str(section_counters[section]) section_counters[section] += 1 line = line.replace(section, _section) output_stream.write(line) output_stream.seek(0) return output_stream def getFilters(mydict, name): #print('find filters for ', name) if hasattr(mydict[name], 'filters'): return mydict[name].filters else: assert len(mydict[name].input) >= 1 return getFilters(mydict, mydict[name].input[0]) def readfile(f, len, msg): print(" %s read %d bytes" % (msg, len)) return f.read(len) def buildGraph(config_path, weights_path): unique_config_file = unique_config_sections(config_path) cfg_parser = configparser.ConfigParser() cfg_parser.read_file(unique_config_file) weights_file = open(weights_path, 'rb') # read out major, minor, revision, net.seen readfile(weights_file, (4*4), 'head') mydict = OrderedDict() # record the output of the original layer mylist = [] count = 4 import queue for _section in cfg_parser.sections(): sec_q = queue.Queue(0) sec_q.put(cfg_parser[_section]) while not sec_q.empty(): sec = sec_q.get() section = sec.name print('Parsing section {}'.format(section)) # this section will can be a subsection if section.startswith('activation') or section.endswith('activation'): activation = sec.get('activation', fallback = 'logistic') if activation == 'linear': pass elif activation == 'linear' or activation == 'leaky' or activation == 'relu': node = MyGraph.MyNode() node.name = section node.op = 'Leaky' if activation == 'linear': node.slope = 1 elif activation == 'leaky': node.slope = 0.1 elif activation == 'relu': node.slope = 0 node.input = [prev_output] node.input_norm = node.input #node.attr = [] mydict[node.name] = node prev_output = node.name # prev_layer_filters no change else: raise ValueError( 'Unknown activation function `{}` in section {}'.format( activation, section)) if section.startswith('activation'): mylist.append(section) elif re.match(r'^(convolutional|depthwise|groupwise)_\d+$', section): if section.startswith('convolutional'): conv = 'conv' filters = sec.getint('filters', fallback = 1) groups = 1 op = 'Conv2D' elif section.startswith('depthwise'): conv = 'dconv' filters = prev_layer_filters multiplier = sec.getint('multiplier', fallback = 1) assert multiplier == 1 groups = filters op = 'DepthwiseConv2dNative' elif section.startswith('groupwise'): conv = 'gconv' filters = sec.getint('filters', fallback=1) groups = sec.getint('groups', fallback = 1) op = 'DepthwiseConv2dNative' size = sec.getint('size', fallback = 1) stride = sec.getint('stride', fallback = 1) pad = sec.getint('pad', fallback = 0) padding = sec.getint('padding', fallback = 0) activation = sec.get('activation', fallback = 'logistic') batch_normalize = sec.getint('batch_normalize', 0) # padding='same' is equivalent to Darknet pad=1 # padding = 'same' if pad == 1 else 'valid' if pad: padding = size//2 # Setting weights. # Darknet serializes convolutional weights as: # [bias/beta, [gamma, mean, variance], conv_weights] #prev_layer_shape = prev_layer.shape # TODO: This assumes channel last dim_ordering. if conv == 'conv': weights_shape = (size, size, prev_layer_filters, filters) idx_tf2darknet = [0, 1, 2, 3] elif conv == 'dconv': weights_shape = (size, size, filters) idx_tf2darknet = [0, 1, 2] elif conv == 'gconv': weights_shape = (size, size, prev_layer_filters//groups, filters//groups, groups) idx_tf2darknet = [0, 1, 2, 3, 4] idxmap = {x: i for i, x in enumerate(idx_tf2darknet)} idx_dartnet2tf = [idxmap[i] for i in range(len(idxmap))] weights_size = np.product(weights_shape) print(' ' + conv, 'bn' if batch_normalize else ' ', activation, weights_shape) conv_bias = np.ndarray( shape=(filters, ), dtype=np.float32, buffer=readfile(weights_file, (filters * 4), section+'-bias')) count += filters if batch_normalize: bn_weights = np.ndarray( shape=(3, filters), dtype=np.float32, buffer=readfile(weights_file, (filters * 12), section+'-batchnorm')) count += 3 * filters # TODO: Keras BatchNormalization mistakenly refers to var # as std. bn_weight_list = [ bn_weights[0], # scale gamma conv_bias, # shift beta bn_weights[1], # running mean bn_weights[2] # running var ] conv_weights = np.ndarray( shape=[weights_shape[i] for i in idx_tf2darknet], dtype=np.float32, buffer=readfile(weights_file, (weights_size * 4), section+'-weights')) count += weights_size # DarkNet conv_weights are serialized Caffe-style: # (out_dim, in_dim, height, width) # We would like to set these to Tensorflow order: # (height, width, in_dim, out_dim) # TODO: Add check for Theano dim ordering. #print("the darknet shape is ", conv_weights.shape) conv_weights = np.transpose(conv_weights, idx_dartnet2tf) #print("the tf shape is ", conv_weights.shape) conv_weights = [conv_weights] if batch_normalize else [ conv_weights, conv_bias ] # Create nodes #conv_layer = np.zeros([1, 1, filters], dtype = np.float32) node = MyGraph.MyNode() node.name = section node.op = op node.input = [prev_output] node.input_norm = node.input node.kernel = conv_weights[0] node.padding = padding node.strides = [1,stride,stride,1] node.groups = groups node.filters = filters mydict[node.name] = node prev_output = node.name prev_layer_filters = filters if batch_normalize: node = MyGraph.MyNode() node.name = section + '_batch_normalize' node.op = 'FusedBatchNorm' node.input = [prev_output] node.input_norm = node.input #node.attr = [] node.gamma = bn_weights[0] node.beta = conv_bias node.mean = bn_weights[1] node.variance = bn_weights[2] mydict[node.name] = node prev_output = node.name # prev_layer_filters no change else: node = MyGraph.MyNode() node.name = section + '_bias' node.op = 'BiasAdd' node.input = [prev_output] node.input_norm = node.input #node.attr = [] node.bias = conv_bias mydict[node.name] = node prev_output = node.name if activation == 'linear': mylist.append(prev_output) else: tmp_parser = configparser.ConfigParser() name = section + '_activation' tmp_parser.add_section(name) tmp_parser.set(name, 'activation', activation) sec_q.put(tmp_parser[name]) mylist.append(name) elif section.startswith('shuffle'): node = MyGraph.MyNode() node.name = section node.op = 'Shuffle' node.input = [prev_output] node.input_norm = node.input node.groups = int(cfg_parser[section]['groups']) mydict[node.name] = node prev_output = node.name mylist.append(section) elif re.match(r'^(pooling|maxpool|avgpool)_\d+$', section): node = MyGraph.MyNode() node.stride = sec.getint('stride', fallback = 1) node.size = sec.getint('size', node.stride) node.padding = sec.getint('padding', fallback = (node.size-1)//2) if section.startswith('pooling'): node.mode = str(cfg_parser[section]['mode']) node.global_pooling = 0 elif section.startswith('maxpool'): node.mode = 'max' node.global_pooling = 0 elif section.startswith('avgpool'): node.mode = 'avg' node.global_pooling = 1 node.name = section node.op = 'Pooling' node.input = [prev_output] node.input_norm = node.input mydict[node.name] = node prev_output = node.name #print('pooling ', vars(node)) mylist.append(section) elif section.startswith('route'): ids = [int(i) for i in cfg_parser[section]['layers'].split(',')] node = MyGraph.MyNode() node.name = section node.op = 'NCNNConcat' node.input = [mylist[i] for i in ids] #print('mylist is ', mylist, 'the ids is ', ids, 'node input is ', node.input) node.input_norm = node.input node.axis = 0 node.filters = sum([getFilters(mydict, mylist[i]) for i in ids]) mydict[node.name] = node prev_output = node.name mylist.append(section) prev_layer_filters = node.filters elif section.startswith('reorg'): node = MyGraph.MyNode() node.name = section node.op = 'DarknetReorg' node.input = [prev_output] node.stride = sec.getint('stride', fallback = 1) node.input_norm = node.input node.filters = getFilters(mydict, node.input[0]) * node.stride * node.stride mydict[node.name] = node prev_output = node.name mylist.append(section) prev_layer_filters = node.filters elif re.match(r'^(shortcut)_\d+$', section): activation = sec.get('activation', fallback = 'logistic') from_ = sec.getint('from') node = MyGraph.MyNode() node.name = section node.op = 'BinaryOp' node.op_type = 0 node.input = [prev_output, mylist[from_]] #print('mylist is ', mylist, 'the from_ is ', from_, 'node input is ', node.input) node.input_norm = node.input mydict[node.name] = node prev_output = node.name if activation == 'linear': mylist.append(prev_output) else: tmp_parser = configparser.ConfigParser() name = section + '_activation' tmp_parser.add_section(name) tmp_parser.set(name, 'activation', activation) sec_q.put(tmp_parser[name]) # NOTE: this section has relative reference mylist.append(name) elif section.startswith('connected'): activation = sec.get('activation', fallback='linear') filters = sec.getint('output', 2) bias_data = np.ndarray( shape=[filters], dtype=np.float32, buffer=readfile(weights_file, (filters * 4), section+'-bias')) fc_data = np.ndarray( shape=[prev_layer_filters, filters], dtype=np.float32, buffer=readfile(weights_file, (prev_layer_filters * filters * 4), section+'-weight')) node = MyGraph.MyNode() node.name = section node.op = 'MatMul' node.input = [prev_output] node.input_norm = node.input node.multiplier = fc_data mydict[node.name] = node prev_output = node.name prev_layer_filters = filters node = MyGraph.MyNode() node.name = section + '_bias' node.op = 'BiasAdd' node.input = [prev_output] node.input_norm = node.input # node.attr = [] node.bias = bias_data mydict[node.name] = node prev_output = node.name if activation == 'linear': mylist.append(prev_output) else: tmp_parser = configparser.ConfigParser() name = section + '_activation' tmp_parser.add_section(name) tmp_parser.set(name, 'activation', activation) sec_q.put(tmp_parser[name]) mylist.append(name) elif section.startswith('net'): node = MyGraph.MyNode() node.name = section node.op = 'DarknetNet' node.input = [] node.input_norm = [] node.width = int(cfg_parser['net_0']['width']) node.height = int(cfg_parser['net_0']['height']) node.channels = int(cfg_parser['net_0']['channels']) node.filters = node.channels # print(vars(node)) # node.attr = [] mydict[node.name] = node # start here prev_output = node.name prev_layer_filters = node.channels mylist.append(section) elif section.startswith('region'): node = MyGraph.MyNode() node.name = section node.op = 'DarknetRegion' node.input = [prev_output] node.input_norm = node.input node.classes = int(cfg_parser[section]['classes']) node.num = int(cfg_parser[section]['num']) node.softmax = int(cfg_parser[section]['softmax']) node.anchors = [float(i) for i in re.split(r',', cfg_parser[section]['anchors'])] #print(vars(node)) #node.attr = [] mydict[node.name] = node prev_output = node.name mylist.append(section) elif section.startswith('softmax'): node = MyGraph.MyNode() node.name = section node.op = 'Softmax' node.input = [prev_output] node.input_norm = node.input mydict[node.name] = node prev_output = node.name mylist.append(section) pass elif section.startswith('cost'): pass # Configs not currently handled during model definition. else: raise ValueError( 'Unsupported section header type: {}'.format(section)) print(' out filters ', prev_layer_filters) print('loaded {} bytes in weights file'.format(count*4)) mygraph = MyGraph(mydict) mygraph.type = 'darknet' return mygraph if __name__ == '__main__': config_path = sys.argv[1] weights_path = sys.argv[2] mygraph = buildGraph(config_path, weights_path) # 定义子图所需要的输出节点，输入节点，终止节点 outputNodes = ['region_0', 'softmax_0'] stopNodes = [] inputNodes = ['darknet_0'] mygraph.extractSubGraph(inputNodes, outputNodes, stopNodes) mygraph.generateDot('YoloV2.dot') # 生成子图对应的代码 mygraph.generateSource('YoloV2', os.path.split(config_path)[1]+'.ncnn', os.path.split(weights_path)[1] + '.ncnn')

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null

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0

0ad20a796d3e2e784e9676daf81a22cf86a1d3cb

8,474

py

Python

liuetal2019/utils.py

wasiahmad/GATE

1e48504a3641f00265a271a19eb6b6449fdc33bd

[ "MIT" ]

24

2020-12-07T10:22:40.000Z

2022-03-31T09:24:13.000Z

liuetal2019/utils.py

wasiahmad/GATE

1e48504a3641f00265a271a19eb6b6449fdc33bd

[ "MIT" ]

15

2021-03-22T04:52:57.000Z

2022-01-01T18:32:31.000Z

liuetal2019/utils.py

wasiahmad/GATE

1e48504a3641f00265a271a19eb6b6449fdc33bd

[ "MIT" ]

8

2021-03-04T05:09:42.000Z

2022-01-25T12:59:19.000Z

import io import logging import json import numpy import torch import numpy as np from tqdm import tqdm from clie.inputters import constant from clie.objects import Sentence from torch.utils.data import Dataset from torch.utils.data.sampler import Sampler logger = logging.getLogger(__name__) def load_word_embeddings(file): embeddings_index = {} fin = io.open(file, 'r', encoding='utf-8', newline='\n', errors='ignore') n, d = map(int, fin.readline().split()) for i, line in tqdm(enumerate(fin), total=n): tokens = line.rstrip().split(' ') v = numpy.array(tokens[1:], dtype=float) embeddings_index[tokens[0]] = v return embeddings_index # ------------------------------------------------------------------------------ # Data loading # ------------------------------------------------------------------------------ def load_data(filename, src_lang, tgt_lang, knn_file, knn_size, max_examples=-1): examples = [] wrong_subj_pos, wrong_obj_pos = 0, 0 with open(filename) as f: data = json.load(f) knn_dict = None if knn_file: with open(knn_file) as f: knn_dict = json.load(f) for idx, ex in enumerate(tqdm(data, total=len(data))): sentence = Sentence(ex['id']) sentence.language = src_lang sentence.words = ex['token'] sentence.pos = ex['stanford_pos'] sentence.ner = ex['stanford_ner'] sentence.deprel = ex['stanford_deprel'] sentence.head = [int(x) for x in ex['stanford_head']] sentence.subj_type = ex['subj_type'] sentence.obj_type = ex['obj_type'] sentence.relation = ex['relation'] if ex['subj_end'] - ex['subj_start'] < 0: # we swap the start and end index wrong_subj_pos += 1 sentence.subject = [ex['subj_end'], ex['subj_start']] else: sentence.subject = [ex['subj_start'], ex['subj_end']] if ex['obj_end'] - ex['obj_start'] < 0: # we swap the start and end index wrong_obj_pos += 1 sentence.object = [ex['obj_end'], ex['obj_start']] else: sentence.object = [ex['obj_start'], ex['obj_end']] # store KNN word info if knn_dict: sentence.tgt_lang = tgt_lang knn_words = [] for w in ex['token']: w = '!{}_{}'.format(src_lang, w) if w in knn_dict: assert len(knn_dict[w]) == knn_size knn_words.append(knn_dict[w]) else: knn_words.append([constant.UNK_WORD] * knn_size) sentence.knn_words = knn_words examples.append(sentence) if max_examples != -1 and len(examples) > max_examples: break if wrong_subj_pos > 0 or wrong_obj_pos > 0: logger.info('{} and {} wrong subject and object positions found!'.format( wrong_subj_pos, wrong_obj_pos)) return examples def vectorize(ex, model, iseval): """Torchify a single example.""" words = ['!{}_{}'.format(ex.language, w) for w in ex.words] words = [model.word_dict[w] for w in words] knn_word = None if ex.knn_words: knn_word = [[model.word_dict[w] for w in knn] for knn in ex.knn_words] knn_word = torch.LongTensor(knn_word) word = torch.LongTensor(words) pos = torch.LongTensor([model.pos_dict[p] for p in ex.pos]) ner = torch.LongTensor([model.ner_dict[n] for n in ex.ner]) deprel = torch.LongTensor([model.deprel_dict[d] for d in ex.deprel]) assert any([x == 0 for x in ex.head]) head = torch.LongTensor(ex.head) subj_position = torch.LongTensor(ex.subj_position) obj_position = torch.LongTensor(ex.obj_position) type = [0] * len(ex.words) ttype = model.type_dict[ex.subj_type] start, end = ex.subject type[start: end + 1] = [ttype] * (end - start + 1) atype = model.type_dict[ex.obj_type] start, end = ex.object type[start: end + 1] = [atype] * (end - start + 1) type = torch.LongTensor(type) return { 'id': ex.id, 'language': ex.language, 'word': word, 'pos': pos, 'ner': ner, 'deprel': deprel, 'type': type, 'head': head, 'subject': ex.subj_text, 'object': ex.obj_text, 'subject_pos': subj_position, 'object_pos': obj_position, 'relation': model.label_dict[ex.relation], 'knn_word': knn_word } def batchify(batch): """Gather a batch of individual examples into one batch.""" # batch is a list of vectorized examples batch_size = len(batch) ids = [ex['id'] for ex in batch] language = [ex['language'] for ex in batch] use_knn = batch[0]['knn_word'] is not None # NOTE. batch[0]['knn_word'] is a 2d list knn_size = len(batch[0]['knn_word'][0]) if use_knn else 0 # --------- Prepare Code tensors --------- max_len = max([ex['word'].size(0) for ex in batch]) # Batch Code Representations len_rep = torch.LongTensor(batch_size).fill_(constant.PAD) word_rep = torch.LongTensor(batch_size, max_len).fill_(constant.PAD) head_rep = torch.LongTensor(batch_size, max_len).fill_(constant.PAD) subject_pos_rep = torch.LongTensor(batch_size, max_len).fill_(constant.PAD) object_pos_rep = torch.LongTensor(batch_size, max_len).fill_(constant.PAD) pos_rep = torch.LongTensor(batch_size, max_len).fill_(constant.PAD) ner_rep = torch.LongTensor(batch_size, max_len).fill_(constant.PAD) deprel_rep = torch.LongTensor(batch_size, max_len).fill_(constant.PAD) type_rep = torch.LongTensor(batch_size, max_len).fill_(constant.PAD) labels = torch.LongTensor(batch_size) subject = [] object = [] knn_rep = None if use_knn: knn_rep = torch.LongTensor(batch_size, max_len, knn_size).fill_(constant.PAD) for i, ex in enumerate(batch): len_rep[i] = ex['word'].size(0) labels[i] = ex['relation'] word_rep[i, :len_rep[i]] = ex['word'] head_rep[i, :len_rep[i]] = ex['head'] subject_pos_rep[i, :len_rep[i]] = ex['subject_pos'] object_pos_rep[i, :len_rep[i]] = ex['object_pos'] pos_rep[i, :len_rep[i]] = ex['pos'] ner_rep[i, :len_rep[i]] = ex['ner'] deprel_rep[i, :len_rep[i]] = ex['deprel'] type_rep[i, :len_rep[i]] = ex['type'] subject.append(ex['subject']) object.append(ex['object']) if use_knn: knn_rep[i, :len_rep[i]] = ex['knn_word'] return { 'ids': ids, 'language': language, 'batch_size': batch_size, 'len_rep': len_rep, 'word_rep': word_rep, 'knn_rep': knn_rep, 'head_rep': head_rep, 'subject': subject, 'object': object, 'subject_pos_rep': subject_pos_rep, 'object_pos_rep': object_pos_rep, 'labels': labels, 'pos_rep': pos_rep, 'ner_rep': ner_rep, 'deprel_rep': deprel_rep, 'type_rep': type_rep } class ACE05Dataset(Dataset): def __init__(self, examples, model, evaluation=False): self.model = model self.examples = examples self.evaluation = evaluation def __len__(self): return len(self.examples) def __getitem__(self, index): return vectorize(self.examples[index], self.model, iseval=self.evaluation) def lengths(self): return [len(ex.words) for ex in self.examples] class SortedBatchSampler(Sampler): def __init__(self, lengths, batch_size, shuffle=True): self.lengths = lengths self.batch_size = batch_size self.shuffle = shuffle def __iter__(self): lengths = np.array( [(-l, np.random.random()) for l in self.lengths], dtype=[('l1', np.int_), ('rand', np.float_)] ) indices = np.argsort(lengths, order=('l1', 'rand')) batches = [indices[i:i + self.batch_size] for i in range(0, len(indices), self.batch_size)] if self.shuffle: np.random.shuffle(batches) return iter([i for batch in batches for i in batch]) def __len__(self): return len(self.lengths)

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8,474

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0.169231

0

null

0

null

0

1

0

0ad2503d07ac5b15fee30f7480f83b4ea51f1515

914

py

Python

build.py

dnanexus/IndexTools

0392b3be92ff50b401290b59e9ca6c7767fa5a96

[ "MIT" ]

15

2019-07-17T11:41:36.000Z

2021-03-02T09:36:34.000Z

build.py

dnanexus/IndexTools

0392b3be92ff50b401290b59e9ca6c7767fa5a96

[ "MIT" ]

22

2019-05-15T20:08:12.000Z

2019-10-11T13:33:42.000Z

build.py

dnanexus/IndexTools

0392b3be92ff50b401290b59e9ca6c7767fa5a96

[ "MIT" ]

3

2019-06-01T15:58:06.000Z

2022-01-21T21:10:01.000Z

from distutils.extension import Extension cmdclass = {} try: # with Cython from Cython.Build import build_ext cmdclass["build_ext"] = build_ext module_src = "cgranges/python/cgranges.pyx" except ImportError: # without Cython module_src = "cgranges/python/cgranges.c" def build(setup_kwargs): """ This function is mandatory in order to build the extensions. """ setup_kwargs.update( { "ext_modules": [ Extension( "cgranges", sources=[module_src, "cgranges/cgranges.c"], depends=[ "cgranges/cgranges.h", "cgranges/khash.h", "cgranges/python/cgranges.pyx" ], include_dirs=["cgranges"] ) ], "cmdclass": cmdclass } )

25.388889

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0.053215

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0.399344

914

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1

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false

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0

null

0

null

0

1

0

0ad2916f049d06f5df6ddbf5e08b57510f7c1b78

17,212

py

Python

gluoncv/data/kinetics400/classification.py

YvetteGuo/gluon-cv

123af8cf9f15a879c16a5c7d12f01ce1471d85b6

[ "Apache-2.0" ]

1

2019-04-02T02:08:04.000Z

gluoncv/data/kinetics400/classification.py

YvetteGuo/gluon-cv

123af8cf9f15a879c16a5c7d12f01ce1471d85b6

[ "Apache-2.0" ]

1

2019-06-06T08:39:12.000Z

gluoncv/data/kinetics400/classification.py

YvetteGuo/gluon-cv

123af8cf9f15a879c16a5c7d12f01ce1471d85b6

[ "Apache-2.0" ]

1

2019-08-26T09:26:42.000Z

# pylint: disable=line-too-long,too-many-lines,missing-docstring """Kinetics400 action classification dataset.""" import os import random import numpy as np from mxnet import nd from mxnet.gluon.data import dataset __all__ = ['Kinetics400'] class Kinetics400(dataset.Dataset): """Load the Kinetics400 action recognition dataset. Refer to :doc:`../build/examples_datasets/kinetics400` for the description of this dataset and how to prepare it. Parameters ---------- root : str, default '~/.mxnet/datasets/kinetics400' Path to the folder stored the dataset. setting : str, required Config file of the prepared dataset. train : bool, default True Whether to load the training or validation set. test_mode : bool, default False Whether to perform evaluation on the test set name_pattern : str, default None The naming pattern of the decoded video frames. For example, img_00012.jpg is_color : bool, default True Whether the loaded image is color or grayscale modality : str, default 'rgb' Input modalities, we support only rgb video frames for now. Will add support for rgb difference image and optical flow image later. num_segments : int, default 1 Number of segments to evenly divide the video into clips. A useful technique to obtain global video-level information. Limin Wang, etal, Temporal Segment Networks: Towards Good Practices for Deep Action Recognition, ECCV 2016 new_length : int, default 1 The length of input video clip. Default is a single image, but it can be multiple video frames. For example, new_length=16 means we will extract a video clip of consecutive 16 frames. new_width : int, default 340 Scale the width of loaded image to 'new_width' for later multiscale cropping and resizing. new_height : int, default 256 Scale the height of loaded image to 'new_height' for later multiscale cropping and resizing. target_width : int, default 224 Scale the width of transformed image to the same 'target_width' for batch forwarding. target_height : int, default 224 Scale the height of transformed image to the same 'target_height' for batch forwarding. transform : function, default None A function that takes data and label and transforms them. """ def __init__(self, setting=os.path.expanduser('~/.mxnet/datasets/kinetics400/kinetics400_train_list_rawframes.txt'), root=os.path.expanduser('~/.mxnet/datasets/kinetics400/rawframes_train'), train=True, test_mode=False, name_pattern=None, is_color=True, modality='rgb', num_segments=1, new_length=1, new_width=340, new_height=256, target_width=224, target_height=224, transform=None): super(Kinetics400, self).__init__() self.root = root self.setting = setting self.train = train self.test_mode = test_mode self.is_color = is_color self.modality = modality self.num_segments = num_segments self.new_height = new_height self.new_width = new_width self.target_height = target_height self.target_width = target_width self.new_length = new_length self.transform = transform self.classes, self.class_to_idx = self._find_classes(root) self.clips = self._make_dataset(root, setting) if len(self.clips) == 0: raise(RuntimeError("Found 0 video clips in subfolders of: " + root + "\n" "Check your data directory (opt.data-dir).")) if name_pattern: self.name_pattern = name_pattern else: if self.modality == "rgb": self.name_pattern = "img_%05d.jpg" elif self.modality == "flow": self.name_pattern = "flow_%s_%05d.jpg" def __getitem__(self, index): directory, duration, target = self.clips[index] average_duration = int(duration / self.num_segments) offsets = [] for seg_id in range(self.num_segments): if self.train and not self.test_mode: # training if average_duration >= self.new_length: offset = random.randint(0, average_duration - self.new_length) # No +1 because randint(a,b) return a random integer N such that a <= N <= b. offsets.append(offset + seg_id * average_duration) else: offsets.append(0) elif not self.train and not self.test_mode: # validation if average_duration >= self.new_length: offsets.append(int((average_duration - self.new_length + 1)/2 + seg_id * average_duration)) else: offsets.append(0) else: # test if average_duration >= self.new_length: offsets.append(int((average_duration - self.new_length + 1)/2 + seg_id * average_duration)) else: offsets.append(0) clip_input = self._TSN_RGB(directory, offsets, self.new_height, self.new_width, self.new_length, self.is_color, self.name_pattern) if self.transform is not None: clip_input = self.transform(clip_input) if self.num_segments > 1 and not self.test_mode: # For TSN training, reshape the input to B x 3 x H x W. Here, B = batch_size * num_segments clip_input = clip_input.reshape((-1, 3 * self.new_length, self.target_height, self.target_width)) return clip_input, target def __len__(self): return len(self.clips) def _find_classes(self, directory): classes = [d for d in os.listdir(directory) if os.path.isdir(os.path.join(directory, d))] classes.sort() class_to_idx = {classes[i]: i for i in range(len(classes))} return classes, class_to_idx def _make_dataset(self, directory, setting): if not os.path.exists(setting): raise(RuntimeError("Setting file %s doesn't exist. Check opt.train-list and opt.val-list. " % (setting))) clips = [] with open(setting) as split_f: data = split_f.readlines() for line in data: line_info = line.split() # line format: video_path, video_duration, video_label if len(line_info) < 3: print('Video input format is not correct, missing one or more element. %s' % line) continue clip_path = os.path.join(directory, line_info[0]) duration = int(line_info[1]) target = int(line_info[2]) item = (clip_path, duration, target) clips.append(item) return clips def _TSN_RGB(self, directory, offsets, new_height, new_width, new_length, is_color, name_pattern): from ...utils.filesystem import try_import_cv2 cv2 = try_import_cv2() if is_color: cv_read_flag = cv2.IMREAD_COLOR else: cv_read_flag = cv2.IMREAD_GRAYSCALE interpolation = cv2.INTER_LINEAR sampled_list = [] for _, offset in enumerate(offsets): for length_id in range(1, new_length+1): frame_name = name_pattern % (length_id + offset) frame_path = directory + "/" + frame_name cv_img_origin = cv2.imread(frame_path, cv_read_flag) if cv_img_origin is None: raise(RuntimeError("Could not load file %s. Check data path." % (frame_path))) if new_width > 0 and new_height > 0: cv_img = cv2.resize(cv_img_origin, (new_width, new_height), interpolation) else: cv_img = cv_img_origin cv_img = cv2.cvtColor(cv_img, cv2.COLOR_BGR2RGB) sampled_list.append(cv_img) # the shape of clip_input will be H x W x C, and C = num_segments * new_length * 3 clip_input = np.concatenate(sampled_list, axis=2) return nd.array(clip_input) class Kinetics400Attr(object): def __init__(self): self.num_class = 400 self.classes = ['abseiling', 'air_drumming', 'answering_questions', 'applauding', 'applying_cream', 'archery', 'arm_wrestling', 'arranging_flowers', 'assembling_computer', 'auctioning', 'baby_waking_up', 'baking_cookies', 'balloon_blowing', 'bandaging', 'barbequing', 'bartending', 'beatboxing', 'bee_keeping', 'belly_dancing', 'bench_pressing', 'bending_back', 'bending_metal', 'biking_through_snow', 'blasting_sand', 'blowing_glass', 'blowing_leaves', 'blowing_nose', 'blowing_out_candles', 'bobsledding', 'bookbinding', 'bouncing_on_trampoline', 'bowling', 'braiding_hair', 'breading_or_breadcrumbing', 'breakdancing', 'brush_painting', 'brushing_hair', 'brushing_teeth', 'building_cabinet', 'building_shed', 'bungee_jumping', 'busking', 'canoeing_or_kayaking', 'capoeira', 'carrying_baby', 'cartwheeling', 'carving_pumpkin', 'catching_fish', 'catching_or_throwing_baseball', 'catching_or_throwing_frisbee', 'catching_or_throwing_softball', 'celebrating', 'changing_oil', 'changing_wheel', 'checking_tires', 'cheerleading', 'chopping_wood', 'clapping', 'clay_pottery_making', 'clean_and_jerk', 'cleaning_floor', 'cleaning_gutters', 'cleaning_pool', 'cleaning_shoes', 'cleaning_toilet', 'cleaning_windows', 'climbing_a_rope', 'climbing_ladder', 'climbing_tree', 'contact_juggling', 'cooking_chicken', 'cooking_egg', 'cooking_on_campfire', 'cooking_sausages', 'counting_money', 'country_line_dancing', 'cracking_neck', 'crawling_baby', 'crossing_river', 'crying', 'curling_hair', 'cutting_nails', 'cutting_pineapple', 'cutting_watermelon', 'dancing_ballet', 'dancing_charleston', 'dancing_gangnam_style', 'dancing_macarena', 'deadlifting', 'decorating_the_christmas_tree', 'digging', 'dining', 'disc_golfing', 'diving_cliff', 'dodgeball', 'doing_aerobics', 'doing_laundry', 'doing_nails', 'drawing', 'dribbling_basketball', 'drinking', 'drinking_beer', 'drinking_shots', 'driving_car', 'driving_tractor', 'drop_kicking', 'drumming_fingers', 'dunking_basketball', 'dying_hair', 'eating_burger', 'eating_cake', 'eating_carrots', 'eating_chips', 'eating_doughnuts', 'eating_hotdog', 'eating_ice_cream', 'eating_spaghetti', 'eating_watermelon', 'egg_hunting', 'exercising_arm', 'exercising_with_an_exercise_ball', 'extinguishing_fire', 'faceplanting', 'feeding_birds', 'feeding_fish', 'feeding_goats', 'filling_eyebrows', 'finger_snapping', 'fixing_hair', 'flipping_pancake', 'flying_kite', 'folding_clothes', 'folding_napkins', 'folding_paper', 'front_raises', 'frying_vegetables', 'garbage_collecting', 'gargling', 'getting_a_haircut', 'getting_a_tattoo', 'giving_or_receiving_award', 'golf_chipping', 'golf_driving', 'golf_putting', 'grinding_meat', 'grooming_dog', 'grooming_horse', 'gymnastics_tumbling', 'hammer_throw', 'headbanging', 'headbutting', 'high_jump', 'high_kick', 'hitting_baseball', 'hockey_stop', 'holding_snake', 'hopscotch', 'hoverboarding', 'hugging', 'hula_hooping', 'hurdling', 'hurling_-sport-', 'ice_climbing', 'ice_fishing', 'ice_skating', 'ironing', 'javelin_throw', 'jetskiing', 'jogging', 'juggling_balls', 'juggling_fire', 'juggling_soccer_ball', 'jumping_into_pool', 'jumpstyle_dancing', 'kicking_field_goal', 'kicking_soccer_ball', 'kissing', 'kitesurfing', 'knitting', 'krumping', 'laughing', 'laying_bricks', 'long_jump', 'lunge', 'making_a_cake', 'making_a_sandwich', 'making_bed', 'making_jewelry', 'making_pizza', 'making_snowman', 'making_sushi', 'making_tea', 'marching', 'massaging_back', 'massaging_feet', 'massaging_legs', "massaging_person's_head", 'milking_cow', 'mopping_floor', 'motorcycling', 'moving_furniture', 'mowing_lawn', 'news_anchoring', 'opening_bottle', 'opening_present', 'paragliding', 'parasailing', 'parkour', 'passing_American_football_-in_game-', 'passing_American_football_-not_in_game-', 'peeling_apples', 'peeling_potatoes', 'petting_animal_-not_cat-', 'petting_cat', 'picking_fruit', 'planting_trees', 'plastering', 'playing_accordion', 'playing_badminton', 'playing_bagpipes', 'playing_basketball', 'playing_bass_guitar', 'playing_cards', 'playing_cello', 'playing_chess', 'playing_clarinet', 'playing_controller', 'playing_cricket', 'playing_cymbals', 'playing_didgeridoo', 'playing_drums', 'playing_flute', 'playing_guitar', 'playing_harmonica', 'playing_harp', 'playing_ice_hockey', 'playing_keyboard', 'playing_kickball', 'playing_monopoly', 'playing_organ', 'playing_paintball', 'playing_piano', 'playing_poker', 'playing_recorder', 'playing_saxophone', 'playing_squash_or_racquetball', 'playing_tennis', 'playing_trombone', 'playing_trumpet', 'playing_ukulele', 'playing_violin', 'playing_volleyball', 'playing_xylophone', 'pole_vault', 'presenting_weather_forecast', 'pull_ups', 'pumping_fist', 'pumping_gas', 'punching_bag', 'punching_person_-boxing-', 'push_up', 'pushing_car', 'pushing_cart', 'pushing_wheelchair', 'reading_book', 'reading_newspaper', 'recording_music', 'riding_a_bike', 'riding_camel', 'riding_elephant', 'riding_mechanical_bull', 'riding_mountain_bike', 'riding_mule', 'riding_or_walking_with_horse', 'riding_scooter', 'riding_unicycle', 'ripping_paper', 'robot_dancing', 'rock_climbing', 'rock_scissors_paper', 'roller_skating', 'running_on_treadmill', 'sailing', 'salsa_dancing', 'sanding_floor', 'scrambling_eggs', 'scuba_diving', 'setting_table', 'shaking_hands', 'shaking_head', 'sharpening_knives', 'sharpening_pencil', 'shaving_head', 'shaving_legs', 'shearing_sheep', 'shining_shoes', 'shooting_basketball', 'shooting_goal_-soccer-', 'shot_put', 'shoveling_snow', 'shredding_paper', 'shuffling_cards', 'side_kick', 'sign_language_interpreting', 'singing', 'situp', 'skateboarding', 'ski_jumping', 'skiing_-not_slalom_or_crosscountry-', 'skiing_crosscountry', 'skiing_slalom', 'skipping_rope', 'skydiving', 'slacklining', 'slapping', 'sled_dog_racing', 'smoking', 'smoking_hookah', 'snatch_weight_lifting', 'sneezing', 'sniffing', 'snorkeling', 'snowboarding', 'snowkiting', 'snowmobiling', 'somersaulting', 'spinning_poi', 'spray_painting', 'spraying', 'springboard_diving', 'squat', 'sticking_tongue_out', 'stomping_grapes', 'stretching_arm', 'stretching_leg', 'strumming_guitar', 'surfing_crowd', 'surfing_water', 'sweeping_floor', 'swimming_backstroke', 'swimming_breast_stroke', 'swimming_butterfly_stroke', 'swing_dancing', 'swinging_legs', 'swinging_on_something', 'sword_fighting', 'tai_chi', 'taking_a_shower', 'tango_dancing', 'tap_dancing', 'tapping_guitar', 'tapping_pen', 'tasting_beer', 'tasting_food', 'testifying', 'texting', 'throwing_axe', 'throwing_ball', 'throwing_discus', 'tickling', 'tobogganing', 'tossing_coin', 'tossing_salad', 'training_dog', 'trapezing', 'trimming_or_shaving_beard', 'trimming_trees', 'triple_jump', 'tying_bow_tie', 'tying_knot_-not_on_a_tie-', 'tying_tie', 'unboxing', 'unloading_truck', 'using_computer', 'using_remote_controller_-not_gaming-', 'using_segway', 'vault', 'waiting_in_line', 'walking_the_dog', 'washing_dishes', 'washing_feet', 'washing_hair', 'washing_hands', 'water_skiing', 'water_sliding', 'watering_plants', 'waxing_back', 'waxing_chest', 'waxing_eyebrows', 'waxing_legs', 'weaving_basket', 'welding', 'whistling', 'windsurfing', 'wrapping_present', 'wrestling', 'writing', 'yawning', 'yoga', 'zumba']

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23,842

py

Python

watcher/api/controllers/v1/action_plan.py

ajaytikoo/watcher

6dbac1f6ae7f3e10dfdcef5721fa4af7af54e159

[ "Apache-2.0" ]

64

2015-10-18T02:57:24.000Z

2022-01-13T11:27:51.000Z

watcher/api/controllers/v1/action_plan.py

ajaytikoo/watcher

6dbac1f6ae7f3e10dfdcef5721fa4af7af54e159

[ "Apache-2.0" ]

null

watcher/api/controllers/v1/action_plan.py

ajaytikoo/watcher

6dbac1f6ae7f3e10dfdcef5721fa4af7af54e159

[ "Apache-2.0" ]

35

2015-12-25T13:53:21.000Z

2021-07-19T15:50:16.000Z

# -*- encoding: utf-8 -*- # Copyright 2013 Red Hat, Inc. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or # implied. # See the License for the specific language governing permissions and # limitations under the License. """ An :ref:`Action Plan <action_plan_definition>` specifies a flow of :ref:`Actions <action_definition>` that should be executed in order to satisfy a given :ref:`Goal <goal_definition>`. It also contains an estimated :ref:`global efficacy <efficacy_definition>` alongside a set of :ref:`efficacy indicators <efficacy_indicator_definition>`. An :ref:`Action Plan <action_plan_definition>` is generated by Watcher when an :ref:`Audit <audit_definition>` is successful which implies that the :ref:`Strategy <strategy_definition>` which was used has found a :ref:`Solution <solution_definition>` to achieve the :ref:`Goal <goal_definition>` of this :ref:`Audit <audit_definition>`. In the default implementation of Watcher, an action plan is composed of a list of successive :ref:`Actions <action_definition>` (i.e., a Workflow of :ref:`Actions <action_definition>` belonging to a unique branch). However, Watcher provides abstract interfaces for many of its components, allowing other implementations to generate and handle more complex :ref:`Action Plan(s) <action_plan_definition>` composed of two types of Action Item(s): - simple :ref:`Actions <action_definition>`: atomic tasks, which means it can not be split into smaller tasks or commands from an OpenStack point of view. - composite Actions: which are composed of several simple :ref:`Actions <action_definition>` ordered in sequential and/or parallel flows. An :ref:`Action Plan <action_plan_definition>` may be described using standard workflow model description formats such as `Business Process Model and Notation 2.0 (BPMN 2.0) <http://www.omg.org/spec/BPMN/2.0/>`_ or `Unified Modeling Language (UML) <http://www.uml.org/>`_. To see the life-cycle and description of :ref:`Action Plan <action_plan_definition>` states, visit :ref:`the Action Plan state machine <action_plan_state_machine>`. """ import datetime from http import HTTPStatus from oslo_log import log import pecan from pecan import rest import wsme from wsme import types as wtypes import wsmeext.pecan as wsme_pecan from watcher._i18n import _ from watcher.api.controllers import base from watcher.api.controllers import link from watcher.api.controllers.v1 import collection from watcher.api.controllers.v1 import efficacy_indicator as efficacyindicator from watcher.api.controllers.v1 import types from watcher.api.controllers.v1 import utils as api_utils from watcher.applier import rpcapi from watcher.common import exception from watcher.common import policy from watcher.common import utils from watcher import objects from watcher.objects import action_plan as ap_objects LOG = log.getLogger(__name__) def hide_fields_in_newer_versions(obj): """This method hides fields that were added in newer API versions. Certain node fields were introduced at certain API versions. These fields are only made available when the request's API version matches or exceeds the versions when these fields were introduced. """ pass class ActionPlanPatchType(types.JsonPatchType): @staticmethod def _validate_state(patch): serialized_patch = {'path': patch.path, 'op': patch.op} if patch.value is not wtypes.Unset: serialized_patch['value'] = patch.value # todo: use state machines to handle state transitions state_value = patch.value if state_value and not hasattr(ap_objects.State, state_value): msg = _("Invalid state: %(state)s") raise exception.PatchError( patch=serialized_patch, reason=msg % dict(state=state_value)) @staticmethod def validate(patch): if patch.path == "/state": ActionPlanPatchType._validate_state(patch) return types.JsonPatchType.validate(patch) @staticmethod def internal_attrs(): return types.JsonPatchType.internal_attrs() @staticmethod def mandatory_attrs(): return ["audit_id", "state"] class ActionPlan(base.APIBase): """API representation of a action plan. This class enforces type checking and value constraints, and converts between the internal object model and the API representation of an action plan. """ _audit_uuid = None _strategy_uuid = None _strategy_name = None _efficacy_indicators = None def _get_audit_uuid(self): return self._audit_uuid def _set_audit_uuid(self, value): if value == wtypes.Unset: self._audit_uuid = wtypes.Unset elif value and self._audit_uuid != value: try: audit = objects.Audit.get(pecan.request.context, value) self._audit_uuid = audit.uuid self.audit_id = audit.id except exception.AuditNotFound: self._audit_uuid = None def _get_efficacy_indicators(self): if self._efficacy_indicators is None: self._set_efficacy_indicators(wtypes.Unset) return self._efficacy_indicators def _set_efficacy_indicators(self, value): efficacy_indicators = [] if value == wtypes.Unset and not self._efficacy_indicators: try: _efficacy_indicators = objects.EfficacyIndicator.list( pecan.request.context, filters={"action_plan_uuid": self.uuid}) for indicator in _efficacy_indicators: efficacy_indicator = efficacyindicator.EfficacyIndicator( context=pecan.request.context, name=indicator.name, description=indicator.description, unit=indicator.unit, value=float(indicator.value), ) efficacy_indicators.append(efficacy_indicator.as_dict()) self._efficacy_indicators = efficacy_indicators except exception.EfficacyIndicatorNotFound as exc: LOG.exception(exc) elif value and self._efficacy_indicators != value: self._efficacy_indicators = value def _get_strategy(self, value): if value == wtypes.Unset: return None strategy = None try: if utils.is_uuid_like(value) or utils.is_int_like(value): strategy = objects.Strategy.get( pecan.request.context, value) else: strategy = objects.Strategy.get_by_name( pecan.request.context, value) except exception.StrategyNotFound: pass if strategy: self.strategy_id = strategy.id return strategy def _get_strategy_uuid(self): return self._strategy_uuid def _set_strategy_uuid(self, value): if value and self._strategy_uuid != value: self._strategy_uuid = None strategy = self._get_strategy(value) if strategy: self._strategy_uuid = strategy.uuid def _get_strategy_name(self): return self._strategy_name def _set_strategy_name(self, value): if value and self._strategy_name != value: self._strategy_name = None strategy = self._get_strategy(value) if strategy: self._strategy_name = strategy.name uuid = wtypes.wsattr(types.uuid, readonly=True) """Unique UUID for this action plan""" audit_uuid = wtypes.wsproperty(types.uuid, _get_audit_uuid, _set_audit_uuid, mandatory=True) """The UUID of the audit this port belongs to""" strategy_uuid = wtypes.wsproperty( wtypes.text, _get_strategy_uuid, _set_strategy_uuid, mandatory=False) """Strategy UUID the action plan refers to""" strategy_name = wtypes.wsproperty( wtypes.text, _get_strategy_name, _set_strategy_name, mandatory=False) """The name of the strategy this action plan refers to""" efficacy_indicators = wtypes.wsproperty( types.jsontype, _get_efficacy_indicators, _set_efficacy_indicators, mandatory=True) """The list of efficacy indicators associated to this action plan""" global_efficacy = wtypes.wsattr(types.jsontype, readonly=True) """The global efficacy of this action plan""" state = wtypes.text """This action plan state""" links = wtypes.wsattr([link.Link], readonly=True) """A list containing a self link and associated action links""" hostname = wtypes.wsattr(wtypes.text, mandatory=False) """Hostname the actionplan is running on""" def __init__(self, **kwargs): super(ActionPlan, self).__init__() self.fields = [] fields = list(objects.ActionPlan.fields) for field in fields: # Skip fields we do not expose. if not hasattr(self, field): continue self.fields.append(field) setattr(self, field, kwargs.get(field, wtypes.Unset)) self.fields.append('audit_uuid') self.fields.append('efficacy_indicators') setattr(self, 'audit_uuid', kwargs.get('audit_id', wtypes.Unset)) fields.append('strategy_uuid') setattr(self, 'strategy_uuid', kwargs.get('strategy_id', wtypes.Unset)) fields.append('strategy_name') setattr(self, 'strategy_name', kwargs.get('strategy_id', wtypes.Unset)) @staticmethod def _convert_with_links(action_plan, url, expand=True): if not expand: action_plan.unset_fields_except( ['uuid', 'state', 'efficacy_indicators', 'global_efficacy', 'updated_at', 'audit_uuid', 'strategy_uuid', 'strategy_name']) action_plan.links = [ link.Link.make_link( 'self', url, 'action_plans', action_plan.uuid), link.Link.make_link( 'bookmark', url, 'action_plans', action_plan.uuid, bookmark=True)] return action_plan @classmethod def convert_with_links(cls, rpc_action_plan, expand=True): action_plan = ActionPlan(**rpc_action_plan.as_dict()) hide_fields_in_newer_versions(action_plan) return cls._convert_with_links(action_plan, pecan.request.host_url, expand) @classmethod def sample(cls, expand=True): sample = cls(uuid='9ef4d84c-41e8-4418-9220-ce55be0436af', state='ONGOING', created_at=datetime.datetime.utcnow(), deleted_at=None, updated_at=datetime.datetime.utcnow()) sample._audit_uuid = 'abcee106-14d3-4515-b744-5a26885cf6f6' sample._efficacy_indicators = [{'description': 'Test indicator', 'name': 'test_indicator', 'unit': '%'}] sample._global_efficacy = {'description': 'Global efficacy', 'name': 'test_global_efficacy', 'unit': '%'} return cls._convert_with_links(sample, 'http://localhost:9322', expand) class ActionPlanCollection(collection.Collection): """API representation of a collection of action_plans.""" action_plans = [ActionPlan] """A list containing action_plans objects""" def __init__(self, **kwargs): self._type = 'action_plans' @staticmethod def convert_with_links(rpc_action_plans, limit, url=None, expand=False, **kwargs): ap_collection = ActionPlanCollection() ap_collection.action_plans = [ActionPlan.convert_with_links( p, expand) for p in rpc_action_plans] ap_collection.next = ap_collection.get_next(limit, url=url, **kwargs) return ap_collection @classmethod def sample(cls): sample = cls() sample.action_plans = [ActionPlan.sample(expand=False)] return sample class ActionPlansController(rest.RestController): """REST controller for Actions.""" def __init__(self): super(ActionPlansController, self).__init__() self.applier_client = rpcapi.ApplierAPI() from_actionsPlans = False """A flag to indicate if the requests to this controller are coming from the top-level resource ActionPlan.""" _custom_actions = { 'start': ['POST'], 'detail': ['GET'] } def _get_action_plans_collection(self, marker, limit, sort_key, sort_dir, expand=False, resource_url=None, audit_uuid=None, strategy=None): additional_fields = ['audit_uuid', 'strategy_uuid', 'strategy_name'] api_utils.validate_sort_key( sort_key, list(objects.ActionPlan.fields) + additional_fields) limit = api_utils.validate_limit(limit) api_utils.validate_sort_dir(sort_dir) marker_obj = None if marker: marker_obj = objects.ActionPlan.get_by_uuid( pecan.request.context, marker) filters = {} if audit_uuid: filters['audit_uuid'] = audit_uuid if strategy: if utils.is_uuid_like(strategy): filters['strategy_uuid'] = strategy else: filters['strategy_name'] = strategy need_api_sort = api_utils.check_need_api_sort(sort_key, additional_fields) sort_db_key = (sort_key if not need_api_sort else None) action_plans = objects.ActionPlan.list( pecan.request.context, limit, marker_obj, sort_key=sort_db_key, sort_dir=sort_dir, filters=filters) action_plans_collection = ActionPlanCollection.convert_with_links( action_plans, limit, url=resource_url, expand=expand, sort_key=sort_key, sort_dir=sort_dir) if need_api_sort: api_utils.make_api_sort(action_plans_collection.action_plans, sort_key, sort_dir) return action_plans_collection @wsme_pecan.wsexpose(ActionPlanCollection, types.uuid, int, wtypes.text, wtypes.text, types.uuid, wtypes.text) def get_all(self, marker=None, limit=None, sort_key='id', sort_dir='asc', audit_uuid=None, strategy=None): """Retrieve a list of action plans. :param marker: pagination marker for large data sets. :param limit: maximum number of resources to return in a single result. :param sort_key: column to sort results by. Default: id. :param sort_dir: direction to sort. "asc" or "desc". Default: asc. :param audit_uuid: Optional UUID of an audit, to get only actions for that audit. :param strategy: strategy UUID or name to filter by """ context = pecan.request.context policy.enforce(context, 'action_plan:get_all', action='action_plan:get_all') return self._get_action_plans_collection( marker, limit, sort_key, sort_dir, audit_uuid=audit_uuid, strategy=strategy) @wsme_pecan.wsexpose(ActionPlanCollection, types.uuid, int, wtypes.text, wtypes.text, types.uuid, wtypes.text) def detail(self, marker=None, limit=None, sort_key='id', sort_dir='asc', audit_uuid=None, strategy=None): """Retrieve a list of action_plans with detail. :param marker: pagination marker for large data sets. :param limit: maximum number of resources to return in a single result. :param sort_key: column to sort results by. Default: id. :param sort_dir: direction to sort. "asc" or "desc". Default: asc. :param audit_uuid: Optional UUID of an audit, to get only actions for that audit. :param strategy: strategy UUID or name to filter by """ context = pecan.request.context policy.enforce(context, 'action_plan:detail', action='action_plan:detail') # NOTE(lucasagomes): /detail should only work agaist collections parent = pecan.request.path.split('/')[:-1][-1] if parent != "action_plans": raise exception.HTTPNotFound expand = True resource_url = '/'.join(['action_plans', 'detail']) return self._get_action_plans_collection( marker, limit, sort_key, sort_dir, expand, resource_url, audit_uuid=audit_uuid, strategy=strategy) @wsme_pecan.wsexpose(ActionPlan, types.uuid) def get_one(self, action_plan_uuid): """Retrieve information about the given action plan. :param action_plan_uuid: UUID of a action plan. """ if self.from_actionsPlans: raise exception.OperationNotPermitted context = pecan.request.context action_plan = api_utils.get_resource('ActionPlan', action_plan_uuid) policy.enforce( context, 'action_plan:get', action_plan, action='action_plan:get') return ActionPlan.convert_with_links(action_plan) @wsme_pecan.wsexpose(None, types.uuid, status_code=HTTPStatus.NO_CONTENT) def delete(self, action_plan_uuid): """Delete an action plan. :param action_plan_uuid: UUID of a action. """ context = pecan.request.context action_plan = api_utils.get_resource( 'ActionPlan', action_plan_uuid, eager=True) policy.enforce(context, 'action_plan:delete', action_plan, action='action_plan:delete') allowed_states = (ap_objects.State.SUCCEEDED, ap_objects.State.RECOMMENDED, ap_objects.State.FAILED, ap_objects.State.SUPERSEDED, ap_objects.State.CANCELLED) if action_plan.state not in allowed_states: raise exception.DeleteError( state=action_plan.state) action_plan.soft_delete() @wsme.validate(types.uuid, [ActionPlanPatchType]) @wsme_pecan.wsexpose(ActionPlan, types.uuid, body=[ActionPlanPatchType]) def patch(self, action_plan_uuid, patch): """Update an existing action plan. :param action_plan_uuid: UUID of a action plan. :param patch: a json PATCH document to apply to this action plan. """ if self.from_actionsPlans: raise exception.OperationNotPermitted context = pecan.request.context action_plan_to_update = api_utils.get_resource( 'ActionPlan', action_plan_uuid, eager=True) policy.enforce(context, 'action_plan:update', action_plan_to_update, action='action_plan:update') try: action_plan_dict = action_plan_to_update.as_dict() action_plan = ActionPlan(**api_utils.apply_jsonpatch( action_plan_dict, patch)) except api_utils.JSONPATCH_EXCEPTIONS as e: raise exception.PatchError(patch=patch, reason=e) launch_action_plan = False cancel_action_plan = False # transitions that are allowed via PATCH allowed_patch_transitions = [ (ap_objects.State.RECOMMENDED, ap_objects.State.PENDING), (ap_objects.State.RECOMMENDED, ap_objects.State.CANCELLED), (ap_objects.State.ONGOING, ap_objects.State.CANCELLING), (ap_objects.State.PENDING, ap_objects.State.CANCELLED), ] # todo: improve this in blueprint watcher-api-validation if hasattr(action_plan, 'state'): transition = (action_plan_to_update.state, action_plan.state) if transition not in allowed_patch_transitions: error_message = _("State transition not allowed: " "(%(initial_state)s -> %(new_state)s)") raise exception.PatchError( patch=patch, reason=error_message % dict( initial_state=action_plan_to_update.state, new_state=action_plan.state)) if action_plan.state == ap_objects.State.PENDING: launch_action_plan = True if action_plan.state == ap_objects.State.CANCELLED: cancel_action_plan = True # Update only the fields that have changed for field in objects.ActionPlan.fields: try: patch_val = getattr(action_plan, field) except AttributeError: # Ignore fields that aren't exposed in the API continue if patch_val == wtypes.Unset: patch_val = None if action_plan_to_update[field] != patch_val: action_plan_to_update[field] = patch_val if (field == 'state' and patch_val == objects.action_plan.State.PENDING): launch_action_plan = True action_plan_to_update.save() # NOTE: if action plan is cancelled from pending or recommended # state update action state here only if cancel_action_plan: filters = {'action_plan_uuid': action_plan.uuid} actions = objects.Action.list(pecan.request.context, filters=filters, eager=True) for a in actions: a.state = objects.action.State.CANCELLED a.save() if launch_action_plan: self.applier_client.launch_action_plan(pecan.request.context, action_plan.uuid) action_plan_to_update = objects.ActionPlan.get_by_uuid( pecan.request.context, action_plan_uuid) return ActionPlan.convert_with_links(action_plan_to_update) @wsme_pecan.wsexpose(ActionPlan, types.uuid) def start(self, action_plan_uuid, **kwargs): """Start an action_plan :param action_plan_uuid: UUID of an action_plan. """ action_plan_to_start = api_utils.get_resource( 'ActionPlan', action_plan_uuid, eager=True) context = pecan.request.context policy.enforce(context, 'action_plan:start', action_plan_to_start, action='action_plan:start') if action_plan_to_start['state'] != \ objects.action_plan.State.RECOMMENDED: raise exception.StartError( state=action_plan_to_start.state) action_plan_to_start['state'] = objects.action_plan.State.PENDING action_plan_to_start.save() self.applier_client.launch_action_plan(pecan.request.context, action_plan_uuid) action_plan_to_start = objects.ActionPlan.get_by_uuid( pecan.request.context, action_plan_uuid) return ActionPlan.convert_with_links(action_plan_to_start)

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false

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0

null

0

null

0

1

0

0ad57f93e09c3cfa475ee8a3a4f941a9c684524d

1,613

py

Python

run.py

shark803/Torch_serve_example_NLP

7f7984a1668f21aced3a7a1e8ddac3c8e0ff0105

[ "MIT" ]

1

2021-11-19T07:59:58.000Z

run.py

shark803/Torch_serve_example_NLP

7f7984a1668f21aced3a7a1e8ddac3c8e0ff0105

[ "MIT" ]

null

run.py

shark803/Torch_serve_example_NLP

7f7984a1668f21aced3a7a1e8ddac3c8e0ff0105

[ "MIT" ]

null

# coding: UTF-8 import time import torch import numpy as np from train_eval import train, init_network from importlib import import_module import argparse parser = argparse.ArgumentParser(description='Chinese Text Classification') parser.add_argument('--model', type=str, required=True, help='choose a model: TextCNN') parser.add_argument('--embedding', default='pre_trained', type=str, help='random or pre_trained') parser.add_argument('--word', default=False, type=bool, help='True for word, False for char') args = parser.parse_args() if __name__ == '__main__': dataset = 'THUCNews' # 数据集 # 搜狗新闻:embedding_SougouNews.npz, 腾讯:embedding_Tencent.npz, 随机初始化:random # embedding = 'random' model_name = args.model # TextCNN from utils import build_dataset, build_iterator, get_time_dif x = import_module('models.' + model_name) from config import Config config = Config(dataset) np.random.seed(1) torch.manual_seed(1) torch.cuda.manual_seed_all(1) torch.backends.cudnn.deterministic = True # 保证每次结果一样 start_time = time.time() print("Loading data...") vocab, train_data, dev_data, test_data = build_dataset(config, args.word) train_iter = build_iterator(train_data, config) dev_iter = build_iterator(dev_data, config) test_iter = build_iterator(test_data, config) time_dif = get_time_dif(start_time) print("Time usage:", time_dif) # train config.n_vocab = len(vocab) model = x.Model().to(config.device) init_network(model) print(model.parameters) train(config, model, train_iter, dev_iter, test_iter)

32.918367

97

0.726596

223

1,613

5.017937

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0ad630d29820371f228b1287947197de5ede3fb0

5,954

py

Python

tests/mb_util.py

vasilydenisenko/modbus_rtu_slave

8a531b776ab82c60b5d335f0565468f19a7801f5

[ "MIT" ]

null

tests/mb_util.py

vasilydenisenko/modbus_rtu_slave

8a531b776ab82c60b5d335f0565468f19a7801f5

[ "MIT" ]

null

tests/mb_util.py

vasilydenisenko/modbus_rtu_slave

8a531b776ab82c60b5d335f0565468f19a7801f5

[ "MIT" ]

null

# MIT License # Copyright (c) 2021 Vasily Denisenko, Sergey Kuznetsov # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import mb_bsp PDU_SIZE_REG = 0 CONFIG_REG = 1 SLAVE_ADDR_REG = 2 CS_REG = 3 MB_MAX_WRITE_REGNUM = 123 MB_MAX_READ_REGNUM = 125 MB_MAX_REG_ADDR = 65535 MB_MAX_REG_VAL = 65535 MB_MAX_SLAVE_ADDR = 247 MB_MIN_SLAVE_ADDR = 1 MB_MAX_PDU_SIZE = 253 MB_MIN_PDU_SIZE = 1 FCODE_0x3 = 0x3 FCODE_0x6 = 0x6 FCODE_0x10 = 0x10 def incr_err_count(): incr_err_count.count += 1 setattr(incr_err_count, 'count', 0) def wait_mb_master_status(status): mb_bsp.wait_master_status(status) # 'FSM status' or 'PDU status' if mb_bsp.alarm_cb.status_timeout == 1: print('*** Test FAILED: ', status , ' timeout ***') mb_bsp.alarm_cb.status_timeout = 0 incr_err_count() def config_modbus(modbus_role, slave_addr, pdu, config_val): wait_mb_master_status('FSM status') if modbus_role == 'Master': mb_bsp.write_mb_master_cs(CONFIG_REG, config_val) # Set configuration mb_bsp.write_mb_master_cs(SLAVE_ADDR_REG, slave_addr) # Set slave address mb_bsp.write_mb_master_cs(PDU_SIZE_REG, len(pdu)) # Set request PDU size mb_bsp.write_mb_master_pdu(pdu) # Set request PDU else: mb_bsp.write_mb_slave_cs(CONFIG_REG, config_val) # Set configuration mb_bsp.write_mb_slave_cs(SLAVE_ADDR_REG, slave_addr) # Set slave address def generate_0x03_pdu(addr, regnum): pdu = list() ref_pdu = list() pdu.append(0x3) ref_pdu.append(0x3) addr_h = (addr & 0xff00) >> 8 pdu.append(addr_h) addr_l = (addr & 0xff) pdu.append(addr_l) regnum_h = (regnum & 0xff00) >> 8 pdu.append(regnum_h) regnum_l = regnum & 0xff pdu.append(regnum_l) bytecount = regnum << 1 ref_pdu.append(bytecount) for i in range(bytecount): ref_pdu.append(0) return [pdu, ref_pdu] def generate_0x06_pdu(addr, regval): pdu = list() pdu.append(0x6) addr_h = (addr & 0xff00) >> 8 pdu.append(addr_h) addr_l = (addr & 0xff) pdu.append(addr_l) regval_h = (regval[0] & 0xff00) >> 8 pdu.append(regval_h) regval_l = regval[0] & 0xff pdu.append(regval_l) ref_pdu = pdu.copy() return [pdu, ref_pdu] def generate_0x10_pdu(addr, regnum, regval): pdu = list() pdu.append(0x10) addr_h = (addr & 0xff00) >> 8 pdu.append(addr_h) addr_l = (addr & 0xff) pdu.append(addr_l) regnum_h = (regnum & 0xff00) >> 8 pdu.append(regnum_h) regnum_l = regnum & 0xff pdu.append(regnum_l) ref_pdu = pdu.copy() bytecount = regnum_l << 1 pdu.append(bytecount) for i in range(regnum_l): regval_h = (regval[i] & 0xff00) >> 8 pdu.append(regval_h) regval_l = regval[i] & 0xff pdu.append(regval_l) return [pdu, ref_pdu] def print_test_result(result_ok): if result_ok: msg = '\tTest Successful' else: msg = '\tTest FAILED' print() print('***************************') print(msg) print('***************************') print() def get_total_error_count(modbus_role): count = 0 error_tuple = mb_bsp.get_error_count() if modbus_role == 'Both': for err_list in error_tuple: for i in err_list: count += i elif modbus_role == 'Master': for i in error_tuple[0]: count += i elif modbus_role == 'Slave': for i in error_tuple[1]: count += i return count def get_single_error_count(modbus_role, error_type): error_tuple = mb_bsp.get_error_count() count = 0 if modbus_role == 'Master': if error_type == 'parity': count = error_tuple[0][0] elif error_type == 'start bit': count = error_tuple[0][1] elif error_type == 'stop bit': count = error_tuple[0][2] elif error_type == 'address': count = error_tuple[0][3] elif error_type == 'crc': count = error_tuple[0][4] elif modbus_role == 'Slave': if error_type == 'parity': count = error_tuple[1][0] elif error_type == 'start bit': count = error_tuple[1][1] elif error_type == 'stop bit': count = error_tuple[1][2] elif error_type == 'address': count = error_tuple[1][3] elif error_type == 'crc': count = error_tuple[1][4] return count def print_error_count(): error_tuple = mb_bsp.get_error_count() print() print('master_parity_err_count = ', error_tuple[0][0]) print('master_start_bit_err_count = ', error_tuple[0][1]) print('master_stop_bit_err_count = ', error_tuple[0][2]) print('master_addr_err_count = ', error_tuple[0][3]) print('master_crc_err_count = ', error_tuple[0][4]) print('slave_parity_err_count = ', error_tuple[1][0]) print('slave_start_bit_err_count = ', error_tuple[1][1]) print('slave_stop_bit_err_count = ', error_tuple[1][2]) print('slave_addr_err_count = ', error_tuple[1][3]) print('slave_crc_err_count = ', error_tuple[1][4]) print('--------------------------------') print()

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null

0

null

0

1

0

0ad85408ba998c356a370a0f1582159d01f77a69

8,390

py

Python

carto/maps.py

danicarrion/carto-python

631b018f065960baa35473e2087ce598560b9e17

[ "BSD-3-Clause" ]

85

2016-08-07T16:46:58.000Z

2022-03-23T01:44:02.000Z

carto/maps.py

danicarrion/carto-python

631b018f065960baa35473e2087ce598560b9e17

[ "BSD-3-Clause" ]

109

2016-08-02T18:40:04.000Z

2021-08-23T08:08:02.000Z

carto/maps.py

danicarrion/carto-python

631b018f065960baa35473e2087ce598560b9e17

[ "BSD-3-Clause" ]

29

2016-11-29T03:42:47.000Z

2022-01-23T17:37:11.000Z

""" Module for working with named and anonymous maps .. module:: carto.maps :platform: Unix, Windows :synopsis: Module for working with named and anonymous maps .. moduleauthor:: Daniel Carrion <daniel@carto.com> .. moduleauthor:: Alberto Romeu <alrocar@carto.com> """ try: from urllib.parse import urljoin except ImportError: from urlparse import urljoin from pyrestcli.resources import Manager, Resource from .exceptions import CartoException, CartoRateLimitException API_VERSION = "v1" NAMED_API_ENDPOINT = "api/{api_version}/map/named/" ANONYMOUS_API_ENDPOINT = "api/{api_version}/map/" class BaseMap(Resource): """ Base class for NamedMap and AnonymousMap """ def __init__(self, auth_client): """ Initializes a BaseMap instance :param auth_client: Auth client """ super(BaseMap, self).__init__(auth_client) def get_tile_url(self, x, y, z, layer_id=None, feature_id=None, filter=None, extension="png"): """ Prepares a URL to get data (raster or vector) from a NamedMap or AnonymousMap :param x: The x tile :param y: The y tile :param z: The zoom level :param layer_id: Can be a number (referring to the # layer of your \ map), all layers of your map, or a list of layers. To show just the basemap layer, enter the value 0 To show the first layer, enter the value 1 To show all layers, enter the value 'all' To show a list of layers, enter the comma separated \ layer value as '0,1,2' :param feature_id: The id of the feature :param filter: The filter to be applied to the layer :param extension: The format of the data to be retrieved: png, mvt, ... :type x: int :type y: int :type z: int :type layer_id: str :type feature_id: str :type filter: str :type extension: str :return: A URL to download data :rtype: str :raise: CartoException """ base_url = self.client.base_url + self.Meta.collection_endpoint template_id = self.template_id if hasattr(self, 'template_id') \ else self.layergroupid if layer_id is not None and feature_id is not None: url = urljoin(base_url, "{template_id}/{layer}/attributes/{feature_id}"). \ format(template_id=template_id, layer=layer_id, feature_id=feature_id) elif layer_id is not None and filter is not None: url = urljoin(base_url, "{template_id}/{filter}/{z}/{x}/{y}.{extension}"). \ format(template_id=template_id, filter=filter, z=z, x=x, y=y, extension=extension) elif layer_id is not None: url = urljoin(base_url, "{template_id}/{layer}/{z}/{x}/{y}.{extension}"). \ format(template_id=template_id, layer=layer_id, z=z, x=x, y=y, extension=extension) else: url = urljoin(base_url, "{template_id}/{z}/{x}/{y}.{extension}"). \ format( template_id=template_id, z=z, x=x, y=y, extension=extension) if hasattr(self, 'auth') and self.auth is not None \ and len(self.auth['valid_tokens']) > 0: url = urljoin(url, "?auth_token={auth_token}"). \ format(auth_token=self.auth['valid_tokens'][0]) return url class NamedMap(BaseMap): """ Equivalent to creating a named map in CARTO. """ class Meta: collection_endpoint = NAMED_API_ENDPOINT.format( api_version=API_VERSION) id_field = "template_id" name_field = "name" def __str__(self): try: return unicode(self.name).encode("utf-8") except AttributeError: return super(NamedMap, self).__repr__() def __init__(self, auth_client): """ Initializes a NamedMap instance :param auth_client: Auth client """ self.fields = ["version", "name", "auth", "placeholders", "layergroup", "view"] # Optional fields can be assigned by some responses create, instantiate, # but are not saved to the backend self.optional_fields = ["template_id", "layergroupid", "last_updated"] super(NamedMap, self).__init__(auth_client) def instantiate(self, params, auth=None): """ Allows you to fetch the map tiles of a created map :param params: The json with the styling info for the named map :param auth: The auth client :type params: dict :type auth: :class:`carto.auth.APIKeyAuthClient` :return: :raise: CartoException """ try: endpoint = (self.Meta.collection_endpoint + "{template_id}"). \ format(template_id=self.template_id) if (auth is not None): endpoint = (endpoint + "?auth_token={auth_token}"). \ format(auth_token=auth) self.send(endpoint, "POST", json=params) except CartoRateLimitException as e: raise e except Exception as e: raise CartoException(e) def update_from_dict(self, attribute_dict): """ Method overriden from the base class """ if 'template' in attribute_dict: self.update_from_dict(attribute_dict['template']) setattr(self, self.Meta.id_field, attribute_dict['template']['name']) return try: for k, v in attribute_dict.items(): if k in self.fields + self.optional_fields: setattr(self, k, v) except Exception: setattr(self, self.Meta.id_field, attribute_dict) class AnonymousMap(BaseMap): """ Equivalent to creating an anonymous map in CARTO. """ class Meta: collection_endpoint = ANONYMOUS_API_ENDPOINT.format( api_version=API_VERSION) def __init__(self, auth_client): """ Initializes an AnonymousMap instance :param auth_client: Auth client """ self.optional_fields = ['cdn_url', 'last_updated', 'layergroupid', 'metadata'] super(AnonymousMap, self).__init__(auth_client) def instantiate(self, params): """ Allows you to fetch the map tiles of a created map :param params: The json with the styling info for the named map :type params: dict :return: :raise: CartoException """ try: self.send(self.Meta.collection_endpoint, "POST", json=params) except CartoRateLimitException as e: raise e except Exception as e: raise CartoException(e) def update_from_dict(self, attribute_dict): for k, v in attribute_dict.items(): if k in self.fields + self.optional_fields: setattr(self, k, v) class NamedMapManager(Manager): """ Manager for the NamedMap class """ resource_class = NamedMap json_collection_attribute = "template_ids" def create(self, **kwargs): """ Creates a named map :param kwargs: Attributes for creating the named map. Specifically an attribute `template` must contain the JSON object defining the named map :type kwargs: kwargs :return: New named map object :rtype: NamedMap :raise: CartoException """ resource = self.resource_class(self.client) resource.update_from_dict(kwargs['template']) resource.save(force_create=True) return resource

33.293651

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8,390

4.815565

0.203625

0.044277

0.013947

0.009741

0.423733

0.410228

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0.263006

0.193048

0.148771

0

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0

null

0

null

0

1

0

0ad8ce46348b78515a8db8b2c9bc54898f1ab6f9

1,208

py

Python

pytorch-frontend/benchmarks/operator_benchmark/pt/embeddingbag_test.py

AndreasKaratzas/stonne

2915fcc46cc94196303d81abbd1d79a56d6dd4a9

[ "MIT" ]

206

2020-11-28T22:56:38.000Z

2022-03-27T02:33:04.000Z

pytorch-frontend/benchmarks/operator_benchmark/pt/embeddingbag_test.py

AndreasKaratzas/stonne

2915fcc46cc94196303d81abbd1d79a56d6dd4a9

[ "MIT" ]

19

2020-12-09T23:13:14.000Z

2022-01-24T23:24:08.000Z

pytorch-frontend/benchmarks/operator_benchmark/pt/embeddingbag_test.py

AndreasKaratzas/stonne

2915fcc46cc94196303d81abbd1d79a56d6dd4a9

[ "MIT" ]

28

2020-11-29T15:25:12.000Z

2022-01-20T02:16:27.000Z

import operator_benchmark as op_bench import torch import numpy from . import configs """EmbeddingBag Operator Benchmark""" class EmbeddingBagBenchmark(op_bench.TorchBenchmarkBase): def init(self, embeddingbags, dim, mode, input_size, offset, sparse, include_last_offset, device): self.embedding = torch.nn.EmbeddingBag( num_embeddings=embeddingbags, embedding_dim=dim, mode=mode, include_last_offset=include_last_offset, sparse=sparse).to(device=device) numpy.random.seed((1 << 32) - 1) self.input = torch.tensor(numpy.random.randint(0, embeddingbags, input_size), device=device).long() offsets = torch.LongTensor([offset], device=device) self.offset = torch.cat((offsets, torch.tensor([self.input.size(0)], dtype=torch.long)), 0) self.set_module_name('embeddingbag') def forward(self): return self.embedding(self.input, self.offset) op_bench.generate_pt_test(configs.embeddingbag_short_configs, EmbeddingBagBenchmark) op_bench.generate_pt_gradient_test(configs.embeddingbag_short_configs, EmbeddingBagBenchmark) if __name__ == "__main__": op_bench.benchmark_runner.main()

38.967742

107

0.724338

144

1,208

5.826389

0.395833

0.041716

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0.040524

0.133492

0

0.006979

0.169702

1,208

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false

0

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0.347826

0

null

0

null

0

1

0

0ad9fee81c50ef01672c1f7b553d66bc07bc9155

3,972

py

Python

python/dgl/geometry/capi.py

lfchener/dgl

77f4287a4118db64c46f4f413a426e1419a09d53

[ "Apache-2.0" ]

9,516

2018-12-08T22:11:31.000Z

2022-03-31T13:04:33.000Z

python/dgl/geometry/capi.py

lfchener/dgl

77f4287a4118db64c46f4f413a426e1419a09d53

[ "Apache-2.0" ]

2,494

2018-12-08T22:43:00.000Z

2022-03-31T21:16:27.000Z

python/dgl/geometry/capi.py

lfchener/dgl

77f4287a4118db64c46f4f413a426e1419a09d53

[ "Apache-2.0" ]

2,529

2018-12-08T22:56:14.000Z

2022-03-31T13:07:41.000Z

"""Python interfaces to DGL farthest point sampler.""" from dgl._ffi.base import DGLError import numpy as np from .._ffi.function import _init_api from .. import backend as F from .. import ndarray as nd def _farthest_point_sampler(data, batch_size, sample_points, dist, start_idx, result): r"""Farthest Point Sampler Parameters ---------- data : tensor A tensor of shape (N, d) where N is the number of points and d is the dimension. batch_size : int The number of batches in the ``data``. N should be divisible by batch_size. sample_points : int The number of points to sample in each batch. dist : tensor Pre-allocated tensor of shape (N, ) for to-sample distance. start_idx : tensor of int Pre-allocated tensor of shape (batch_size, ) for the starting sample in each batch. result : tensor of int Pre-allocated tensor of shape (sample_points * batch_size, ) for the sampled index. Returns ------- No return value. The input variable ``result`` will be overwriten with sampled indices. """ assert F.shape(data)[0] >= sample_points * batch_size assert F.shape(data)[0] % batch_size == 0 _CAPI_FarthestPointSampler(F.zerocopy_to_dgl_ndarray(data), batch_size, sample_points, F.zerocopy_to_dgl_ndarray(dist), F.zerocopy_to_dgl_ndarray(start_idx), F.zerocopy_to_dgl_ndarray(result)) def _neighbor_matching(graph_idx, num_nodes, edge_weights=None, relabel_idx=True): """ Description ----------- The neighbor matching procedure of edge coarsening used in `Metis <http://cacs.usc.edu/education/cs653/Karypis-METIS-SIAMJSC98.pdf>`__ and `Graclus <https://www.cs.utexas.edu/users/inderjit/public_papers/multilevel_pami.pdf>`__ for homogeneous graph coarsening. This procedure keeps picking an unmarked vertex and matching it with one its unmarked neighbors (that maximizes its edge weight) until no match can be done. If no edge weight is given, this procedure will randomly pick neighbor for each vertex. The GPU implementation is based on `A GPU Algorithm for Greedy Graph Matching <http://www.staff.science.uu.nl/~bisse101/Articles/match12.pdf>`__ NOTE: The input graph must be bi-directed (undirected) graph. Call :obj:`dgl.to_bidirected` if you are not sure your graph is bi-directed. Parameters ---------- graph : HeteroGraphIndex The input homogeneous graph. num_nodes : int The number of nodes in this homogeneous graph. edge_weight : tensor, optional The edge weight tensor holding non-negative scalar weight for each edge. default: :obj:`None` relabel_idx : bool, optional If true, relabel resulting node labels to have consecutive node ids. default: :obj:`True` Returns ------- a 1-D tensor A vector with each element that indicates the cluster ID of a vertex. """ edge_weight_capi = nd.NULL["int64"] if edge_weights is not None: edge_weight_capi = F.zerocopy_to_dgl_ndarray(edge_weights) node_label = F.full_1d( num_nodes, -1, getattr(F, graph_idx.dtype), F.to_backend_ctx(graph_idx.ctx)) node_label_capi = F.zerocopy_to_dgl_ndarray_for_write(node_label) _CAPI_NeighborMatching(graph_idx, edge_weight_capi, node_label_capi) if F.reduce_sum(node_label < 0).item() != 0: raise DGLError("Find unmatched node") # reorder node id # TODO: actually we can add `return_inverse` option for `unique` # function in backend for efficiency. if relabel_idx: node_label_np = F.zerocopy_to_numpy(node_label) _, node_label_np = np.unique(node_label_np, return_inverse=True) return F.tensor(node_label_np) else: return node_label _init_api('dgl.geometry', __name__)

38.563107

95

0.680514

558

3,972

4.646953

0.370968

0.03818

0.029695

0.032395

0.121481

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0

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102

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0

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1

0.064516

false

0

0.16129

0

0.290323

0

null

0

null

0

1

0

0adab04d82e555974b5ee3aecff08feca7c75415

6,478

py

Python

scidb/core/data.py

oxdc/sci.db

0a751a0e05e7ad4c83c350e32e32ea9ce5831cbb

[ "MIT" ]

null

scidb/core/data.py

oxdc/sci.db

0a751a0e05e7ad4c83c350e32e32ea9ce5831cbb

[ "MIT" ]

null

scidb/core/data.py

oxdc/sci.db

0a751a0e05e7ad4c83c350e32e32ea9ce5831cbb

[ "MIT" ]

null

import shutil import hashlib from pathlib import Path from typing import TextIO, BinaryIO, IO, Union from datetime import datetime from os.path import getmtime from .low import ObservableDict class Data: def __init__(self, data_name: str, parent, bucket, protected_parent_methods: Union[None, dict] = None): self.__data_name__ = data_name self.__parent__ = parent self.__bucket__ = bucket self.__protected_parent_methods__ = protected_parent_methods self.__protected_parent_methods__['increase_data_count']() self.init_metadata() self.init_properties() @property def database(self): return self.__bucket__.db @property def db(self): return self.__bucket__.db @property def bucket(self): return self.__bucket__ def init_metadata(self): if self.__data_name__ not in self.__parent__.metadata: self.__parent__.metadata[self.__data_name__] = dict() def init_properties(self): if self.__data_name__ not in self.__parent__.properties: self.__parent__.properties[self.__data_name__] = dict() def set_metadata(self, metadata: Union[None, dict], merge: bool = True): if metadata is None: return if merge: metadata = {**self.metadata, **metadata} self.__parent__.metadata[self.__data_name__] = metadata def set_properties(self, properties: Union[None, dict], merge: bool = True): if properties is None: return if merge: properties = {**self.properties, **properties} self.__parent__.properties[self.__data_name__] = properties @property def parent(self): return self.__parent__ @property def path(self) -> Path: return self.__parent__.path / self.__data_name__ @property def name(self) -> str: return self.__data_name__ @property def metadata(self) -> ObservableDict: return self.__parent__.metadata[self.__data_name__] @property def properties(self) -> ObservableDict: return self.__parent__.properties[self.__data_name__] def rename(self, new_name: str): shutil.move(str(self.path), str(self.__parent__.path / new_name)) self.__data_name__ = new_name def reader(self, binary: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: mode = 'r' mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def creator(self, binary: bool = False, confirm: bool = False, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if confirm and not feedback: return None mode = 'x' mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def writer(self, binary: bool = False, append: bool = True, allow_overwrite: bool = False, confirm: bool = True, feedback: bool = False, **kwargs) -> [IO, BinaryIO, TextIO, None]: if not allow_overwrite and not append: raise PermissionError('Trying to overwrite existed data.') if confirm and not feedback: return mode = 'a' if append else 'w' mode += 'b' if binary else '' return open(str(self.path), mode=mode, **kwargs) def __repr__(self): return f"Data('{self.__data_name__}')" def import_file(self, src_path: [str, Path], allow_overwrite=False, confirm=True, feedback=False): if self.path.exists() and not allow_overwrite: return if confirm and not feedback: return shutil.copyfile(str(src_path), str(self.path)) def export_file(self, dst_path: [str, Path], allow_overwrite=False): if Path(dst_path).exists() and not allow_overwrite: return shutil.copyfile(str(self.path), str(dst_path)) def __calc_hash__(self, h, buffer_size: int = 131072): if not self.path.exists(): return None with open(str(self.path), 'rb') as file_reader: while True: data = file_reader.read(buffer_size) if not data: break h.update(data) return h.hexdigest() def md5(self, buffer_size: int = 131072, require_update: bool = False) -> [str, None]: if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \ or 'md5' not in self.metadata \ or 'md5-timestamp' not in self.metadata \ or self.metadata['md5-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.md5(), buffer_size) self.metadata['md5'] = result self.metadata['md5-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['md5'] def sha1(self, buffer_size: int = 131072, require_update: bool = False) -> [str, None]: if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \ or 'sha1' not in self.metadata \ or 'sha1-timestamp' not in self.metadata \ or self.metadata['sha1-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha1(), buffer_size) self.metadata['sha1'] = result self.metadata['sha1-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha1'] def sha256(self, buffer_size: int = 131072, require_update: bool = False) -> [str, None]: if not self.path.exists(): return None last_modified_time = getmtime(str(self.path)) if require_update \ or 'sha256' not in self.metadata \ or 'sha256-timestamp' not in self.metadata \ or self.metadata['sha256-timestamp'] < last_modified_time: result = self.__calc_hash__(hashlib.sha256(), buffer_size) self.metadata['sha256'] = result self.metadata['sha256-timestamp'] = datetime.now().timestamp() return result else: return self.metadata['sha256']

35.988889

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6,478

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103

36.189944

0.789532

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false

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null

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null

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1

0

0adacd25859bed18399a4d523ba68cd8adb2bc90

39,932

py

Python

tensorflow/python/keras/optimizer_v2/optimizer_v2.py

PaulWang1905/tensorflow

ebf12d22b4801fb8dab5034cc94562bf7cc33fa0

[ "Apache-2.0" ]

9

2019-12-29T01:47:37.000Z

2021-12-21T13:47:41.000Z

tensorflow/python/keras/optimizer_v2/optimizer_v2.py

PaulWang1905/tensorflow

ebf12d22b4801fb8dab5034cc94562bf7cc33fa0

[ "Apache-2.0" ]

null

tensorflow/python/keras/optimizer_v2/optimizer_v2.py

PaulWang1905/tensorflow

ebf12d22b4801fb8dab5034cc94562bf7cc33fa0

[ "Apache-2.0" ]

1

2020-05-28T11:22:49.000Z

# Copyright 2018 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Version 2 of class Optimizer.""" # pylint: disable=g-bad-name from __future__ import absolute_import from __future__ import division from __future__ import print_function import abc import functools import six from tensorflow.python.distribute import distribution_strategy_context as distribute_ctx from tensorflow.python.distribute import reduce_util as ds_reduce_util from tensorflow.python.eager import backprop from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import initializers from tensorflow.python.keras.engine import base_layer_utils from tensorflow.python.keras.optimizer_v2 import learning_rate_schedule from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import clip_ops from tensorflow.python.ops import gradients from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variables as tf_variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import revived_types from tensorflow.python.training.tracking import base as trackable from tensorflow.python.util import nest from tensorflow.python.util.tf_export import keras_export def _deduplicate_indexed_slices(values, indices): """Sums `values` associated with any non-unique `indices`. Args: values: A `Tensor` with rank >= 1. indices: A one-dimensional integer `Tensor`, indexing into the first dimension of `values` (as in an IndexedSlices object). Returns: A tuple of (`summed_values`, `unique_indices`) where `unique_indices` is a de-duplicated version of `indices` and `summed_values` contains the sum of `values` slices associated with each unique index. """ unique_indices, new_index_positions = array_ops.unique(indices) summed_values = math_ops.unsorted_segment_sum( values, new_index_positions, array_ops.shape(unique_indices)[0]) return (summed_values, unique_indices) @six.add_metaclass(abc.ABCMeta) @keras_export("keras.optimizers.Optimizer") class OptimizerV2(trackable.Trackable): """Updated base class for optimizers. This class defines the API to add Ops to train a model. You never use this class directly, but instead instantiate one of its subclasses such as `tf.keras.optimizers.SGD`, `tf.keras.optimizers.Adam`. ### Usage ```python # Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument and returns the value # to minimize. loss = lambda: 3 * var1 * var1 + 2 * var2 * var2 # In graph mode, returns op that minimizes the loss by updating the listed # variables. opt_op = opt.minimize(loss, var_list=[var1, var2]) opt_op.run() # In eager mode, simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) ``` ### Custom training loop with Keras models In Keras models, sometimes variables are created when the model is first called, instead of construction time. Examples include 1) sequential models without input shape pre-defined, or 2) subclassed models. Pass var_list as callable in these cases. Example: ```python opt = tf.keras.optimizers.SGD(learning_rate=0.1) model = tf.keras.Sequential() model.add(tf.keras.layers.Dense(num_hidden, activation='relu')) model.add(tf.keras.layers.Dense(num_classes, activation='sigmoid') loss_fn = lambda: tf.keras.losses.mse(model(input), output) var_list_fn = lambda: model.trainable_weights for input, output in data: opt.minimize(loss_fn, var_list_fn) ``` ### Processing gradients before applying them. Calling `minimize()` takes care of both computing the gradients and applying them to the variables. If you want to process the gradients before applying them you can instead use the optimizer in three steps: 1. Compute the gradients with `tf.GradientTape`. 2. Process the gradients as you wish. 3. Apply the processed gradients with `apply_gradients()`. Example: ```python # Create an optimizer. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # Compute the gradients for a list of variables. with tf.GradientTape() as tape: loss = <call_loss_function> vars = <list_of_variables> grads = tape.gradient(loss, vars) processed_grads = [process_gradient(g) for g in grads] grads_and_vars = zip(processed_grads, var_list) # grads_and_vars is a list of tuples (gradient, variable). Do whatever you # need to the 'gradient' part, for example cap them, etc. capped_grads_and_vars = [(MyCapper(gv[0]), gv[1]) for gv in grads_and_vars] # Ask the optimizer to apply the capped gradients. opt.apply_gradients(capped_grads_and_vars) ``` ### Use with `tf.distribute.Strategy`. This optimizer class is `tf.distribute.Strategy` aware, which means it automatically sums gradients across all replicas. To average gradients, you divide your loss by the global batch size, which is done automatically if you use `tf.keras` built-in training or evaluation loops. See the `reduction` argument of your loss which should be set to `tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE` for averaging or `tf.keras.losses.Reduction.SUM` for not. If you are not using these and you want to average gradients, you should use `tf.math.reduce_sum` to add up your per-example losses and then divide by the global batch size. Note that when using `tf.distribute.Strategy`, the first component of a tensor's shape is the *replica-local* batch size, which is off by a factor equal to the number of replicas being used to compute a single step. As a result, using `tf.math.reduce_mean` will give the wrong answer, resulting in gradients that can be many times too big. ### Variable Constraint All Keras optimizers respect variable constraints. If constraint function is passed to any variable, the constraint will be applied to the variable after the gradient has been applied to the variable. Important: If gradient is sparse tensor, variable constraint is not supported. ### Thread Compatibility The entire optimizer is currently thread compatible, not thread-safe. The user needs to perform synchronization if necessary. ### Slots Many optimizer subclasses, such as `Adam` and `Adagrad` allocate and manage additional variables associated with the variables to train. These are called <i>Slots</i>. Slots have names and you can ask the optimizer for the names of the slots that it uses. Once you have a slot name you can ask the optimizer for the variable it created to hold the slot value. This can be useful if you want to log debug a training algorithm, report stats about the slots, etc. ### Hyper parameters These are arguments passed to the optimizer subclass constructor (the `__init__` method), and then passed to `self._set_hyper()`. They can be either regular Python values (like 1.0), tensors, or callables. If they are callable, the callable will be called during `apply_gradients()` to get the value for the hyper parameter. Hyper parameters can be overwritten through user code: Example: ```python # Create an optimizer with the desired parameters. opt = tf.keras.optimizers.SGD(learning_rate=0.1) # `loss` is a callable that takes no argument and returns the value # to minimize. loss = lambda: 3 * var1 + 2 * var2 # In eager mode, simply call minimize to update the list of variables. opt.minimize(loss, var_list=[var1, var2]) # update learning rate opt.learning_rate = 0.05 opt.minimize(loss, var_list=[var1, var2]) ``` ### Write a customized optimizer. If you intend to create your own optimization algorithm, simply inherit from this class and override the following methods: - resource_apply_dense (update variable given gradient tensor is dense) - resource_apply_sparse (update variable given gradient tensor is sparse) - create_slots (if your optimizer algorithm requires additional variables) - get_config (serialization of the optimizer, include all hyper parameters) """ def __init__(self, name, **kwargs): """Create a new Optimizer. This must be called by the constructors of subclasses. Note that Optimizer instances should not bind to a single graph, and so shouldn't keep Tensors as member variables. Generally you should be able to use the _set_hyper()/state.get_hyper() facility instead. This class in stateful and thread-compatible. Args: name: A non-empty string. The name to use for accumulators created for the optimizer. **kwargs: keyword arguments. Allowed to be {`clipnorm`, `clipvalue`, `lr`, `decay`}. `clipnorm` is clip gradients by norm; `clipvalue` is clip gradients by value, `decay` is included for backward compatibility to allow time inverse decay of learning rate. `lr` is included for backward compatibility, recommended to use `learning_rate` instead. Raises: ValueError: If name is malformed. RuntimeError: If _create_slots has been overridden instead of _create_vars. """ allowed_kwargs = {"clipnorm", "clipvalue", "lr", "decay"} for k in kwargs: if k not in allowed_kwargs: raise TypeError("Unexpected keyword argument " "passed to optimizer: " + str(k)) # checks that all keyword arguments are non-negative. if kwargs[k] < 0: raise ValueError("Expected {} >= 0, received: {}".format(k, kwargs[k])) self._use_locking = True self._name = name self._hyper = {} # dict: {variable name : {slot name : variable}} self._slots = {} self._slot_names = [] self._weights = [] self._iterations = None # For implementing Trackable. Stores information about how to restore # slot variables which have not yet been created # (trackable._CheckpointPosition objects). # {slot_name : # {_var_key(variable_to_train): [checkpoint_position, ... ], ... }, # ... } self._deferred_slot_restorations = {} decay = kwargs.pop("decay", 0.0) if decay < 0.: raise ValueError("decay cannot be less than 0: {}".format(decay)) self._initial_decay = decay if "clipnorm" in kwargs: self.clipnorm = kwargs.pop("clipnorm") if "clipvalue" in kwargs: self.clipvalue = kwargs.pop("clipvalue") self._hypers_created = False def minimize(self, loss, var_list, grad_loss=None, name=None): """Minimize `loss` by updating `var_list`. This method simply computes gradient using `tf.GradientTape` and calls `apply_gradients()`. If you want to process the gradient before applying then call `tf.GradientTape` and `apply_gradients()` explicitly instead of using this function. Args: loss: A callable taking no arguments which returns the value to minimize. var_list: list or tuple of `Variable` objects to update to minimize `loss`, or a callable returning the list or tuple of `Variable` objects. Use callable when the variable list would otherwise be incomplete before `minimize` since the variables are created at the first time `loss` is called. grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. name: Optional name for the returned operation. Returns: An Operation that updates the variables in `var_list`. If `global_step` was not `None`, that operation also increments `global_step`. Raises: ValueError: If some of the variables are not `Variable` objects. """ grads_and_vars = self._compute_gradients( loss, var_list=var_list, grad_loss=grad_loss) return self.apply_gradients(grads_and_vars, name=name) def _compute_gradients(self, loss, var_list, grad_loss=None): """Compute gradients of `loss` for the variables in `var_list`. This is the first part of `minimize()`. It returns a list of (gradient, variable) pairs where "gradient" is the gradient for "variable". Note that "gradient" can be a `Tensor`, an `IndexedSlices`, or `None` if there is no gradient for the given variable. Args: loss: A callable taking no arguments which returns the value to minimize. var_list: list or tuple of `Variable` objects to update to minimize `loss`, or a callable returning the list or tuple of `Variable` objects. Use callable when the variable list would otherwise be incomplete before `minimize` and the variables are created at the first time when `loss` is called. grad_loss: Optional. A `Tensor` holding the gradient computed for `loss`. Returns: A list of (gradient, variable) pairs. Variable is always present, but gradient can be `None`. Raises: TypeError: If `var_list` contains anything else than `Variable` objects. ValueError: If some arguments are invalid, or var_list is None. """ # TODO(josh11b): Test that we handle weight decay in a reasonable way. with backprop.GradientTape() as tape: if not callable(var_list): tape.watch(var_list) loss_value = loss() if callable(var_list): var_list = var_list() var_list = nest.flatten(var_list) grads = tape.gradient(loss_value, var_list, grad_loss) if hasattr(self, "clipnorm"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if hasattr(self, "clipvalue"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] grads_and_vars = list(zip(grads, var_list)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is not None and v.dtype != dtypes.resource ]) return grads_and_vars def get_gradients(self, loss, params): """Returns gradients of `loss` with respect to `params`. Arguments: loss: Loss tensor. params: List of variables. Returns: List of gradient tensors. Raises: ValueError: In case any gradient cannot be computed (e.g. if gradient function not implemented). """ params = nest.flatten(params) with backend.get_graph().as_default(): grads = gradients.gradients(loss, params) for grad, param in zip(grads, params): if grad is None: raise ValueError("Variable {} has `None` for gradient. " "Please make sure that all of your ops have a " "gradient defined (i.e. are differentiable). " "Common ops without gradient: " "K.argmax, K.round, K.eval.".format(param)) if hasattr(self, "clipnorm"): grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads] if hasattr(self, "clipvalue"): grads = [ clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue) for g in grads ] return grads def apply_gradients(self, grads_and_vars, name=None): """Apply gradients to variables. This is the second part of `minimize()`. It returns an `Operation` that applies gradients. Args: grads_and_vars: List of (gradient, variable) pairs. name: Optional name for the returned operation. Default to the name passed to the `Optimizer` constructor. Returns: An `Operation` that applies the specified gradients. If `global_step` was not None, that operation also increments `global_step`. Raises: TypeError: If `grads_and_vars` is malformed. ValueError: If none of the variables have gradients. """ grads_and_vars = _filter_grads(grads_and_vars) var_list = [v for (_, v) in grads_and_vars] # Create iteration if necessary. with ops.init_scope(): _ = self.iterations self._create_hypers() self._create_slots(var_list) self._prepare(var_list) return distribute_ctx.get_replica_context().merge_call( self._distributed_apply, args=(grads_and_vars,), kwargs={"name": name}) def _distributed_apply(self, distribution, grads_and_vars, name): """`apply_gradients` using a `DistributionStrategy`.""" reduced_grads = distribution.extended.batch_reduce_to( ds_reduce_util.ReduceOp.SUM, grads_and_vars) var_list = [v for _, v in grads_and_vars] grads_and_vars = zip(reduced_grads, var_list) def apply_grad_to_update_var(var, grad): """Apply gradient to variable.""" if isinstance(var, ops.Tensor): raise NotImplementedError("Trying to update a Tensor ", var) if isinstance(grad, ops.IndexedSlices): if var.constraint is not None: raise RuntimeError( "Cannot use a constraint function on a sparse variable.") return self._resource_apply_sparse_duplicate_indices( grad.values, var, grad.indices) update_op = self._resource_apply_dense(grad, var) if var.constraint is not None: with ops.control_dependencies([update_op]): return var.assign(var.constraint(var)) else: return update_op update_ops = [] with backend.name_scope(name or self._name): for grad, var in grads_and_vars: scope_name = ("" if ops.executing_eagerly_outside_functions() else "_" + var.op.name) with backend.name_scope("update" + scope_name): update_ops.extend( distribution.extended.update( var, apply_grad_to_update_var, args=(grad,), group=False)) any_symbolic = any(isinstance(i, ops.Operation) or tf_utils.is_symbolic_tensor(i) for i in update_ops) if not context.executing_eagerly() or any_symbolic: # If the current context is graph mode or any of the update ops are # symbolic then the step update should be carried out under a graph # context. (eager updates execute immediately) with ops._get_graph_from_inputs(update_ops).as_default(): # pylint: disable=protected-access with ops.control_dependencies(update_ops): return self._iterations.assign_add(1).op return self._iterations.assign_add(1) def get_updates(self, loss, params): grads = self.get_gradients(loss, params) grads_and_vars = list(zip(grads, params)) self._assert_valid_dtypes([ v for g, v in grads_and_vars if g is not None and v.dtype != dtypes.resource ]) return [self.apply_gradients(grads_and_vars)] def _set_hyper(self, name, value): """set hyper `name` to value. value can be callable, tensor, numeric.""" if isinstance(value, trackable.Trackable): self._track_trackable(value, name, overwrite=True) if name not in self._hyper: self._hyper[name] = value else: prev_value = self._hyper[name] if (callable(prev_value) or isinstance(prev_value, (ops.Tensor, int, float, learning_rate_schedule.LearningRateSchedule)) or isinstance(value, learning_rate_schedule.LearningRateSchedule)): self._hyper[name] = value else: backend.set_value(self._hyper[name], value) def _get_hyper(self, name, dtype=None): if not self._hypers_created: self._create_hypers() value = self._hyper[name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return value if callable(value): value = value() if dtype: return math_ops.cast(value, dtype) else: return value def __getattribute__(self, name): """Overridden to support hyperparameter access.""" try: return super(OptimizerV2, self).__getattribute__(name) except AttributeError as e: # Needed to avoid infinite recursion with __setattr__. if name == "_hyper": raise e # Backwards compatibility with Keras optimizers. if name == "lr": name = "learning_rate" if name in self._hyper: return self._get_hyper(name) raise e def __setattr__(self, name, value): """Override setattr to support dynamic hyperparameter setting.""" # Backwards compatibility with Keras optimizers. if name == "lr": name = "learning_rate" if hasattr(self, "_hyper") and name in self._hyper: self._set_hyper(name, value) else: super(OptimizerV2, self).__setattr__(name, value) def get_slot_names(self): """A list of names for this optimizer's slots.""" return self._slot_names def add_slot(self, var, slot_name, initializer="zeros"): """Add a new slot variable for `var`.""" if slot_name not in self._slot_names: self._slot_names.append(slot_name) var_key = _var_key(var) slot_dict = self._slots.setdefault(var_key, {}) weight = slot_dict.get(slot_name, None) if weight is None: if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) initial_value = functools.partial( initializer, shape=var.shape, dtype=var.dtype) else: initial_value = initializer strategy = distribute_ctx.get_strategy() with strategy.extended.colocate_vars_with(var): weight = tf_variables.Variable( name="%s/%s" % (var._shared_name, slot_name), # pylint: disable=protected-access dtype=var.dtype, trainable=False, initial_value=initial_value) backend.track_variable(weight) slot_dict[slot_name] = weight self._restore_slot_variable( slot_name=slot_name, variable=var, slot_variable=weight) self._weights.append(weight) return weight def get_slot(self, var, slot_name): var_key = _var_key(var) slot_dict = self._slots[var_key] return slot_dict[slot_name] def _prepare(self, var_list): pass def _create_hypers(self): if self._hypers_created: return # Iterate hyper values deterministically. for name, value in sorted(self._hyper.items()): if isinstance(value, ops.Tensor) or callable(value): continue else: self._hyper[name] = self.add_weight( name, shape=[], trainable=False, initializer=value, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._hypers_created = True @property def iterations(self): """Variable. The number of training steps this Optimizer has run.""" if self._iterations is None: self._iterations = self.add_weight( "iter", shape=[], dtype=dtypes.int64, trainable=False, aggregation=tf_variables.VariableAggregation.ONLY_FIRST_REPLICA) self._weights.append(self._iterations) return self._iterations @iterations.setter def iterations(self, variable): if self._iterations is not None: raise RuntimeError("Cannot set `iterations` to a new Variable after " "the Optimizer weights have been created") self._iterations = variable self._weights.append(self._iterations) def _decayed_lr(self, var_dtype): """Get decayed learning rate as a Tensor with dtype=var_dtype.""" lr_t = self._get_hyper("learning_rate", var_dtype) if isinstance(lr_t, learning_rate_schedule.LearningRateSchedule): local_step = math_ops.cast(self.iterations, var_dtype) lr_t = math_ops.cast(lr_t(local_step), var_dtype) if self._initial_decay > 0.: local_step = math_ops.cast(self.iterations, var_dtype) decay_t = self._get_hyper("decay", var_dtype) lr_t = lr_t / (1. + decay_t * local_step) return lr_t @abc.abstractmethod def get_config(self): """Returns the config of the optimimizer. An optimizer config is a Python dictionary (serializable) containing the configuration of an optimizer. The same optimizer can be reinstantiated later (without any saved state) from this configuration. Returns: Python dictionary. """ config = {"name": self._name} if hasattr(self, "clipnorm"): config["clipnorm"] = self.clipnorm if hasattr(self, "clipvalue"): config["clipvalue"] = self.clipvalue return config @classmethod def from_config(cls, config, custom_objects=None): """Creates an optimizer from its config. This method is the reverse of `get_config`, capable of instantiating the same optimizer from the config dictionary. Arguments: config: A Python dictionary, typically the output of get_config. custom_objects: A Python dictionary mapping names to additional Python objects used to create this optimizer, such as a function used for a hyperparameter. Returns: An optimizer instance. """ if "lr" in config: config["learning_rate"] = config.pop("lr") if "learning_rate" in config: if isinstance(config["learning_rate"], dict): config["learning_rate"] = learning_rate_schedule.deserialize( config["learning_rate"], custom_objects=custom_objects) return cls(**config) def _serialize_hyperparameter(self, hyperparameter_name): """Serialize a hyperparameter that can be a float, callable, or Tensor.""" value = self._hyper[hyperparameter_name] if isinstance(value, learning_rate_schedule.LearningRateSchedule): return learning_rate_schedule.serialize(value) if callable(value): return value() if tensor_util.is_tensor(value): return backend.get_value(value) return value def variables(self): """Returns variables of this Optimizer based on the order created.""" return self._weights @property def weights(self): """Returns variables of this Optimizer based on the order created.""" return self._weights def get_weights(self): params = self.weights return backend.batch_get_value(params) # TODO(tanzheny): Maybe share this logic with base_layer. def set_weights(self, weights): params = self.weights if len(params) != len(weights): raise ValueError( "You called `set_weights(weights)` on optimizer " + self._name + " with a weight list of length " + str(len(weights)) + ", but the optimizer was expecting " + str(len(params)) + " weights. Provided weights: " + str(weights)[:50] + "...") if not params: return weight_value_tuples = [] param_values = backend.batch_get_value(params) for pv, p, w in zip(param_values, params, weights): if pv.shape != w.shape: raise ValueError("Optimizer weight shape " + str(pv.shape) + " not compatible with " "provided weight shape " + str(w.shape)) weight_value_tuples.append((p, w)) backend.batch_set_value(weight_value_tuples) def add_weight(self, name, shape, dtype=None, initializer="zeros", trainable=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE): if dtype is None: dtype = dtypes.float32 if isinstance(initializer, six.string_types) or callable(initializer): initializer = initializers.get(initializer) if synchronization == tf_variables.VariableSynchronization.ON_READ: if trainable: raise ValueError( "Synchronization value can be set to " "VariableSynchronization.ON_READ only for non-trainable variables. " "You have specified trainable=True and " "synchronization=VariableSynchronization.ON_READ.") else: # Set trainable to be false when variable is to be synced on read. trainable = False elif trainable is None: trainable = True variable = self._add_variable_with_custom_getter( name=name, shape=shape, getter=base_layer_utils.make_variable, overwrite=True, initializer=initializer, dtype=dtype, trainable=trainable, use_resource=True, synchronization=synchronization, aggregation=aggregation) backend.track_variable(variable) return variable def _assert_valid_dtypes(self, tensors): """Asserts tensors are all valid types (see `_valid_dtypes`). Args: tensors: Tensors to check. Raises: ValueError: If any tensor is not a valid type. """ valid_dtypes = self._valid_dtypes() for t in tensors: dtype = t.dtype.base_dtype if dtype not in valid_dtypes: raise ValueError("Invalid type %r for %s, expected: %s." % (dtype, t.name, [v for v in valid_dtypes])) def _valid_dtypes(self): """Valid types for loss, variables and gradients. Subclasses should override to allow other float types. Returns: Valid types for loss, variables and gradients. """ return set( [dtypes.float16, dtypes.bfloat16, dtypes.float32, dtypes.float64]) def _call_if_callable(self, param): """Call the function if param is callable.""" return param() if callable(param) else param def _resource_apply_dense(self, grad, handle): """Add ops to apply dense gradients to the variable `handle`. Args: grad: a `Tensor` representing the gradient. handle: a `Tensor` of dtype `resource` which points to the variable to be updated. Returns: An `Operation` which updates the value of the variable. """ raise NotImplementedError() def _resource_apply_sparse_duplicate_indices(self, grad, handle, indices): """Add ops to apply sparse gradients to `handle`, with repeated indices. Optimizers which override this method must deal with repeated indices. See the docstring of `_apply_sparse_duplicate_indices` for details. By default the correct behavior, to sum non-unique indices and their associated gradients, is enforced by first pre-processing `grad` and `indices` and passing them on to `_resource_apply_sparse`. Optimizers which deal correctly with duplicate indices may instead override this method to avoid the overhead of summing. Args: grad: a `Tensor` representing the gradient for the affected indices. handle: a `Tensor` of dtype `resource` which points to the variable to be updated. indices: a `Tensor` of integral type representing the indices for which the gradient is nonzero. Indices may be repeated. Returns: An `Operation` which updates the value of the variable. """ summed_grad, unique_indices = _deduplicate_indexed_slices( values=grad, indices=indices) return self._resource_apply_sparse(summed_grad, handle, unique_indices) def _resource_apply_sparse(self, grad, handle, indices): """Add ops to apply sparse gradients to the variable `handle`. Similar to `_apply_sparse`, the `indices` argument to this method has been de-duplicated. Optimizers which deal correctly with non-unique indices may instead override `_resource_apply_sparse_duplicate_indices` to avoid this overhead. Args: grad: a `Tensor` representing the gradient for the affected indices. handle: a `Tensor` of dtype `resource` which points to the variable to be updated. indices: a `Tensor` of integral type representing the indices for which the gradient is nonzero. Indices are unique. Returns: An `Operation` which updates the value of the variable. """ raise NotImplementedError() def _resource_scatter_add(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_add(x.handle, i, v)]): return x.value() def _resource_scatter_update(self, x, i, v): with ops.control_dependencies( [resource_variable_ops.resource_scatter_update(x.handle, i, v)]): return x.value() # --------------- # For implementing the trackable interface # --------------- def _restore_slot_variable(self, slot_name, variable, slot_variable): """Restore a newly created slot variable's value.""" variable_key = _var_key(variable) deferred_restorations = self._deferred_slot_restorations.get( slot_name, {}).pop(variable_key, []) # Iterate over restores, highest restore UID first to minimize the number # of assignments. deferred_restorations.sort(key=lambda position: position.restore_uid, reverse=True) for checkpoint_position in deferred_restorations: checkpoint_position.restore(slot_variable) def _create_or_restore_slot_variable( self, slot_variable_position, slot_name, variable): """Restore a slot variable's value, possibly creating it. Called when a variable which has an associated slot variable is created or restored. When executing eagerly, we create the slot variable with a restoring initializer. No new variables are created when graph building. Instead, _restore_slot_variable catches these after normal creation and adds restore ops to the graph. This method is nonetheless important when graph building for the case when a slot variable has already been created but `variable` has just been added to a dependency graph (causing us to realize that the slot variable needs to be restored). Args: slot_variable_position: A `trackable._CheckpointPosition` object indicating the slot variable `Trackable` object to be restored. slot_name: The name of this `Optimizer`'s slot to restore into. variable: The variable object this slot is being created for. """ variable_key = _var_key(variable) slot_dict = self._slots.get(variable_key, {}) slot_variable = slot_dict.get(slot_name, None) if (slot_variable is None and context.executing_eagerly() and slot_variable_position.is_simple_variable() # Defer slot variable creation if there is an active variable creator # scope. Generally we'd like to eagerly create/restore slot variables # when possible, but this may mean that scopes intended to catch # `variable` also catch its eagerly created slot variable # unintentionally (specifically make_template would add a dependency on # a slot variable if not for this case). Deferring is mostly harmless # (aside from double initialization), and makes variable creator scopes # behave the same way they do when graph building. and not ops.get_default_graph()._variable_creator_stack): # pylint: disable=protected-access initializer = trackable.CheckpointInitialValue( checkpoint_position=slot_variable_position) slot_variable = self.add_slot( var=variable, initializer=initializer, slot_name=slot_name) # Slot variables are not owned by any one object (because we don't want to # save the slot variable if the optimizer is saved without the non-slot # variable, or if the non-slot variable is saved without the optimizer; # it's a dependency hypergraph with edges of the form (optimizer, non-slot # variable, variable)). So we don't _track_ slot variables anywhere, and # instead special-case this dependency and otherwise pretend it's a normal # graph. if slot_variable is not None: # If we've either made this slot variable, or if we've pulled out an # existing slot variable, we should restore it. slot_variable_position.restore(slot_variable) else: # We didn't make the slot variable. Defer restoring until it gets created # normally. We keep a list rather than the one with the highest restore # UID in case slot variables have their own dependencies, in which case # those could differ between restores. self._deferred_slot_restorations.setdefault( slot_name, {}).setdefault(variable_key, []).append( slot_variable_position) def _filter_grads(grads_and_vars): """Filter out iterable with grad equal to None.""" grads_and_vars = tuple(grads_and_vars) if not grads_and_vars: return grads_and_vars filtered = [] vars_with_empty_grads = [] for grad, var in grads_and_vars: if grad is None: vars_with_empty_grads.append(var) else: filtered.append((grad, var)) filtered = tuple(filtered) if not filtered: raise ValueError("No gradients provided for any variable: %s." % ([v.name for _, v in grads_and_vars],)) if vars_with_empty_grads: logging.warning( ("Gradients does not exist for variables %s when minimizing the loss."), ([v.name for v in vars_with_empty_grads])) return filtered def _var_key(var): """Key for representing a primary variable, for looking up slots. In graph mode the name is derived from the var shared name. In eager mode the name is derived from the var unique id. If distribution strategy exists, get the primary variable first. Args: var: the variable. Returns: the unique name of the variable. """ # pylint: disable=protected-access # Get the distributed variable if it exists. if getattr(var, "_distributed_container", None) is not None: var = var._distributed_container() if var._in_graph_mode: return var._shared_name return var._unique_id def _get_slot_key_from_var(var, slot_name): """Get the slot key for the variable: var_name/slot_name.""" name = _var_key(var) return name + "/" + slot_name class _RestoredOptimizer(OptimizerV2): """A non-functional Optimizer implementation for checkpoint compatibility. Holds slot variables and hyperparameters when an optimizer is restored from a SavedModel. These variables may be referenced in functions along with ops created by the original optimizer, but currently we do not support using the optimizer object iself (e.g. through `apply_gradients`). """ # TODO(allenl): Make the restored optimizer functional by tracing its apply # methods. def __init__(self): super(_RestoredOptimizer, self).__init__("_RestoredOptimizer") self._hypers_created = True def get_config(self): # TODO(allenl): Save and restore the Optimizer's config raise NotImplementedError( "Restoring functional Optimzers from SavedModels is not currently " "supported. Please file a feature request if this limitation bothers " "you.") revived_types.register_revived_type( "optimizer", lambda obj: isinstance(obj, OptimizerV2), versions=[revived_types.VersionedTypeRegistration( object_factory=lambda proto: _RestoredOptimizer(), version=1, min_producer_version=1, min_consumer_version=1, setter=_RestoredOptimizer._set_hyper # pylint: disable=protected-access )])

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null

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0adb9e87674ba38043bf368fb738d4c5e8ba7c5c

362

py

Python

escola/teste_get.py

danielrosendos/djangoRestFramework

946bb95b8dd9976d1920302ce724572ffd9f98cf

[ "MIT" ]

2

2020-07-26T15:17:23.000Z

2020-07-26T16:50:18.000Z

escola/teste_get.py

sport129/djangoRestFramework

946bb95b8dd9976d1920302ce724572ffd9f98cf

[ "MIT" ]

3

2021-03-30T14:12:18.000Z

2021-06-04T23:44:47.000Z

escola/teste_get.py

sport129/djangoRestFramework

946bb95b8dd9976d1920302ce724572ffd9f98cf

[ "MIT" ]

null

import requests headers = { 'content-type': 'application/json', 'Authorization': 'Token 80ca9f249b80e7226cdc7fcaada8d7297352f0f9' } url_base_cursos = 'http://127.0.0.1:8000/api/v2/cursos' url_base_avaliacoes = 'http://127.0.0.1:8000/api/v2/avaliacoes' resultado = requests.get(url=url_base_cursos, headers=headers) assert resultado.status_code == 200

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0.111111

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null

0

null

0

1

0

0adc55ed2f06787ab63a1224266a2dd707ce1b10

6,455

py

Python

python/avi/sdk/utils/waf_policy/vdi_waf_policy.py

aaronjwood/alb-sdk

ae4c47b2228651d3f5095e7c14f081aa4adbb732

[ "Apache-2.0" ]

null

python/avi/sdk/utils/waf_policy/vdi_waf_policy.py

aaronjwood/alb-sdk

ae4c47b2228651d3f5095e7c14f081aa4adbb732

[ "Apache-2.0" ]

null

python/avi/sdk/utils/waf_policy/vdi_waf_policy.py

aaronjwood/alb-sdk

ae4c47b2228651d3f5095e7c14f081aa4adbb732

[ "Apache-2.0" ]

null

# Copyright 2021 VMware, Inc. import argparse import json import re import logging import os import sys from avi.sdk.avi_api import ApiSession API_VERSION = "18.2.13" SYSTEM_WAF_POLICY_VDI='System-WAF-Policy-VDI' logger = logging.getLogger(__name__) def add_allowlist_rule(waf_policy_obj): #add a allowlist rule to allow request with uri beginning with /ice/ allowlist_rule={ "index": 0, "name": "allowlist-start-with-ice", "description": "WAF will buffer the whole request body first and then release to backend. With VDI, client wants to stream data between client and server for some URLs like /ice/..., we should allow these URLs", "actions": [ "WAF_POLICY_WHITELIST_ACTION_ALLOW" ], "match": { "path": { "match_case": "SENSITIVE", "match_str": [ "/ice/" ], "match_criteria": "BEGINS_WITH" } } } index = 0 waf_policy_obj.setdefault("whitelist", {}).setdefault("rules", []) for rule in waf_policy_obj["whitelist"]["rules"][:]: if rule["name"] == "allowlist-start-with-ice": waf_policy_obj["whitelist"]["rules"].remove(rule) if rule["index"]>index: index = rule["index"] allowlist_rule["index"] = index+1 waf_policy_obj["whitelist"]["rules"].append(allowlist_rule) def get_id_from_group(group): pattern = re.compile("[^\d]*(?P<group_id>\d\d\d)") match = pattern.match(group["name"]) assert match, "can not extract group id from group '{}'".format(group["name"]) groupid = int(match.group("group_id")) assert groupid == 0 or 100 <= groupid <= 999, "group id for group '{}' not in expected range".format(group["name"]) return groupid def disable_crs_response_rules(waf_policy_obj): #disable response side rules and some specific rules for crs_group in waf_policy_obj.get("crs_groups", []): group_id = get_id_from_group(crs_group) if group_id >= 950: crs_group["enable"] = False for rule in crs_group.get("rules", []): if rule["rule_id"] == "920330" or rule["rule_id"] == "932105": rule["enable"] = False def add_pre_crs_group(waf_policy_obj): #add a rule to parse body as xml for requests with /broker/xml uri xml_rule = [ { "index": 0, "name": "enforce XML parsing for /broker/xml", "description": "Clients often send the wrong Content-Type header. We ignore the header and enforce the request body to be parsed as XML in WAF", "rule": "SecRule REQUEST_METHOD \"@streq POST\" \"phase:1,id:4099822,t:none,nolog,pass,chain\" \n SecRule REQUEST_URI \"@streq /broker/xml\" \"t:none,ctl:requestBodyProcessor=XML\"" } ] pre_crs_group = { "index": 0, "name": "VDI_409_ENFORCE_XML", "rules": xml_rule } index = 0 if "pre_crs_groups" not in waf_policy_obj: waf_policy_obj["pre_crs_groups"] = list() for pre_crs in waf_policy_obj["pre_crs_groups"]: if pre_crs["name"] == "VDI_409_ENFORCE_XML": pre_crs["rules"] = xml_rule pre_crs["enable"] = True return if pre_crs["index"] > index: index = pre_crs["index"] pre_crs_group["index"] = index + 1 waf_policy_obj["pre_crs_groups"].append(pre_crs_group) def get_crs(api): tested_crs = "CRS-2021-1" resp = api.get("wafcrs?name=" + tested_crs) if resp.status_code not in range(200, 300): if resp.status_code == 404: logger.error("Controller does not have CRS %s, please install first." % tested_crs) return None logger.error('Error : %s', resp.text) exit(0) waf_crs = json.loads(resp.text)["results"] return waf_crs[0] def create_vdi_waf_policy(api, args): waf_policy_obj = { "name": SYSTEM_WAF_POLICY_VDI, "mode": "WAF_MODE_DETECTION_ONLY", "waf_profile_ref": "/api/wafprofile?name=System-WAF-Profile" } waf_crs = get_crs(api) if waf_crs is None: return waf_policy_obj["waf_crs_ref"]="/api/wafcrs?name="+waf_crs["name"] waf_policy_obj["crs_groups"] = list() for group in waf_crs["groups"]: waf_policy_obj["crs_groups"].append(group) add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.post('wafpolicy', data=json.dumps(waf_policy_obj)) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text) def update_waf_policy(api, args, waf_policy_obj): add_allowlist_rule(waf_policy_obj) disable_crs_response_rules(waf_policy_obj) add_pre_crs_group(waf_policy_obj) resp = api.put('wafpolicy/%s' %waf_policy_obj['uuid'], data=waf_policy_obj) if resp.status_code in range(200, 300): logger.debug('Create WAF policy successfully') else: logger.error('Error : %s' % resp.text) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('-u', '--user', action="store", help='controller user', default='admin') parser.add_argument('-p', '--password', action="store", help='controller user password', default='admin') parser.add_argument('-t', '--tenant', action="store", help='tenant name', default='admin') parser.add_argument('-a', '--authtoken', help='Authentication token') parser.add_argument('-c', '--controller_ip', action="store", help='controller ip') args = parser.parse_args() if args.password: api = ApiSession.get_session(args.controller_ip, args.user, args.password, tenant=args.tenant, api_version=API_VERSION) elif args.authtoken: api = ApiSession.get_session(args.controller_ip, args.user, tenant=args.tenant, token=args.authtoken, api_version=API_VERSION) else: logging.error("Either password or authtokentoken must be provided.") sys.exit(1) waf_policy_obj = api.get_object_by_name('wafpolicy', SYSTEM_WAF_POLICY_VDI) if not waf_policy_obj: create_vdi_waf_policy(api, args) else: update_waf_policy(api, args, waf_policy_obj)

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null

0

null

0

1

0

0adcde8b96a5cb82b17bdf29ba072f1b54339883

4,101

py

Python

src/api/bkuser_core/tests/bkiam/test_constants.py

Chace-wang/bk-user

057f270d66a1834312306c9fba1f4e95521f10b1

[ "MIT" ]

null

src/api/bkuser_core/tests/bkiam/test_constants.py

Chace-wang/bk-user

057f270d66a1834312306c9fba1f4e95521f10b1

[ "MIT" ]

null

src/api/bkuser_core/tests/bkiam/test_constants.py

Chace-wang/bk-user

057f270d66a1834312306c9fba1f4e95521f10b1

[ "MIT" ]

1

2021-12-31T06:48:41.000Z

# -*- coding: utf-8 -*- """ TencentBlueKing is pleased to support the open source community by making 蓝鲸智云-用户管理(Bk-User) available. Copyright (C) 2017-2021 THL A29 Limited, a Tencent company. All rights reserved. Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://opensource.org/licenses/MIT Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import pytest from bkuser_core.bkiam.constants import ResourceType from bkuser_core.categories.models import Department, ProfileCategory from bkuser_core.tests.utils import make_simple_department pytestmark = pytest.mark.django_db class TestResourceTypeEnum: @pytest.mark.parametrize( "is_leaf, path, f, v", [ (True, "/category,5/department,3440/department,3443/", "parent_id", 3443), (False, "/category,5/department,3440/department,3443/", "id", 3443), (True, "/category,5/", "category_id", 5), (False, "/category,5/", "category_id", 5), (True, "/department,3440/department,3443/", "parent_id", 3443), (False, "/department,3440/department,3443/", "id", 3443), ], ) def test_get_key_mapping(self, is_leaf, path, f, v): key_mapping = ResourceType.get_key_mapping(ResourceType.DEPARTMENT) path_method = key_mapping["department._bk_iam_path_"] data = {"value": path} if not is_leaf: data["node_type"] = "non-leaf" f, v = path_method(data) assert f == f assert v == v @pytest.mark.parametrize( "dep_chain, expected", [ ( [1000, 1001, 1002], {"_bk_iam_path_": "/category,1/department,1000/department,1001/department,1002/"}, ), ( [1000], {"_bk_iam_path_": "/category,1/department,1000/"}, ), ], ) def test_get_attributes_mapping(self, dep_chain, expected): target_parent = None for d in dep_chain: parent_id = target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(str(d), force_create_params={"id": d}, parent_id=parent_id) attributes_mapping = ResourceType.get_attributes_mapping(target_parent) assert attributes_mapping == expected def test_get_attributes_mapping_other(self): pc = ProfileCategory.objects.get_default() attributes_mapping = ResourceType.get_attributes_mapping(pc) assert attributes_mapping == {} @pytest.mark.parametrize( "dep_chain,expected", [ ( ["a", "b", "c"], [ ("category", "默认目录"), ("department", "a"), ("department", "b"), ("department", "c"), ], ), ( ["a", "b"], [("category", "默认目录"), ("department", "a"), ("department", "b")], ), ], ) def test_get_resource_nodes_dep(self, dep_chain, expected): target_parent = None for d in dep_chain: parent_id = target_parent if not target_parent else target_parent.pk target_parent = make_simple_department(d, parent_id=parent_id) # 只添加 parent，mptt 树需要重建 Department.tree_objects.rebuild() nodes = ResourceType.get_instance_resource_nodes(target_parent) assert [(x["type"], x["name"]) for x in nodes] == expected def test_get_resource_nodes_other(self): pc = ProfileCategory.objects.get_default() nodes = ResourceType.get_instance_resource_nodes(pc) assert [(x["type"], x["name"]) for x in nodes] == [("category", "默认目录")]

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0.613997

477

4,101

5.075472

0.348008

0.05948

0.020653

0.046262

0.484924

0.403139

0.234614

0.172656

0.135481

0.113176

0

0.033077

0.270178

4,101

103

116

39.815534

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0.176298

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0

0.163847

0.078955

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0.075

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0.0625

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0.125

0

null

0

null

0

1

0

0adf4b5bea842a306db59cff9711a1e6a19b7ae0

3,753

py

Python

improver_tests/precipitation_type/test_utilities.py

cpelley/improver

ebf77fe2adc85ed7aec74c26671872a2e4388ded

[ "BSD-3-Clause" ]

77

2017-04-26T07:47:40.000Z

2022-03-31T09:40:49.000Z

improver_tests/precipitation_type/test_utilities.py

cpelley/improver

ebf77fe2adc85ed7aec74c26671872a2e4388ded

[ "BSD-3-Clause" ]

1,440

2017-03-29T10:04:15.000Z

2022-03-28T10:11:29.000Z

improver_tests/precipitation_type/test_utilities.py

MoseleyS/improver

ca028e3a1c842e3ff00b188c8ea6eaedd0a07149

[ "BSD-3-Clause" ]

72

2017-03-17T16:53:45.000Z

2022-02-16T09:41:37.000Z

# -*- coding: utf-8 -*- # ----------------------------------------------------------------------------- # (C) British Crown Copyright 2017-2021 Met Office. # 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 the copyright holder nor the names of its # contributors 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 HOLDER 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. """ Tests of precipitation_type utilities""" import numpy as np import pytest from iris.exceptions import CoordinateNotFoundError from improver.metadata.constants import FLOAT_DTYPE from improver.precipitation_type.utilities import make_shower_condition_cube from improver.synthetic_data.set_up_test_cubes import set_up_probability_cube def set_up_test_cube(n_thresholds=1): """Set up a cube testing shower condition conversion""" thresholds = np.arange(n_thresholds) shape = [2, 2] shape = [n_thresholds, *shape] if n_thresholds > 0 else shape data = np.ones(shape, dtype=FLOAT_DTYPE) cube = set_up_probability_cube( data, thresholds, variable_name="texture_of_cloud_area_fraction", threshold_units=1, spatial_grid="equalarea", ) return cube def test_basic(): """Test that with a valid input the cube is transformed into a shower condition cube.""" cube = set_up_test_cube() result = make_shower_condition_cube(cube) threshold_coord = result.coord(var_name="threshold") assert result.name() == "probability_of_shower_condition_above_threshold" assert result.dtype == FLOAT_DTYPE assert (result.data == cube.data).all() assert threshold_coord.name() == "shower_condition" assert threshold_coord.units == 1 def test_no_threshold_coord(): """Test an exception is raised if the proxy diagnostic cube does not have a threshold coordinate.""" cube = set_up_test_cube() cube.remove_coord("texture_of_cloud_area_fraction") expected = "Input has no threshold coordinate and cannot be used" with pytest.raises(CoordinateNotFoundError, match=expected): make_shower_condition_cube(cube) def test_multi_valued_threshold_coord(): """Test an exception is raised if the proxy diagnostic cube has a multi valued threshold coordinate.""" cube = set_up_test_cube(n_thresholds=2) expected = "Expected a single valued threshold coordinate.*" with pytest.raises(ValueError, match=expected): make_shower_condition_cube(cube)

39.925532

79

0.742073

507

3,753

5.34714

0.402367

0.044264

0.035042

0.033936

0.217263

0.164146

0.151236

0.095168

0

0.005146

0.171596

3,753

93

80

40.354839

0.866838

0.523848

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0.139616

0.062245

0

0.128205

1

0.102564

false

0

0.153846

0

0.282051

0

null

0

null

0

1

0

0ae04a483b4283bc6fdc84bd651d77ab70b6120c

5,149

py

Python

app/api/v1/models/items.py

bryan-munene/Store-Manager-DB

40b24039189aea6854d7fcf33ccb648bb6642231

[ "MIT" ]

null

app/api/v1/models/items.py

bryan-munene/Store-Manager-DB

40b24039189aea6854d7fcf33ccb648bb6642231

[ "MIT" ]

4

2018-10-25T00:57:18.000Z

2018-10-25T21:29:09.000Z

app/api/v1/models/items.py

bryan-munene/Store-Manager-DB

40b24039189aea6854d7fcf33ccb648bb6642231

[ "MIT" ]

null

from .db_conn import ModelSetup class ItemsModel(ModelSetup): '''Handles the data logic of the items section''' def __init__( self, name=None, price=None, quantity=None, category_id=None, reorder_point=None, auth=None): '''Initializes the variables for the items class''' self.name = name self.price = price self.quantity = quantity self.category_id = category_id self.reorder_point = reorder_point self.auth = auth def add_item( self, name, price, quantity, image, category_id, reorder_point, auth): '''Adds item given the above arguements. Then returns the created item''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = """INSERT INTO items(name, price, quantity, image, category, reorder_point, created_by)\ VALUES(%s,%s,%s,%s,%s,%s,%s);""" self.cur.execute( query, (name, price, quantity, image, category_id, reorder_point, auth)) self.conn.commit() query_confirm = """SELECT * FROM items WHERE name = %s AND price = %s;""" self.cur.execute(query_confirm, (name, price)) self.item = self.cur.fetchone() return self.item def get_all(self): '''gets all records of items in the databas and returns them''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = """SELECT * FROM items;""" self.cur.execute(query) self.items = self.cur.fetchall() return self.items def get_by_id(self, item_id): '''retrieves one item by finding them using their unique item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = """SELECT * FROM items WHERE item_id = %s;""" self.cur.execute(query, (item_id, )) self.item = self.cur.fetchone() return self.item def get_by_category(self, category): '''retrieves items by finding them using their category. all items in the same category are retrieved''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = """SELECT * FROM items WHERE category LIKE %s;""" self.cur.execute(query, (category)) self.item = self.cur.fetchall() return self.item def get_by_name_and_price(self, name, price): '''retrieves one item by finding them using their unique unique combination''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = """SELECT * FROM items WHERE name LIKE %s AND price = %s;""" self.cur.execute(query, (name, price)) self.item = self.cur.fetchone() return self.item def update_item( self, item_id, price, quantity, image, category_id, reorder_point, auth): '''updates item's details. the values in the db are changed to what is provided''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = """UPDATE items SET price = %s, quantity = %s, image = %s, category = %s, reorder_point = %s, created_by = %s WHERE item_id= %s """ self.cur.execute( query, (price, quantity, image, category_id, reorder_point, auth, item_id)) self.conn.commit() query_confirm = """SELECT * FROM items WHERE item_id = %s;""" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def update_item_quantity(self, item_id, quantity): '''updates item's quantity.adds the quantity added to the quantity available''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = """UPDATE items SET quantity = %s WHERE item_id= %s """ self.cur.execute(query, (quantity, item_id)) self.conn.commit() query_confirm = """SELECT * FROM items WHERE item_id = %s;""" self.cur.execute(query_confirm, (item_id, )) self.item = self.cur.fetchone() return self.item def delete_item(self, item_id): '''deletes an item by finding them using the item_id''' model = ModelSetup() self.conn = model.conn self.cur = model.cur query = """DELETE FROM items WHERE item_id = %s""" self.cur.execute(query, (item_id, )) self.conn.commit() query_confirm = """SELECT * FROM items;""" self.cur.execute(query_confirm) self.items = self.cur.fetchall() return self.items

31.206061

112

0.543212

595

5,149

4.594958

0.147899

0.07169

0.061448

0.083394

0.664228

0.618508

0.604974

0.58376

0.501829

0.393563

0

0.350554

5,149

164

113

31.396341

0.817584

0.126238

0

0.633588

0

0.007634

0.168615

0.00652

0

1

0.068702

false

0

0.007634

0

0.145038

0

null

0

null

0

1

0

0ae122f08d00736fbd1d09356f366ff9dcd6baf8

4,215

py

Python

site/src/sphinx/_extensions/api.py

linxGnu/armeria

7f4b10e66acc377dd16929157aeb417b729ce55a

[ "Apache-2.0" ]

null

site/src/sphinx/_extensions/api.py

linxGnu/armeria

7f4b10e66acc377dd16929157aeb417b729ce55a

[ "Apache-2.0" ]

null

site/src/sphinx/_extensions/api.py

linxGnu/armeria

7f4b10e66acc377dd16929157aeb417b729ce55a

[ "Apache-2.0" ]

null

from docutils.parsers.rst.roles import register_canonical_role, set_classes from docutils.parsers.rst import directives from docutils import nodes from sphinx.writers.html import HTMLTranslator from sphinx.errors import ExtensionError import os import re def api_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(False, role, rawtext, text, lineno, inliner, options, content) def apiplural_role(role, rawtext, text, lineno, inliner, options={}, content=[]): return api_role_internal(True, role, rawtext, text, lineno, inliner, options, content) def api_role_internal(plural, role, rawtext, text, lineno, inliner, options, content): set_classes(options) classes = ['code', 'api-reference'] if 'classes' in options: classes.extend(options['classes']) node = nodes.literal(rawtext, text, classes=classes, api_reference=True, is_plural=plural) return [node], [] def api_visit_literal(self, node, next_visitor): if 'api_reference' not in node.attributes: return next_visitor(self, node) env = self.builder.env javadoc_dir = os.path.abspath(env.config['javadoc_dir']) # Build the mappings from a simple class name to its Javadoc file. if not hasattr(env, '__javadoc_cache__'): env.__javadoc_mappings__ = javadoc_mappings = {} for dirname, subdirs, files in os.walk(javadoc_dir): for basename in files: if re.match(r'^[^A-Z]', basename) or not basename.endswith('.html'): # Ignore the non-class files. We rely on the simple assumption that # a class name always starts with an upper-case English alphabet. continue simple_class_name = basename[:-5].replace('.', '$') javadoc_mappings[simple_class_name] = os.path.relpath(dirname, javadoc_dir) \ .replace(os.sep, '/') + '/' + basename else: javadoc_mappings = env.__javadoc_mappings__ text = node.astext() if text.startswith('@'): text = text[1:] is_annotation = True else: is_annotation = False if text.find('.') != -1: # FQCN or package name. if re.fullmatch(r'^[^A-Z$]+$', text): # Package uri = text.replace('.', '/') + '/package-summary.html' else: # Class uri = text.replace('.', '/').replace('$', '.') + '.html' text = re.sub(r'^.*\.', '', text).replace('$', '.') else: # Simple class name; find from the pre-calculated mappings. if text not in javadoc_mappings: raise ExtensionError('Cannot find a class from Javadoc: ' + text) uri = javadoc_mappings[text] text = text.replace('$', '.') # Prepend the frame index.html path. uri = os.path.relpath(javadoc_dir, env.app.outdir).replace(os.sep, '/') + '/index.html?' + uri # Prepend the '@' back again if necessary if is_annotation: text = '@' + text # Emit the tags. self.body.append(self.starttag(node, 'code', suffix='', CLASS='docutils literal javadoc')) self.body.append(self.starttag(node, 'a', suffix='', CLASS='reference external javadoc', HREF=uri)) self.body.append(text + '</a>') # Append a plural suffix. if node.attributes['is_plural']: self.body.append(self.starttag(node, 'span', suffix='', CLASS='plural-suffix')) if re.fullmatch(r'^.*(ch|s|sh|x|z)$', text): self.body.append('es') else: self.body.append('s') self.body.append('</span>') self.body.append('</code>') raise nodes.SkipNode def setup(app): app.add_config_value('javadoc_dir', os.path.join(app.outdir, 'apidocs'), 'html') # Register the 'javadoc' role. api_role.options = {'class': directives.class_option} register_canonical_role('api', api_role) register_canonical_role('apiplural', apiplural_role) # Intercept the rendering of HTML literals. old_visitor = HTMLTranslator.visit_literal HTMLTranslator.visit_literal = lambda self, node: api_visit_literal(self, node, old_visitor) pass

37.633929

103

0.629656

517

4,215

5

0.29207

0.024758

0.043327

0.040619

0.155513

0.137718

0.102901

0.086654

0.051838

0

0.000931

0.235113

4,215

111

104

37.972973

0.800868

0.112218

0

0.067568

0

0.093079

0.005633

0

1

0.067568

false

0.013514

0.094595

0.027027

0.216216

0

null

0

null

0

1

0

0ae22c03054218a911ddc84125341497677c75ac

2,045

py

Python

ros_buildfarm/debian_repo.py

j-rivero/ros_buildfarm

840d2dc1dd5db00d5407da4644cd2bcbc5e0ac88

[ "Apache-2.0" ]

null

ros_buildfarm/debian_repo.py

j-rivero/ros_buildfarm

840d2dc1dd5db00d5407da4644cd2bcbc5e0ac88

[ "Apache-2.0" ]

1

2019-12-12T21:08:01.000Z

ros_buildfarm/debian_repo.py

j-rivero/ros_buildfarm

840d2dc1dd5db00d5407da4644cd2bcbc5e0ac88

[ "Apache-2.0" ]

null

# Copyright 2014 Open Source Robotics Foundation, Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import logging import os from .common import PlatformPackageDescriptor from .http_cache import fetch_and_cache_gzip def get_debian_repo_index(debian_repository_baseurl, target, cache_dir): url = os.path.join( debian_repository_baseurl, 'dists', target.os_code_name, 'main') if target.arch == 'source': url = os.path.join(url, 'source', 'Sources.gz') else: url = os.path.join(url, 'binary-%s' % target.arch, 'Packages.gz') cache_filename = fetch_and_cache_gzip(url, cache_dir) logging.debug('Reading file: %s' % cache_filename) # split package blocks with open(cache_filename, 'rb') as f: blocks = f.read().decode('utf8').split('\n\n') blocks = [b.splitlines() for b in blocks if b] # extract version number of every package package_versions = {} for lines in blocks: prefix = 'Package: ' assert lines[0].startswith(prefix) debian_pkg_name = lines[0][len(prefix):] prefix = 'Version: ' versions = [l[len(prefix):] for l in lines if l.startswith(prefix)] version = versions[0] if len(versions) == 1 else None prefix = 'Source: ' source_names = [l[len(prefix):] for l in lines if l.startswith(prefix)] source_name = source_names[0] if len(source_names) == 1 else None package_versions[debian_pkg_name] = PlatformPackageDescriptor(version, source_name) return package_versions

36.517857

91

0.695355

288

2,045

4.822917

0.444444

0.043197

0.019438

0.028078

0.080634

0.057595

0

0.009208

0.203423

2,045

55

92

37.181818

0.843462

0.310513

0

0.073888

0

0.034483

1

0.034483

false

0

0.137931

0

0.206897

0

null

0

null

0

1

0

0ae2b8b9a2e89b056cf58f74862944546c4ef4a9

48,440

py

Python

Framwork-Backpropagation/utils/utils_v2.py

ConvolutedDog/Implicit-Im2col-for-Backpropagation

529a62f52903326b9289091b7d0abb45e6c7bb31

[ "Apache-2.0" ]

null

Framwork-Backpropagation/utils/utils_v2.py

ConvolutedDog/Implicit-Im2col-for-Backpropagation

529a62f52903326b9289091b7d0abb45e6c7bb31

[ "Apache-2.0" ]

null

Framwork-Backpropagation/utils/utils_v2.py

ConvolutedDog/Implicit-Im2col-for-Backpropagation

529a62f52903326b9289091b7d0abb45e6c7bb31

[ "Apache-2.0" ]

null

# Copyright 2022 ConvolutedDog (https://github.com/ConvolutedDog/) # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. #!/usr/bin/python3 import torch import torch.nn as nn import torch.nn.functional as F from graphviz import Digraph, render from torch.autograd import Variable @torch.no_grad() def cross_entropy_loss(y_predict, y_true): print('\n=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' Start ===========================') print('# y_predict.shape: ', list(y_predict.shape)) print('# y_true.shape: ', list(y_true.shape)) y_shift = torch.sub(y_predict, torch.max(y_predict, dim=1, keepdim=True).values) y_exp = torch.exp(y_shift) y_probability = torch.div(y_exp, torch.sum(y_exp, dim=1, keepdim=True)) ypred_loss = torch.mean(-torch.sum(torch.mul(y_true, torch.log(y_probability)), dim=1, keepdim=True)) dLoss_dypred = y_probability - y_true print('# dLoss_dypred.shape: ', list(dLoss_dypred.shape)) print('# Self calculated loss: ', ypred_loss.item()) print('=========================== Layer:'+' {0:18}'.format('cross_entropy_loss')+' End =============================') return ypred_loss, dLoss_dypred @torch.no_grad() def fc_backward(dLoss_dnextz, z, w): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(w.shape)) print('# bias.shape: ', '['+str(dLoss_dnextz.shape[1])+']') N = z.shape[0] if len(z.shape) == 4: z = z.view(z.size(0), -1) dLoss_dz = torch.matmul(dLoss_dnextz, w) #delta dLoss_dfcW = torch.matmul(dLoss_dnextz.t(), z) dLoss_dfcB = torch.sum(dLoss_dnextz, dim=0) print('# dz.shape: ', list(dLoss_dz.shape)) print('# dweight.shape: ', list(dLoss_dfcW.shape)) print('# dbias.shape: ', list(dLoss_dfcB.shape)) return dLoss_dz, dLoss_dfcW/N, dLoss_dfcB/N @torch.no_grad() def view_backward(dLoss_dnextz, last_z, params): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) print('# last_z.shape: ', list(last_z.shape)) if params: pooling = params[0] stride = params[1] padding = params[2] output_size = (int((last_z.shape[2]-pooling[0]+2*padding[0])/stride[0]+1), \ int((last_z.shape[3]-pooling[0]+2*padding[0])/stride[0]+1)) dLoss_dz = dLoss_dnextz.reshape(last_z.shape[0], last_z.shape[1], output_size[0], output_size[1]) else: dLoss_dz = dLoss_dnextz.reshape(last_z.shape) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz def add_backward(dLoss_dnextz): print('# next_dz.shape: ', list(dLoss_dnextz.shape)) dLoss_dz = dLoss_dnextz print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def relu_backward(next_dz, z): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) zeros_tensor = torch.zeros_like(next_dz) dLoss_dz = torch.where(torch.gt(z, 0), next_dz, zeros_tensor) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def dropback_backward(next_dz, mask, p): print('# zeros probability: ', p) print('# next_dz.shape: ', list(next_dz.shape)) print('# mask.shape: ', list(mask.shape)) zeros_tensor = torch.zeros_like(mask) dLoss_dz = torch.mul(torch.where(torch.eq(mask, 1.), next_dz, zeros_tensor), 1./(1.-p)) print('# dz.shape: ', list(dLoss_dz.shape)) return dLoss_dz @torch.no_grad() def max_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding: ', padding) print('# strides: ', strides) N, C, H, W = z.shape _, _, out_h, out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N): for c in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): flat_idx = torch.argmax(padding_z[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]]) h_idx = strides[0] * i + flat_idx // pooling[1] w_idx = strides[1] * j + flat_idx % pooling[1] padding_dz[n, c, h_idx, w_idx] += next_dz[n, c, i, j] dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def batchnorm2d_backward(next_dz, z, eps, gamma=torch.Tensor([1.,1.,1.])): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# eps: ', eps) print('# gamma.shape: ', list(gamma.shape)) N, C, H, W = z.shape m = N*H*W shape = [N,C,H,W] import numpy as np ax = list(np.arange(len(shape))) shape.pop(1) ax.pop(1) axis = tuple(ax) dxhut = torch.zeros_like(next_dz) for c in range(C): dxhut[:,c] = next_dz[:,c]*gamma[c] dz1 = m*dxhut mu = z.mean(axis=axis, keepdim=True) xmu = z - mu xmu2 = xmu**2 var = xmu2.sum(axis=axis, keepdim=True)/m ivar = 1./torch.pow(var+eps, 0.5) dz2 = (ivar**2)*((dxhut*xmu).sum(axis=axis, keepdim=True))*xmu dz3 = dxhut.sum(axis=axis, keepdim=True) dz = ivar/m*(dz1-dz2-dz3) print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def average_pooling_backward(next_dz, z, pooling, strides, padding=(0, 0)): print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# padding: ', padding) print('# strides: ', strides) N, C, H, W = z.shape _, _, out_h, out_w = next_dz.shape padding_z = F.pad(z, pad=(padding[1],padding[1],padding[0],\ padding[0],0,0), mode='constant', value=0) padding_dz = torch.zeros_like(padding_z) for n in torch.arange(N): for c in torch.arange(C): for i in torch.arange(out_h): for j in torch.arange(out_w): padding_dz[n, c, strides[0] * i:strides[0] * i + pooling[0], strides[1] * j:strides[1] * j + pooling[1]] += next_dz[n, c, i, j] / (pooling[0] * pooling[1]) dz = _remove_padding(padding_dz, padding) # padding_z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] print('# dz.shape: ', list(dz.shape)) return dz @torch.no_grad() def _remove_padding(z, padding): if padding[0] > 0 and padding[1] > 0: return z[:, :, padding[0]:-padding[0], padding[1]:-padding[1]] elif padding[0] > 0: return z[:, :, padding[0]:-padding[0], :] elif padding[1] > 0: return z[:, :, :, padding[1]:-padding[1]] else: return z @torch.no_grad() def conv_backward(next_dz, K, z, padding=(0, 0), strides=(1, 1)): N, C, H, W = z.shape D, C, k1, k2 = K.shape N, D, H1, W1 = next_dz.shape print('# next_dz.shape: ', list(next_dz.shape)) print('# z.shape: ', list(z.shape)) print('# weight.shape: ', list(K.shape)) print('# bias.shape: ', '['+str(K.shape[0])+']') print('# padding: ', padding) print('# strides: ', strides) padding_next_dz = _insert_zeros(next_dz, strides) flip_K = torch.flip(K, (2, 3)) swap_flip_K = torch.swapaxes(flip_K, 0, 1) ppadding_next_dz = F.pad(padding_next_dz, pad=(k2-1-padding[1],k2-1-padding[1],\ k1-1-padding[0],k1-1-padding[0],0,0), mode='constant', value=0) dz = _conv_forward(ppadding_next_dz, swap_flip_K) swap_z = torch.swapaxes(z, 0, 1) dK = _conv_forward(torch.swapaxes(F.pad(z, pad=(padding[1],padding[1],\ padding[0],padding[0],0,0), mode='constant', value=0), 0, 1), torch.swapaxes(padding_next_dz, 0, 1)) db = torch.sum(torch.sum(torch.sum(next_dz, axis=-1), axis=-1), axis=0) # 在高度、宽度上相加；批量大小上相加 print('# dz.shape: ', list(dz.shape)) print('# dweight.shape: ', list(dK.transpose(0,1).shape)) print('# dbias.shape: ', list(db.shape)) return dz, (dK/N).transpose(0,1), db/N @torch.no_grad() def _conv_forward(x, weight, strides=(1,1)): n, c, h_in, w_in = x.shape d, c, k, j = weight.shape x_pad = x x_pad = x_pad.unfold(2, k, strides[0]) x_pad = x_pad.unfold(3, j, strides[1]) out = torch.einsum( 'nchwkj,dckj->ndhw', x_pad, weight) return out @torch.no_grad() def _insert_zeros(dz, strides): N, D, H, W = dz.shape H_last = (H-1)*(strides[0]-1) + H W_last = (W-1)*(strides[1]-1) + W pz = torch.zeros(N, D, H_last, W_last) for n in range(N): for d in range(D): for h in range(0, H_last, strides[0]): for w in range(0, W_last, strides[1]): pz[n,d,h,w] = dz[n,d,h//strides[0],w//strides[1]] return pz @torch.no_grad() def judge_tensors_equal(tensor_A, tensor_B): if(not tensor_A.shape == tensor_B.shape): print('Shape of two compard tensors is not equal.') return None error = 0 error_tolerance = 0.001 np_A = tensor_A.detach().numpy() np_B = tensor_B.detach().numpy() if len(tensor_A.shape) == 4: N, C, H, W = tensor_A.shape for n in range(N): for c in range(C): for h in range(H): for w in range(W): if np_A[n,c,h,w]-np_B[n,c,h,w] > error_tolerance or np_B[n,c,h,w]-np_A[n,c,h,w] > error_tolerance: error += 1 if error%20 == 0: pass print('error', np_A[n,c,h,w], np_B[n,c,h,w]) else: if n*c*h*w % 20000000000000 == 0: pass #print('right', np_A[n,c,h,w], np_B[n,c,h,w]) #print('Error rate: ', error/(N*C*H*W)) print('4D-error-rate: ', end=' ') return error/(N*C*H*W) elif len(tensor_A.shape) == 1: C = tensor_A.shape[0] for c in range(C): if np_A[c]-np_B[c] > error_tolerance or np_B[c]-np_A[c] > error_tolerance: #print(np_A[c], np_B[c]) error += 1 #print('Error rate: ', error/C) print('1D-error-rate: ', end=' ') return error/C elif len(tensor_A.shape) == 2: N, C = tensor_A.shape for n in range(N): for c in range(C): if np_A[n,c]-np_B[n,c] > error_tolerance or np_B[n,c]-np_A[n,c] > error_tolerance: #print(np_A[n,c], np_B[n,c]) error += 1 #print('Error rate: ', error/(C*N)) print('2D-error-rate: ', end=' ') return error/(C*N) @torch.no_grad() def get_featuremap(featuremap_dir=None): import os featuremap = [] if featuremap_dir == None: pth_dir = "./tmp_file/" else: pth_dir = featuremap_dir files = os.listdir(pth_dir) file_nums = [] for i in range(len(files)): if '.pth' in files[i]: file_nums.append(int(files[i].split('.pth')[0])) file_nums.sort() for file_num in file_nums: tensor = torch.load(pth_dir+str(file_num)+'.pth') featuremap.append(tensor) delete_allpths(pth_dir=None) return featuremap @torch.no_grad() def get_structure_parameters_v1(model): layers = [] for layer in model.modules(): if not ':' in str(layer): layers.append(layer) parameters = [] fc_conv_weights = [] for layer in layers: if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights.append(layer.weight) parameters.append(Conv2d_params) elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters.append({'layer_name': layer_name}) elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters.append(MaxPool2d_params) elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters.append(AvgPool2d_params) elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters.append(Dropout_params) elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights.append(layer.weight) parameters.append(BatchNorm2d_params) elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights.append(layer.weight) parameters.append(Linear_params) elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters.append(AdaptiveAvgPool2d_params) else: print('The layer has not been processed in get_structure_parameters_v1!') return parameters, fc_conv_weights @torch.no_grad() def delete_allpths(pth_dir=None): import os if pth_dir == None: pth_dir = "./tmp_file/" for root, dirs, files in os.walk(pth_dir, topdown=False): for name in files: if name.endswith('.pth',): os.remove(os.path.join(root, name)) @torch.no_grad() def mul_items(tensor_size): x = list(tensor_size) mul = 1. for i in range(len(x)): mul *= x[i] return mul @torch.no_grad() def gradient_backward_v1(model, img, label, num_class=1000): return_dz = [] parameters, fc_conv_weights = get_structure_parameters_v1(model) featuremap = get_featuremap(featuremap_dir=None) featuremap.insert(0, img) ### y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[-1], y_true) print('Self calculated loss: ', loss) featuremap.pop() return_dz.append(dLoss_dz) dW_dB_fc_conv = [] for i in range(len(parameters)-1, -1, -1): layer = parameters[i] print('\n======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[-1] weight_z = fc_conv_weights[-1] try: padding = layer['padding'] except: padding = (0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz.append(dLoss_dz) fc_conv_weights.pop() if not len(featuremap) == 1: lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'ReLU': z = featuremap[-1] dLoss_dz = relu_backward(dLoss_dz, z) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'MaxPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'AvgPool2d': z = featuremap[-1] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz.append(dLoss_dz) lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[-1] z = featuremap[-1] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'Dropout': p = layer['p'] mask = featuremap[-1] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz.append(dLoss_dz) featuremap.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[-1] gamma = fc_conv_weights[-1] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz.append(dLoss_dz) fc_conv_weights.pop() lastpop = featuremap.pop() if not len(dLoss_dz.shape) == len(lastpop.shape): dLoss_dz = dLoss_dz.reshape(lastpop.shape) else: print('Not completed in gradient_backward_v1!') print('======================== {0:3} Layer: '.format(str(i))+'{0:9}'.format(layer['layer_name'])+' Backward End ==========================') delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB @torch.no_grad() def make_dot(var, params=None): """ Produces Graphviz representation of PyTorch autograd graph Blue nodes are the Variables that require grad, orange are Tensors saved for backward in torch.autograd.Function Args: var: output Variable params: dict of (name, Variable) to add names to node that require grad (TODO: make optional) """ if params is not None: assert isinstance(params.values()[0], Variable) param_map = {id(v): k for k, v in params.items()} node_attr = dict(style='filled', shape='box', align='left', fontsize='12', ranksep='0.1', height='0.2') dot = Digraph(node_attr=node_attr, graph_attr=dict(size="12,12")) seen = set() def size_to_str(size): return '('+(', ').join(['%d' % v for v in size])+')' def add_nodes(var): if var not in seen: if torch.is_tensor(var): dot.node(str(id(var)), size_to_str(var.size()), fillcolor='orange') elif hasattr(var, 'variable'): u = var.variable name = param_map[id(u)] if params is not None else '' node_name = '%s\n %s' % (name, size_to_str(u.size())) dot.node(str(id(var)), node_name, fillcolor='lightblue') else: dot.node(str(id(var)), str(type(var).__name__)) seen.add(var) if hasattr(var, 'next_functions'): for u in var.next_functions: if u[0] is not None: dot.edge(str(id(u[0])), str(id(var))) add_nodes(u[0]) if hasattr(var, 'saved_tensors'): for t in var.saved_tensors: dot.edge(str(id(t)), str(id(var))) add_nodes(t) print(var) add_nodes(var.grad_fn) return dot def generate_g(model, x): delete_allpths(pth_dir=None) print('\n=========================== Store network model Results Start =========================') y = model(x) print('=========================== Store network model Results End ===========================\n') if 'GoogLeNet' in str(model).split('\n')[0]: g = make_dot(y[0]) return g else: g = make_dot(y) return g @torch.no_grad() def exchange_name(name): if 'Relu' in name: return 'ReLU' elif 'AddmmBackward' in name: return 'Linear' elif 'ViewBackward' in name: return 'View' elif 'Mean' in name or 'Avg' in name: return 'AvgPool2d' elif 'BatchNorm' in name: return 'BatchNorm2d' elif 'Conv' in name: return 'Conv2d' elif 'MaxPool' in name: return 'MaxPool2d' elif 'MulBackward' in name: return 'Dropout_2' elif 'DivBackward' in name: return 'Dropout_1' elif 'AddBackward' in name: return 'Add' elif 'Cat' in name: return 'Cat' elif 'Hardtanh' in name: return 'ReLU6' else: return 'None' @torch.no_grad() def generate_connections(g): graph = str(g).split('\n') labels = {} connections = [] for i in range(len(graph)): if 'label' in graph[i] and graph[i][-1] == '"': labels[(graph[i]+graph[i+1][1:]).split('\t')[1].split(' ')[0]]=\ (graph[i]+graph[i+1][1:]).split('\t')[1].split('"')[1] if 'label' in graph[i] and graph[i][-1] == ']': labels[graph[i].split('\t')[1].split(' ')[0]]=\ graph[i].split('\t')[1].split('=')[1].split(']')[0] for i in range(len(graph)): if '->' in graph[i]: connections.append({labels[graph[i].split('\t')[1].split(' -> ')[0]]+'_'+\ graph[i].split('\t')[1].split(' -> ')[0]:\ labels[graph[i].split('\t')[1].split(' -> ')[1]]+'_'+\ graph[i].split('\t')[1].split(' -> ')[1]}) pop_index = [] for i in range(len(connections)): item_key = list(connections[i].keys())[0] if '(' in item_key or 'TBackward' in item_key: pop_index.append(connections[i]) for i in range(len(pop_index)-1, -1, -1): connections.remove(pop_index[i]) new_connections = [] for item in connections: key, value = list(item.items())[0] key1 = exchange_name(key.split('_')[0]) + '_' + key.split('_')[1] value1 = exchange_name(value.split('_')[0]) + '_' + value.split('_')[1] if 'None' in key1 or 'None' in value1: print('Not completed for '+key+' or '+value+'! Check exchange_name function!') exit() new_connections.append({key1: value1}) if not len(new_connections) == len(connections): print('Generate connections not done! Check generate_connections function!') exit() new_connections.insert(0, {list(new_connections[0].values())[0]: None}) new_connections.append({'None': 'None'}) return connections, new_connections @torch.no_grad() def get_split_connections(connections): return_connections = [] tmp_split = [] for i in range(len(connections)): item = connections[i] if len(tmp_split) == 0: tmp_split.append(item) continue value = list(item.values())[0] last_key = list(tmp_split[-1].keys())[0] if value == last_key: tmp_split.append(item) else: return_connections.append(tmp_split) tmp_split = [item] return return_connections @torch.no_grad() def find_start_end(list_dic_key_value, i, j): key1 = list(list_dic_key_value[i].values())[0] key2 = list(list_dic_key_value[j].keys())[0] start = 0 end = len(list_dic_key_value)-1 for index in range(len(list_dic_key_value)): if key1 == list(list_dic_key_value[index].keys())[0]: start = index break for index in range(len(list_dic_key_value)): if key2 == list(list_dic_key_value[index].keys())[0]: end = index break return start+1, end-1 @torch.no_grad() def merge_connections(connections): import copy last_connections = copy.deepcopy(connections) connections.append({'None':'None'}) num_Throwed = 0 notchoosed = [] print('\n=========================== Restore network model Start ===============================') for i in range(len(connections)): print('# Restore network model: processing {}/{}'.format(i, len(connections)-1)) item_key = list(connections[i].keys())[0] if not 'None' in item_key: if i == 0: pass else: last_item_key = list(connections[i-1].keys())[0] if not connections[i][item_key] == last_item_key: for j in range(i+1, len(connections)): if not list(connections[j].values())[0] == list(connections[j-1].keys())[0]: notchoosed.append(i) start, end = find_start_end(connections, i, j-1) tmp = [] tmp.append(connections[start:end+1]) tmp.append(connections[i:j-1]) last_connections[start:end+1] = [tmp] for kk in range(end-start): last_connections.insert(start, 'Throwed') num_Throwed += 1 break if not notchoosed == []: last_connections = last_connections[:notchoosed[0]] else: pass for i in range(num_Throwed): last_connections.remove('Throwed') if last_connections[-1] == {'None': 'None'}: last_connections.remove({'None': 'None'}) print('=========================== Restore network model End =================================\n') return last_connections @torch.no_grad() def find_next_layer_by_name(layers, name, start_i): for i in range(start_i, len(layers)): layer = layers[i] if name in str(layer): return layer, i @torch.no_grad() def get_layers(last_connections, model): return_layers = [] tmp_layers = [] for layer in model.modules(): if not ':' in str(layer): tmp_layers.append(layer) index_tmp_layers = 0 for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): # 单一层，无分支 current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers.insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers.insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers.insert(0, tmp[0]) if isinstance(last_connections[i-1], list): index_tmp_layers = tmp[1] + 1 elif not list(last_connections[i-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 else: return_layers.insert(0, []) for j in range(len(last_connections[i])): return_layers[0].append([]) if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'ReLU' in current_layer_name: return_layers[0][j].insert(0, torch.nn.ReLU(inplace=True)) elif 'Add' in current_layer_name: return_layers[0][j].insert(0, 'Add') elif 'View' in current_layer_name: return_layers.insert(0, 'View') else: tmp = find_next_layer_by_name(tmp_layers, current_layer_name, index_tmp_layers) return_layers[0][j].insert(0, tmp[0]) if not list(last_connections[i][j][k-1].keys())[0].split('_')[0] == 'Dropout': index_tmp_layers = tmp[1] + 1 return return_layers @torch.no_grad() def get_tensors(last_connections): tensors = get_featuremap(featuremap_dir=None) index_tensors = 0 import copy last_tensors = copy.deepcopy(last_connections) for i in range(len(last_connections)-1, -1, -1): if not isinstance(last_connections[i], list): current_layer_name = list(last_connections[i].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i] = 'Add' elif 'View' in current_layer_name: last_tensors[i] = 'View' else: last_tensors[i] = tensors[index_tensors] index_tensors += 1 else: for j in range(len(last_connections[i])): if len(last_connections[i][j]) == 0: continue for k in range(len(last_connections[i][j])-1, -1, -1): current_layer_name = list(last_connections[i][j][k].keys())[0].split('_')[0] if 'Add' in current_layer_name: last_tensors[i][j][k] = 'Add' elif 'View' in current_layer_name: last_tensors[i][j][k] = 'View' else: last_tensors[i][j][k] = tensors[index_tensors] index_tensors += 1 for i in range(len(last_tensors)-1, -1, -1): if isinstance(last_tensors[i], str): # Add or View if last_tensors[i] == 'Add': last_tensors[i] = last_tensors[i+1][0][0] + last_tensors[i+1][1][0] if last_tensors[i] == 'View': last_tensors[i] = last_tensors[i+1].view(last_tensors[i+1].size(0), -1) elif isinstance(last_tensors[i], list): for j in range(len(last_tensors[i])): if len(last_tensors[i][j]) == 0: last_tensors[i][j].append(last_tensors[i+1]) return last_tensors @torch.no_grad() def get_structure_parameters(return_layers): import copy parameters = copy.deepcopy(return_layers) fc_conv_weights = copy.deepcopy(return_layers) for i in range(len(return_layers)): layer = return_layers[i] if isinstance(layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i] = layer.weight parameters[i] = Conv2d_params elif isinstance(layer, nn.ReLU): layer_name = 'ReLU' parameters[i] = {'layer_name': layer_name} elif layer == 'Add': layer_name = 'Add' parameters[i] = {'layer_name': layer_name} elif layer == 'View': layer_name = 'View' parameters[i] = {'layer_name': layer_name} elif layer == 'Cat': layer_name = 'Cat' parameters[i] = {'layer_name': layer_name} elif isinstance(layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i] = MaxPool2d_params elif isinstance(layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding = layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i] = AvgPool2d_params elif isinstance(layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p = layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i] = Dropout_params elif isinstance(layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i] = layer.weight parameters[i] = BatchNorm2d_params elif isinstance(layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features = layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i] = layer.weight parameters[i] = Linear_params elif isinstance(layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i] = AdaptiveAvgPool2d_params elif isinstance(layer, list): for j in range(len(layer)): for k in range(len(layer[j])): tmp_layer = layer[j][k] ### if isinstance(tmp_layer, nn.Conv2d): layer_name = 'Conv2d' Conv2d_params = {} Conv2d_params['layer_name'] = layer_name # in_channel in_channel = tmp_layer.__dict__.get('in_channels') Conv2d_params['in_channel'] = in_channel # out_channel out_channel = tmp_layer.__dict__.get('out_channels') Conv2d_params['out_channel'] = out_channel # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): Conv2d_params['kernel_size'] = (kernel_size, kernel_size) else: Conv2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): Conv2d_params['stride'] = (stride, stride) else: Conv2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): Conv2d_params['padding'] = (padding, padding) else: Conv2d_params['padding'] = padding # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Conv2d_params elif isinstance(tmp_layer, nn.ReLU): layer_name = 'ReLU' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Add': layer_name = 'Add' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'View': layer_name = 'View' parameters[i][j][k] = {'layer_name': layer_name} elif tmp_layer == 'Cat': layer_name = 'Cat' parameters[i][j][k] = {'layer_name': layer_name} elif isinstance(tmp_layer, nn.MaxPool2d): layer_name = 'MaxPool2d' MaxPool2d_params = {} MaxPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): MaxPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: MaxPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): MaxPool2d_params['stride'] = (stride, stride) else: MaxPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): MaxPool2d_params['padding'] = (padding, padding) else: MaxPool2d_params['padding'] = padding # return parameters[i][j][k] = MaxPool2d_params elif isinstance(tmp_layer, nn.AvgPool2d): layer_name = 'AvgPool2d' AvgPool2d_params = {} AvgPool2d_params['layer_name'] = layer_name # kernel_size kernel_size = tmp_layer.__dict__.get('kernel_size') if not isinstance(kernel_size, tuple): AvgPool2d_params['kernel_size'] = (kernel_size, kernel_size) else: AvgPool2d_params['kernel_size'] = kernel_size # stride stride = tmp_layer.__dict__.get('stride') if not isinstance(stride, tuple): AvgPool2d_params['stride'] = (stride, stride) else: AvgPool2d_params['stride'] = stride # padding padding = tmp_layer.__dict__.get('padding') if not isinstance(padding, tuple): AvgPool2d_params['padding'] = (padding, padding) else: AvgPool2d_params['padding'] = padding # return parameters[i][j][k] = AvgPool2d_params elif isinstance(tmp_layer, nn.Dropout): layer_name = 'Dropout' Dropout_params = {} Dropout_params['layer_name'] = layer_name # p p = tmp_layer.__dict__.get('p') Dropout_params['p'] = p # return parameters[i][j][k] = Dropout_params elif isinstance(tmp_layer, nn.BatchNorm2d): layer_name = 'BatchNorm2d' BatchNorm2d_params = {} BatchNorm2d_params['layer_name'] = layer_name # num_features num_features = tmp_layer.__dict__.get('num_features') BatchNorm2d_params['num_features'] = num_features # eps eps = tmp_layer.__dict__.get('eps') BatchNorm2d_params['eps'] = eps # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = BatchNorm2d_params elif isinstance(tmp_layer, nn.Linear): layer_name = 'Linear' Linear_params = {} Linear_params['layer_name'] = layer_name # in_features in_features = tmp_layer.__dict__.get('in_features') Linear_params['in_features'] = in_features # out_features out_features = tmp_layer.__dict__.get('out_features') Linear_params['out_features'] = out_features # return fc_conv_weights[i][j][k] = tmp_layer.weight parameters[i][j][k] = Linear_params elif isinstance(tmp_layer, nn.AdaptiveAvgPool2d): layer_name = 'AdaptiveAvgPool2d' AdaptiveAvgPool2d_params = {} AdaptiveAvgPool2d_params['layer_name'] = layer_name # output_size output_size = tmp_layer.__dict__.get('output_size') if not isinstance(output_size, tuple): AdaptiveAvgPool2d_params['output_size'] = (output_size, output_size) else: AdaptiveAvgPool2d_params['output_size'] = output_size # return parameters[i][j][k] = AdaptiveAvgPool2d_params ### else: print('The layer has not been processed in get_structure_parameters!') return parameters, fc_conv_weights def gradient_backward_v2(model, img, label, num_class=1000, g_view=False): x = Variable(img) g = generate_g(model, x) if g_view: g.view() delete_allpths(pth_dir=None) print('\n=========================== Generate Tensors Start ====================================') result = model(img) print('=========================== Generate Tensors End ======================================\n') Loss = nn.CrossEntropyLoss() if 'GoogLeNet' in str(model).split('\n')[0]: loss_torch = Loss(result[0], label) else: loss_torch = Loss(result, label) _, connections = generate_connections(g) last_connections = merge_connections(connections) return_layers = get_layers(last_connections, model) return_tensors = get_tensors(last_connections) parameters, fc_conv_weights = get_structure_parameters(return_layers) ''' print('================') for i in range(len(last_connections)): print(i, last_connections[i]) print('================') print('================') for i in range(len(return_layers)): print(i, return_layers[i]) print('================') print('================') for i in range(len(parameters)): print(i, parameters[i]) print('================') print('================') for i in range(len(return_tensors)): if not isinstance(return_tensors[i], list) and not isinstance(return_tensors[i], str): print('=========', i, return_tensors[i].shape) print('================') ''' import copy return_dz = copy.deepcopy(last_connections) featuremap = return_tensors featuremap.append(img) y_true = F.one_hot(label, num_classes=num_class).float() loss, dLoss_dz = cross_entropy_loss(featuremap[0], y_true) featuremap.pop(0) return_dz.append(dLoss_dz) #####################tensors ''' for i in range(len(last_connections)): print(last_connections[i]) for i in range(len(featuremap)): if not isinstance(featuremap[i], list): print('=========', i, featuremap[i].shape) else: for j in range(len(featuremap[i])): for k in range(len(featuremap[i][j])): print(' =========', i, j, k, featuremap[i][j][k].shape) ''' ##################### # 前面n层倒序遍历 for i in range(len(parameters)): layer = parameters[i] if not isinstance(layer, list): print('\n======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward Start ========================') if layer['layer_name'] == 'Conv2d': z = featuremap[i] weight_z = fc_conv_weights[i] try: padding = layer['padding'] except: padding = (0, 0) stride = layer['stride'] dLoss_dz, dLoss_dW, dLoss_dB = conv_backward(dLoss_dz, weight_z, z, padding, stride) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'ReLU': z = featuremap[i] dLoss_dz = relu_backward(dLoss_dz, z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'MaxPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = max_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'AvgPool2d': z = featuremap[i] pooling = layer['kernel_size'] stride = layer['stride'] padding = layer['padding'] dLoss_dz = average_pooling_backward(dLoss_dz, z, pooling, stride, padding) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Linear': weight_z = fc_conv_weights[i] z = featuremap[i] dLoss_dz, dLoss_dW, dLoss_dB = fc_backward(dLoss_dz, z, weight_z) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'View': last_z = featuremap[i+1] if 'Pool' in parameters[i+1]['layer_name']: params = (parameters[i+1]['kernel_size'], parameters[i+1]['stride'], parameters[i+1]['padding']) else: params = None dLoss_dz = view_backward(dLoss_dz, last_z, params) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Add': dLoss_dz = add_backward(dLoss_dz) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'Dropout': if parameters[i-1]['layer_name'] == 'Dropout': return_dz[i] = dLoss_dz print('# Skip this layer because the layer has been calcualted!') print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.\ format(layer['layer_name'])+' Backward End ==========================') continue p = layer['p'] mask = featuremap[i] dLoss_dz = dropback_backward(dLoss_dz, mask, p) return_dz[i] = dLoss_dz elif layer['layer_name'] == 'BatchNorm2d': eps = layer['eps'] z = featuremap[i] gamma = fc_conv_weights[i] dLoss_dz = batchnorm2d_backward(dLoss_dz, z, eps, gamma) return_dz[i] = dLoss_dz print('======================== {0:3} Layer: '.format(str(len(parameters)-1-i))+'{0:11}'.format(layer['layer_name'])+' Backward End ==========================') elif isinstance(layer, list): import copy tmp_dLoss_dz = [] for j in range(len(layer)): tmp_dLoss_dz.append(copy.deepcopy(dLoss_dz)) for k in range(len(layer[j])): tmp_layer = layer[j][k] print('\n=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward Start ====================') if tmp_layer['layer_name'] == 'Conv2d': if k+1 >= len(featuremap[i-1][j]): z = featuremap[i] else: z = featuremap[i-1][j][k+1] weight_z = fc_conv_weights[i][j][k] try: padding = tmp_layer['padding'] except: padding = (0, 0) stride = tmp_layer['stride'] tmp_dLoss_dz[-1], dLoss_dW, dLoss_dB = conv_backward(tmp_dLoss_dz[-1], weight_z, z, padding, stride) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'ReLU': z = featuremap[i-1][j][k+1] tmp_dLoss_dz[-1] = relu_backward(tmp_dLoss_dz[-1], z) return_dz[i][j][k] = tmp_dLoss_dz[-1] elif tmp_layer['layer_name'] == 'BatchNorm2d': eps = tmp_layer['eps'] z = featuremap[i-1][j][k+1] gamma = fc_conv_weights[i][j][k] tmp_dLoss_dz[-1] = batchnorm2d_backward(tmp_dLoss_dz[-1], z, eps, gamma) return_dz[i][j][k] = tmp_dLoss_dz[-1] print('=========================== {0:3} Branch: '.format(str(len(parameters)-1-i))+'{0:11}'.format(tmp_layer['layer_name'])+' Backward End ======================') print(tmp_dLoss_dz[0].shape, tmp_dLoss_dz[1].shape) dLoss_dz = tmp_dLoss_dz[0] + tmp_dLoss_dz[1] else: print('Not completed in gradient_backward!') print('# Torch calculated loss: ', loss_torch.detach().numpy()) loss_torch.backward() if 'VGG' in str(model) or 'AlexNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.features[0].weight.grad)) elif 'ResNet' in str(model): print(judge_tensors_equal(dLoss_dW, model.conv1.weight.grad)) delete_allpths(pth_dir=None) return return_dz, dLoss_dW, dLoss_dB

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1,399

173

34.624732

0.732512

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0

0.552056

0

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false

0.0035

0.011374

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0.070866

0

null

0

null

0

1

0

0ae2d03accd91cc3db5f01917f5d31fdecbb74e5

4,372

py

Python

ark_nlp/factory/utils/attack.py

yubuyuabc/ark-nlp

165d35cfacd7476791c0aeba19bf43f4f8079553

[ "Apache-2.0" ]

1

2022-03-23T05:10:55.000Z

ark_nlp/factory/utils/attack.py

yubuyuabc/ark-nlp

165d35cfacd7476791c0aeba19bf43f4f8079553

[ "Apache-2.0" ]

null

ark_nlp/factory/utils/attack.py

yubuyuabc/ark-nlp

165d35cfacd7476791c0aeba19bf43f4f8079553

[ "Apache-2.0" ]

null

import torch class FGM(object): """ 基于FGM算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> # 初始化 >>> fgm = FGM(module) >>> for batch_input, batch_label in data: >>> # 正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> # 对抗训练 >>> fgm.attack() # 在embedding上添加对抗扰动 >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> fgm.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 """ def __init__(self, module): self.module = module self.backup = {} def attack( self, epsilon=1., emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: self.backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at = epsilon * param.grad / norm param.data.add_(r_at) def restore( self, emb_name='word_embeddings' ): for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in self.backup param.data = self.backup[name] self.backup = {} class PGD(object): """ 基于PGD算法的攻击机制 Args: module (:obj:`torch.nn.Module`): 模型 Examples:: >>> pgd = PGD(module) >>> K = 3 >>> for batch_input, batch_label in data: >>> # 正常训练 >>> loss = module(batch_input, batch_label) >>> loss.backward() # 反向传播，得到正常的grad >>> pgd.backup_grad() >>> # 对抗训练 >>> for t in range(K): >>> pgd.attack(is_first_attack=(t==0)) # 在embedding上添加对抗扰动, first attack时备份param.data >>> if t != K-1: >>> optimizer.zero_grad() >>> else: >>> pgd.restore_grad() >>> loss_adv = module(batch_input, batch_label) >>> loss_adv.backward() # 反向传播，并在正常的grad基础上，累加对抗训练的梯度 >>> pgd.restore() # 恢复embedding参数 >>> # 梯度下降，更新参数 >>> optimizer.step() >>> optimizer.zero_grad() Reference: [1] https://zhuanlan.zhihu.com/p/91269728 """ def __init__(self, module): self.module = module self.emb_backup = {} self.grad_backup = {} def attack( self, epsilon=1., alpha=0.3, emb_name='emb.', is_first_attack=False ): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: if is_first_attack: self.emb_backup[name] = param.data.clone() norm = torch.norm(param.grad) if norm != 0 and not torch.isnan(norm): r_at = alpha * param.grad / norm param.data.add_(r_at) param.data = self.project(name, param.data, epsilon) def restore(self, emb_name='emb.'): # emb_name这个参数要换成你模型中embedding的参数名 for name, param in self.module.named_parameters(): if param.requires_grad and emb_name in name: assert name in self.emb_backup param.data = self.emb_backup[name] self.emb_backup = {} def project(self, param_name, param_data, epsilon): r = param_data - self.emb_backup[param_name] if torch.norm(r) > epsilon: r = epsilon * r / torch.norm(r) return self.emb_backup[param_name] + r def backup_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: self.grad_backup[name] = param.grad.clone() def restore_grad(self): for name, param in self.module.named_parameters(): if param.requires_grad: param.grad = self.grad_backup[name]

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4.648536

0.177824

0.044554

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null

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null

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1

0

0ae341f931ab8799a80b73c9036820e58b4d7de6

5,790

py

Python

core.py

sreejithr/deepfake

c7115ce90ea281e2eb95d75f436efa102c8f2e3c

[ "MIT" ]

null

core.py

sreejithr/deepfake

c7115ce90ea281e2eb95d75f436efa102c8f2e3c

[ "MIT" ]

3

2021-09-08T02:24:48.000Z

2022-03-12T00:44:53.000Z

core.py

sreejithr/deepfake

c7115ce90ea281e2eb95d75f436efa102c8f2e3c

[ "MIT" ]

null

import cv2 import torch import yaml import imageio import throttle import numpy as np import matplotlib.pyplot as plt from argparse import ArgumentParser from skimage.transform import resize from scipy.spatial import ConvexHull from modules.generator import OcclusionAwareGenerator from modules.keypoint_detector import KPDetector from sync_batchnorm import DataParallelWithCallback #from animate import normalize_kp # command = [ffmpeg, # '-y', # '-f', 'rawvideo', # '-vcodec','rawvideo', # '-pix_fmt', 'bgr24', # '-s', dimension, # '-i', '-', # '-c:v', 'libx264', # '-pix_fmt', 'yuv420p', # '-preset', 'ultrafast', # '-f', 'flv', # 'rtmp://10.10.10.80/live/mystream'] def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False, use_relative_movement=False, use_relative_jacobian=False): if adapt_movement_scale: source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area) else: adapt_movement_scale = 1 kp_new = {k: v for k, v in kp_driving.items()} if use_relative_movement: kp_value_diff = (kp_driving['value'] - kp_driving_initial['value']) kp_value_diff *= adapt_movement_scale kp_new['value'] = kp_value_diff + kp_source['value'] if use_relative_jacobian: jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian'])) kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian']) return kp_new def load_checkpoints(config_path, checkpoint_path, cpu=False): with open(config_path) as f: config = yaml.load(f) generator = OcclusionAwareGenerator(**config['model_params']['generator_params'], **config['model_params']['common_params']) if not cpu: generator.cuda() kp_detector = KPDetector(**config['model_params']['kp_detector_params'], **config['model_params']['common_params']) if not cpu: kp_detector.cuda() if cpu: checkpoint = torch.load(checkpoint_path, map_location=torch.device('cpu')) else: checkpoint = torch.load(checkpoint_path) generator.load_state_dict(checkpoint['generator']) kp_detector.load_state_dict(checkpoint['kp_detector']) if not cpu: generator = DataParallelWithCallback(generator) kp_detector = DataParallelWithCallback(kp_detector) generator.eval() kp_detector.eval() return generator, kp_detector @throttle.wrap(1, 2) def forward(source_image, driving_frame, kp_source, kp_driving_initial, generator, kp_detector, relative=True, adapt_scale=True, cpu=True): kp_driving = kp_detector(driving_frame) kp_norm = normalize_kp( kp_source=kp_source, kp_driving=kp_driving, kp_driving_initial=kp_driving_initial, use_relative_movement=relative, use_relative_jacobian=relative, adapt_movement_scale=adapt_scale ) out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm) return np.transpose(out["prediction"].data.cpu().numpy(), [0, 2, 3, 1])[0] if __name__ == "__main__": parser = ArgumentParser() parser.add_argument("--config", required=True, help="path to config") parser.add_argument("--source_image", required=True, help="path to source image") parser.add_argument("--checkpoint", default="vox-cpk.pth.tar", help="path to checkpoint") parser.add_argument("--relative", dest="relative", action="store_true", help="use relative or absolute keypoint coordinates") parser.add_argument("--adapt_scale", dest="adapt_scale", action="store_true", help="adapt movement scale based on convex hull of keypoints") parser.add_argument("--cpu", dest="cpu", action="store_true", help="CPU mode") parser.set_defaults(relative=False) parser.set_defaults(adapt_scale=False) opt = parser.parse_args() generator, kp_detector = load_checkpoints(config_path=opt.config, checkpoint_path=opt.checkpoint, cpu=opt.cpu) source_image = imageio.imread(opt.source_image) source_image = resize(source_image, (256, 256))[..., :3] source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not opt.cpu: source = source.cuda() kp_source = kp_detector(source) #out = cv2.VideoWriter('outpy.avi', cv2.VideoWriter_fourcc('M','J','P','G'), 30, (256, 256)) kp_driving_initial = None camera = cv2.VideoCapture(0) ret, frame = camera.read() while True: ret, frame = camera.read() resized = resize(frame, (256, 256))[..., :3] if not opt.cpu: resized = resized.cuda() # y = torch.tensor(np.array(resized)) # x = y.cpu().numpy() # image = cv2.cvtColor(x, cv2.COLOR_BGR2RGB) # # x = y.permute(1, 2, 0) # plt.imshow(np.array(image)) # plt.show() driving_resized = torch.tensor(np.array(resized)[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2) if not kp_driving_initial: kp_driving_initial = kp_detector(driving_resized) fake_frame = forward( source, driving_resized, kp_source, kp_driving_initial, generator, kp_detector, relative=opt.relative, adapt_scale=opt.adapt_scale, cpu=opt.cpu ) cv2.imshow("frame", fake_frame) #x = np.squeeze(driving_resized, axis=(0,)) #x = driving_resized[0].permute(1, 2, 0) # plt_driving = driving_resized #permute(2, 3, 1) #print(plt_driving.shape) #plt.imshow(x) #plt.show() if cv2.waitKey(1) & 0xFF == ord('q'): break camera.release() cv2.destroyAllWindows()

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0.017722

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34.464286

0.779958

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0.027027

false

0

0.117117

0

0.171171

0

null

0

null

0

1

0

0ae6683abfd956b5c3952439b03a59e007c9300a

2,402

py

Python

models/1-Tom/train/kaggle-hubmap-main/src/02_train/transforms.py

navekshasood/HuBMAP---Hacking-the-Kidney

018100fe4bfa5e8764b9df5a9d188e2c670ac061

[ "MIT" ]

null

models/1-Tom/train/kaggle-hubmap-main/src/02_train/transforms.py

navekshasood/HuBMAP---Hacking-the-Kidney

018100fe4bfa5e8764b9df5a9d188e2c670ac061

[ "MIT" ]

null

models/1-Tom/train/kaggle-hubmap-main/src/02_train/transforms.py

navekshasood/HuBMAP---Hacking-the-Kidney

018100fe4bfa5e8764b9df5a9d188e2c670ac061

[ "MIT" ]

null

import numpy as np from albumentations import (Compose, HorizontalFlip, VerticalFlip, Rotate, RandomRotate90, ShiftScaleRotate, ElasticTransform, GridDistortion, RandomSizedCrop, RandomCrop, CenterCrop, RandomBrightnessContrast, HueSaturationValue, IAASharpen, RandomGamma, RandomBrightness, RandomBrightnessContrast, GaussianBlur,CLAHE, Cutout, CoarseDropout, GaussNoise, ChannelShuffle, ToGray, OpticalDistortion, Normalize, OneOf, NoOp) from albumentations.pytorch import ToTensorV2 as ToTensor from get_config import get_config config = get_config() MEAN = np.array([0.485, 0.456, 0.406]) STD = np.array([0.229, 0.224, 0.225]) def get_transforms_train(): transform_train = Compose([ #Basic RandomRotate90(p=1), HorizontalFlip(p=0.5), #Morphology ShiftScaleRotate(shift_limit=0, scale_limit=(-0.2,0.2), rotate_limit=(-30,30), interpolation=1, border_mode=0, value=(0,0,0), p=0.5), GaussNoise(var_limit=(0,50.0), mean=0, p=0.5), GaussianBlur(blur_limit=(3,7), p=0.5), #Color RandomBrightnessContrast(brightness_limit=0.35, contrast_limit=0.5, brightness_by_max=True,p=0.5), HueSaturationValue(hue_shift_limit=30, sat_shift_limit=30, val_shift_limit=0, p=0.5), CoarseDropout(max_holes=2, max_height=config['input_resolution'][0]//4, max_width=config['input_resolution'][1]//4, min_holes=1, min_height=config['input_resolution'][0]//16, min_width=config['input_resolution'][1]//16, fill_value=0, mask_fill_value=0, p=0.5), Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ]) return transform_train def get_transforms_valid(): transform_valid = Compose([ Normalize(mean=(MEAN[0], MEAN[1], MEAN[2]), std=(STD[0], STD[1], STD[2])), ToTensor(), ] ) return transform_valid def denormalize(z, mean=MEAN.reshape(-1,1,1), std=STD.reshape(-1,1,1)): return std*z + mean

40.711864

113

0.572856

272

2,402

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0.015719

0.011976

0.181138

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0

0.06411

0.305162

2,402

58

114

41.413793

0.736369

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0

1

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false

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0.022222

0.222222

0

null

0

null

0

1

0

0ae709052ebf9505470ee0404f1013ba86cb8e0e

13,017

py

Python

cubspack/geometry.py

Majikat/cubspack

16aa6df0603d48d757d74837d3457a1934601d89

[ "Apache-2.0" ]

11

2018-06-18T12:05:34.000Z

2021-02-24T19:00:24.000Z

cubspack/geometry.py

Majikat/cubspack

16aa6df0603d48d757d74837d3457a1934601d89

[ "Apache-2.0" ]

null

cubspack/geometry.py

Majikat/cubspack

16aa6df0603d48d757d74837d3457a1934601d89

[ "Apache-2.0" ]

2

2018-04-08T17:30:00.000Z

2018-09-27T08:38:42.000Z

# -*- coding: utf-8 -*- from math import sqrt class Point(object): __slots__ = ('x', 'y', 'z') def __init__(self, x, y, z): self.x = x self.y = y self.z = z def __eq__(self, other): return (self.x == other.x and self.y == other.y and self.z == other.z) def __repr__(self): return "P({}, {}, {})".format(self.x, self.y, self.z) def distance(self, point): """Calculate distance to another point""" return sqrt((self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2) def distance_squared(self, point): return (self.x - point.x)**2 + (self.y - point.y)**2 + ( self.z - point.z)**2 class Segment(object): __slots__ = ('start', 'end') def __init__(self, start, end): """Arguments: start (Point): Segment start point end (Point): Segment end point """ assert(isinstance(start, Point) and isinstance(end, Point)) self.start = start self.end = end def __eq__(self, other): if not isinstance(other, self.__class__): None return self.start == other.start and self.end == other.end def __repr__(self): return "S({}, {})".format(self.start, self.end) @property def length_squared(self): """Faster than length and useful for some comparisons""" return self.start.distance_squared(self.end) @property def length(self): return self.start.distance(self.end) @property def top(self): return max(self.start.y, self.end.y) @property def bottom(self): return min(self.start.y, self.end.y) @property def right(self): return max(self.start.x, self.end.x) @property def left(self): return min(self.start.x, self.end.x) @property def ineye(self): return max(self.start.z, self.end.z) @property def outeye(self): return min(self.start.z, self.end.z) class HSegment(Segment): """Horizontal Segment""" def __init__(self, start, length): """Create an Horizontal segment given its left most end point and its length. Arguments: - start (Point): Starting Point - length (number): segment length """ assert(isinstance(start, Point) and not isinstance(length, Point)) super(HSegment, self).__init__( start, Point(start.x + length, start.y, start.z)) @property def length(self): return self.end.x - self.start.x class VSegment(Segment): """Vertical Segment""" def __init__(self, start, length): """Create a Vertical segment given its bottom most end point and its length. Arguments: - start (Point): Starting Point - length (number): segment length """ assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y + length, start.z)) @property def length(self): return self.end.y - self.start.y class DSegment(Segment): """In-Depth Segment""" def __init__(self, start, length): """Create an In-Depth segment given its bottom most end point and its length. Arguments: - start (Point): Starting Point - length (number): segment length """ assert(isinstance(start, Point) and not isinstance(length, Point)) super(VSegment, self).__init__( start, Point(start.x, start.y, start.z + length)) @property def length(self): return self.end.z - self.start.z class Cuboid(object): """Basic cuboid primitive class. x, y, z-> Lower right corner coordinates width - height - depth - """ __slots__ = ('width', 'height', 'depth', 'x', 'y', 'z', 'rid') def __init__(self, x, y, z, width, height, depth, rid=None): """Initiating the Cuboid Args: x (int, float): y (int, float): z (int, float): width (int, float): height (int, float): depth (int, float): rid (identifier object): """ assert(height >= 0 and width >= 0 and depth >= 0) self.width = width self.height = height self.depth = depth self.x = x self.y = y self.z = z self.rid = rid @property def bottom(self): """Cuboid bottom edge y coordinate""" return self.y @property def top(self): """Cuboid top edge y coordiante""" return self.y + self.height @property def left(self): """Cuboid left edge x coordinate""" return self.x @property def right(self): """Cuboid right edge x coordinate""" return self.x + self.width @property def outeye(self): """Cuboid farther from eye edge z coordinate""" return self.z @property def ineye(self): """Cuboid nearer from eye edge z coordinate""" return self.z + self.depth @property def corner_top_l(self): return Point(self.left, self.top, self.outeye) @property def corner_top_r(self): return Point(self.right, self.top, self.outeye) @property def corner_bot_r(self): return Point(self.right, self.bottom, self.outeye) @property def corner_bot_l(self): return Point(self.left, self.bottom, self.outeye) @property def corner_top_l_out(self): return Point(self.left, self.top, self.ineye) @property def corner_top_r_out(self): return Point(self.right, self.top, self.ineye) @property def corner_bot_r_out(self): return Point(self.right, self.bottom, self.ineye) @property def corner_bot_l_out(self): return Point(self.left, self.bottom, self.ineye) def __lt__(self, other): """Compare cuboids by volume (used for sorting)""" return self.volume() < other.volume() def __eq__(self, other): """Equal cuboids have same properties.""" if not isinstance(other, self.__class__): return False return (self.width == other.width and self.height == other.height and self.depth == other.depth and self.x == other.x and self.y == other.y and self.z == other.z) def __hash__(self): return hash( (self.x, self.y, self.z, self.width, self.height, self.depth)) def __iter__(self): """Iterate through cuboid corners""" yield self.corner_top_l yield self.corner_top_r yield self.corner_bot_r yield self.corner_bot_l yield self.corner_top_l_out yield self.corner_top_r_out yield self.corner_bot_r_out yield self.corner_bot_l_out def __repr__(self): return "R({}, {}, {}, {}, {}, {})".format( self.x, self.y, self.z, self.width, self.height, self.depth) def volume(self): """Cuboid volume""" return self.width * self.height * self.depth def move(self, x, y, z): """Move Cuboid to x,y,z coordinates Arguments: x (int, float): X coordinate y (int, float): Y coordinate z (int, float): Z coordinate """ self.x = x self.y = y self.z = z def contains(self, cub): """Tests if another cuboid is contained by this one Arguments: cub (Cuboid): The other cuboiud Returns: bool: True if it is inside this one, False otherwise """ return (cub.y >= self.y and cub.x >= self.x and cub.z >= self.z and cub.y + cub.height <= self.y + self.height and cub.x + cub.width <= self.x + self.width and cub.z + cub.depth <= self.z + self.depth) def intersects(self, cub, edges=False): """Detect intersections between this cuboid and cub. Args: cub (Cuboid): Cuboid to test for intersections. edges (bool): Accept edge touching cuboids as intersects or not Returns: bool: True if the cuboids intersect, False otherwise """ # Not even touching if (self.bottom > cub.top or self.top < cub.bottom or self.left > cub.right or self.right < cub.left or self.outeye > cub.ineye or self.ineye < cub.outeye): return False # Discard edge intersects if not edges: if (self.bottom == cub.top or self.top == cub.bottom or self.left == cub.right or self.right == cub.left or self.outeye == cub.ineye or self.ineye == cub.outeye): return False # Discard corner intersects if (self.left == cub.right and self.bottom == cub.top and self.outeye == cub.ineye or self.left == cub.right and cub.bottom == self.top and self.outeye == cub.ineye or self.left == cub.right and self.bottom == cub.top and cub.outeye == self.ineye or self.left == cub.right and cub.bottom == self.top and cub.outeye == self.ineye or cub.left == self.right and self.bottom == cub.top and self.outeye == cub.ineye or cub.left == self.right and cub.bottom == self.top and self.outeye == cub.ineye or cub.left == self.right and self.bottom == cub.top and cub.outeye == self.ineye or cub.left == self.right and cub.bottom == self.top and cub.outeye == self.ineye): return False return True def intersection(self, cub, edges=False): """Returns the cuboid resulting of the intersection of this and cub If the cuboids are only touching by their edges, and the argument 'edges' is True the cuboid returned will have a volume of 0. Returns None if there is no intersection. Arguments: cub (Cuboid): The other cuboid. edges (bool): If true, touching edges are considered an intersection, and a cuboid of 0 height or width or depth will be returned Returns: Cuboid: Intersection. None: There was no intersection. """ if not self.intersects(cub, edges=edges): return None bottom = max(self.bottom, cub.bottom) left = max(self.left, cub.left) top = min(self.top, cub.top) right = min(self.right, cub.right) outeye = max(self.outeye, cub.outeye) ineye = min(self.ineye, cub.ineye) return Cuboid( left, bottom, outeye, right - left, top - bottom, ineye - outeye) def join(self, other): """Try to join a cuboid to this one. If the result is also a cuboid and the operation is successful then this cuboid is modified to the union. Arguments: other (Cuboid): Cuboid to join Returns: bool: True when successfully joined, False otherwise """ if self.contains(other): return True if other.contains(self): self.x = other.x self.y = other.y self.z = other.z self.width = other.width self.height = other.height self.depth = other.depth return True if not self.intersects(other, edges=True): return False # Other cuboid is Up/Down from this if self.left == other.left and self.width == other.width and \ self.outeye == other.outeye and self.depth == self.depth: y_min = min(self.bottom, other.bottom) y_max = max(self.top, other.top) self.y = y_min self.height = y_max - y_min return True # Other cuboid is Right/Left from this if self.bottom == other.bottom and self.height == other.height and \ self.outeye == other.outeye and self.depth == self.depth: x_min = min(self.left, other.left) x_max = max(self.right, other.right) self.x = x_min self.width = x_max - x_min return True # Other cuboid is Right/Left from this if self.bottom == other.bottom and self.height == other.height and \ self.left == other.left and self.width == other.width: z_min = min(self.outeye, other.outeye) z_max = max(self.ineye, other.ineye) self.z = z_min self.depth = z_max - z_min return True return False

29.517007

85

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0.495068

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0.360203

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0ae84e0cfa142229ba7d5efbff2238d28b93f418

16,661

py

Python

app/recipe/tests/test_recipe_api.py

tahmadvand/recipe_app_api

40b4cc6960d7dc4858373b5f6ccca980ed0eeac8

[ "MIT" ]

null

app/recipe/tests/test_recipe_api.py

tahmadvand/recipe_app_api

40b4cc6960d7dc4858373b5f6ccca980ed0eeac8

[ "MIT" ]

null

app/recipe/tests/test_recipe_api.py

tahmadvand/recipe_app_api

40b4cc6960d7dc4858373b5f6ccca980ed0eeac8

[ "MIT" ]

null

from django.contrib.auth import get_user_model from django.test import TestCase from django.urls import reverse from rest_framework import status from rest_framework.test import APIClient # use that for making our API requests from core.models import Recipe, Tag, Ingredient from ..serializers import RecipeSerializer, RecipeDetailSerializer import tempfile # allows you to call a function which will then create a temp file # somewhere in the system and then you can remove that file after # you've used it import os # this allows us to perform things like # creating path names and also checking if files exist on the system from PIL import Image # pillow, this will import our image class which will let us then # create test images which we can then upload to our API RECIPES_URL = reverse('recipe:recipe-list') # since we're going to need to access the URL in more # or less all the tests let's assign that as a variable # at top of the class in all capitals. # app : identifier of the URL in the app # /api/recipe/recipes # /api/recipe/recipes/1/ (id) --> detail url def image_upload_url(recipe_id): """Return URL for recipe image upload""" return reverse('recipe:recipe-upload-image', args=[recipe_id]) # generate our upload image url # you're going to need the existing recipe ID in order to upload an image def detail_url(recipe_id): """Return recipe detail URL""" return reverse('recipe:recipe-detail', args=[recipe_id]) # name of the end point that the default router will create # for our viewset because we're going to have a detail action # this is how you specify arguments with the reverse function # you just pass in args and then you pass in a list of the # arguments you want to add # here we have single item def sample_tag(user, name='Main course'): """Create and return a sample tag""" return Tag.objects.create(user=user, name=name) def sample_ingredient(user, name='Cinnamon'): """Create and return a sample ingredient""" return Ingredient.objects.create(user=user, name=name) def sample_recipe(user, **params): """Create and return a sample recipe""" defaults = { 'title': 'Sample recipe', 'time_minutes': 10, 'price': 5.00, } defaults.update(params) return Recipe.objects.create(user=user, **defaults) # convert the dictionary into the argument # when you use the two asterisks when calling a # function it has the reverse effect. class PublicRecipeApiTests(TestCase): """Test unauthenticated recipe API access""" def setUp(self): self.client = APIClient() def test_required_auth(self): """Test the authenticaiton is required""" res = self.client.get(RECIPES_URL) self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED) class PrivateRecipeApiTests(TestCase): """Test authenticated recipe API access""" def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user( 'test@londonappdev.com', 'testpass' ) self.client.force_authenticate(self.user) def test_retrieve_recipes(self): """Test retrieving list of recipes""" sample_recipe(user=self.user) sample_recipe(user=self.user) # we're going to access them by retrieving # all of the recipes from our database. res = self.client.get(RECIPES_URL) recipes = Recipe.objects.all().order_by('-id') serializer = RecipeSerializer(recipes, many=True) self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(res.data, serializer.data) def test_recipes_limited_to_user(self): """Test retrieving recipes for user""" # test recipes are limited to the authenticated user. user2 = get_user_model().objects.create_user( 'other@londonappdev.com', 'pass' ) sample_recipe(user=user2) sample_recipe(user=self.user) res = self.client.get(RECIPES_URL) # filter our recipes by the authenticated user recipes = Recipe.objects.filter(user=self.user) serializer = RecipeSerializer(recipes, many=True) # many=true: this is because we were returning the list view # or we wanted to simulate the list view in our serializer self.assertEqual(res.status_code, status.HTTP_200_OK) self.assertEqual(len(res.data), 1) self.assertEqual(res.data, serializer.data) def test_view_recipe_detail(self): """Test viewing a recipe detail""" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) recipe.ingredients.add(sample_ingredient(user=self.user)) url = detail_url(recipe.id) res = self.client.get(url) serializer = RecipeDetailSerializer(recipe) # in this case we just want to serialize a single object self.assertEqual(res.data, serializer.data) def test_create_basic_recipe(self): """Test creating recipe""" payload = { 'title': 'Test recipe', 'time_minutes': 30, 'price': 10.00, } res = self.client.post(RECIPES_URL, payload) # post this payload dictionary to our recipes URL. self.assertEqual(res.status_code, status.HTTP_201_CREATED) # this is the standard HTTP response code for creating objects # in an API. recipe = Recipe.objects.get(id=res.data['id']) # When you create an object using the Django rest framework the # default behavior is that it will return a dictionary containing # the created object This is how I know that if we do res.data and # retrieve the id key this will get the id of the created object. # Next what we're going to do is we're going to loop through each # one of these keys and then we're going to check # that is the correct value assigned to our recipe model. for key in payload.keys(): self.assertEqual(payload[key], getattr(recipe, key)) # assertion for each one of these keys, check that it is # equal to the same key in the recipe # payload[key]: This will actually get the value of the # key in our payload object # getattr: that allows you to retrieve an attribute from # an object by passing in a variable. (instead of recipe.key) def test_create_recipe_with_tags(self): """Test creating a recipe with tags""" tag1 = sample_tag(user=self.user, name='Tag 1') tag2 = sample_tag(user=self.user, name='Tag 2') payload = { 'title': 'Test recipe with two tags', 'tags': [tag1.id, tag2.id], 'time_minutes': 30, 'price': 10.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) # retrieve the created recipe tags = recipe.tags.all() # retrieve the tags that were created with the recipe self.assertEqual(tags.count(), 2) # because we expect two tags to be assigned. self.assertIn(tag1, tags) self.assertIn(tag2, tags) # check if the tags that we created as our sample tags are # the same as the tags that are in our queryset. def test_create_recipe_with_ingredients(self): """Test creating recipe with ingredients""" ingredient1 = sample_ingredient(user=self.user, name='Ingredient 1') ingredient2 = sample_ingredient(user=self.user, name='Ingredient 2') payload = { 'title': 'Test recipe with ingredients', 'ingredients': [ingredient1.id, ingredient2.id], 'time_minutes': 45, 'price': 15.00 } res = self.client.post(RECIPES_URL, payload) self.assertEqual(res.status_code, status.HTTP_201_CREATED) recipe = Recipe.objects.get(id=res.data['id']) ingredients = recipe.ingredients.all() # get the ingredients queryset self.assertEqual(ingredients.count(), 2) self.assertIn(ingredient1, ingredients) self.assertIn(ingredient2, ingredients) # test partial update and full update of an object # there are two ways in which you can update an object using the # API there's two different HTTP methods: put, patch # patch: Patch is used to update the fields that are provided # in the payload so the only fields that it will change are the # fields that are provided and any fields that are omitted from # the request will not be modified in the object that's being updated. def test_partial_update_recipe(self): """Test updating a recipe with patch""" # make a request to change a field in our recipe. recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) # add a tag to the recipe new_tag = sample_tag(user=self.user, name='Curry') # add a new tag and what we're going to do is we're going # to swap out this tag that we create here and we're going # to replace it with a new tag payload = {'title': 'Partially Updated sample recipe', 'tags': [new_tag.id]} # tags will be replaced with this new tag so the existing tag that # we created won't be assigned to it url = detail_url(recipe.id) # the way that you update an object using the Django rest framework # view sets is you use the detail URL so that is the URL of the # recipe with the ID of the recipe that we want to update. self.client.patch(url, payload) # make request # We're going to retrieve an update to the recipe from the # database and then we're going to check the fields that # are assigned and just make sure they match what we expect. recipe.refresh_from_db() # refreshes the details in our recipe from the database # typically when you create a new model and you have a # reference to a model the details of that won't change # unless you do refresh from dB if the values have changed # in the database. self.assertEqual(recipe.title, payload['title']) tags = recipe.tags.all() self.assertEqual(len(tags), 1) self.assertIn(new_tag, tags) # check that the tag new tag is in the tags that we retrieved # test full update # put: it will replace the object that we're updating with the full # object that is provided in the request that means if you exclude # any fields in the payload those fields will actually be removed # from the object that you're updating def test_full_update_recipe(self): """Test updating a recipe with put""" recipe = sample_recipe(user=self.user) recipe.tags.add(sample_tag(user=self.user)) payload = { 'title': 'Fully Updated sample recipe', 'time_minutes': 25, 'price': 5.00 } url = detail_url(recipe.id) self.client.put(url, payload) recipe.refresh_from_db() self.assertEqual(recipe.title, payload['title']) self.assertEqual(recipe.time_minutes, payload['time_minutes']) self.assertEqual(recipe.price, payload['price']) tags = recipe.tags.all() self.assertEqual(len(tags), 0) # we will check that the tags assigned are zero now as I explained # because when we do a HTTP put if we omit a field # that should clear the value of that field so now our recipe # that did have a sample tag assigned should not have any tags # assigned class RecipeImageUploadTests(TestCase): # what happens at the setup of the test def setUp(self): self.client = APIClient() self.user = get_user_model().objects.create_user('user', 'testpass') self.client.force_authenticate(self.user) # authenticate our user self.recipe = sample_recipe(user=self.user) # after the test runs it runs tear down def tearDown(self): self.recipe.image.delete() # make sure that our file system is kept clean after our test # removing all of the test files that we create # delete the image if it exists in the recipe def test_upload_image_to_recipe(self): """Test uploading an image to recipe""" url = image_upload_url(self.recipe.id) # going to use the sample recipe that gets created # it creates a named temporary file on the system at a random # location usually in the /temp folder # create a temporary file we're going to write an image # to that file and then we're going to upload that file # through the API like you would with a HTTP POST and then # we're going to run some assertions to check that it # uploaded correctly with tempfile.NamedTemporaryFile(suffix='.jpg') as ntf: img = Image.new('RGB', (10, 10)) # creates black square img.save(ntf, format='JPEG') ntf.seek(0) # it's the way that Python reads files so because we've # saved the file it will be the seeking will be done to the # end of the file so if you try to access it then it would # just be blank because you've already read up to the end # of the file so use this seek function to set # the pointer back to the beginning of the file res = self.client.post(url, {'image': ntf}, format='multipart') # assertions # refreshing the database for our recipe self.recipe.refresh_from_db() self.assertEqual(res.status_code, status.HTTP_200_OK) # check that the images in the response so that's the path to # the image that should be accessible self.assertIn('image', res.data) # check that the path exists for the image that is saved to our model self.assertTrue(os.path.exists(self.recipe.image.path)) def test_upload_image_bad_request(self): """Test uploading an invalid image""" url = image_upload_url(self.recipe.id) res = self.client.post(url, {'image': 'notimage'}, format='multipart') self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST) def test_filter_recipes_by_tags(self): """Test returning recipes with specific tags""" recipe1 = sample_recipe(user=self.user, title='Thai vegetable curry') recipe2 = sample_recipe(user=self.user, title='Aubergine with tahini') tag1 = sample_tag(user=self.user, name='Vegan') tag2 = sample_tag(user=self.user, name='Vegetarian') recipe1.tags.add(tag1) recipe2.tags.add(tag2) recipe3 = sample_recipe(user=self.user, title='Fish and chips') res = self.client.get( RECIPES_URL, {'tags': '{},{}'.format(tag1.id, tag2.id)} ) # this will create a comma separated list string and assign # it to the tags get parameter # if our filtering is working # should only return the first two recipe # test the response: serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) # serialize the recipes and we're going to check if # they exist in the responses returned self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data) # check the return result def test_filter_recipes_by_ingredients(self): """Test returning recipes with specific ingredients""" recipe1 = sample_recipe(user=self.user, title='Posh beans on toast') recipe2 = sample_recipe(user=self.user, title='Chicken cacciatore') ingredient1 = sample_ingredient(user=self.user, name='Feta cheese') ingredient2 = sample_ingredient(user=self.user, name='Chicken') recipe1.ingredients.add(ingredient1) recipe2.ingredients.add(ingredient2) recipe3 = sample_recipe(user=self.user, title='Steak and mushrooms') # test API res = self.client.get( RECIPES_URL, {'ingredients': '{},{}'.format(ingredient1.id, ingredient2.id)} ) serializer1 = RecipeSerializer(recipe1) serializer2 = RecipeSerializer(recipe2) serializer3 = RecipeSerializer(recipe3) self.assertIn(serializer1.data, res.data) self.assertIn(serializer2.data, res.data) self.assertNotIn(serializer3.data, res.data)

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0

null

0

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0

0ae8c65cafc822a3267fba35c6ed220e7f320711

11,646

py

Python

gwcs/coordinate_frames.py

migueldvb/gwcs

4eb2abdb1d9d49ee10c1edbcae0d1cec4c758c39

[ "BSD-3-Clause" ]

null

gwcs/coordinate_frames.py

migueldvb/gwcs

4eb2abdb1d9d49ee10c1edbcae0d1cec4c758c39

[ "BSD-3-Clause" ]

null

gwcs/coordinate_frames.py

migueldvb/gwcs

4eb2abdb1d9d49ee10c1edbcae0d1cec4c758c39

[ "BSD-3-Clause" ]

null

# Licensed under a 3-clause BSD style license - see LICENSE.rst """ Defines coordinate frames and ties them to data axes. """ from __future__ import absolute_import, division, unicode_literals, print_function import numpy as np from astropy import units as u from astropy import utils as astutil from astropy import coordinates as coord from astropy.extern import six from . import utils as gwutils __all__ = ['Frame2D', 'CelestialFrame', 'SpectralFrame', 'CompositeFrame', 'CoordinateFrame'] STANDARD_REFERENCE_FRAMES = [frame.upper() for frame in coord.builtin_frames.__all__] STANDARD_REFERENCE_POSITION = ["GEOCENTER", "BARYCENTER", "HELIOCENTER", "TOPOCENTER", "LSR", "LSRK", "LSRD", "GALACTIC_CENTER", "LOCAL_GROUP_CENTER"] class CoordinateFrame(object): """ Base class for CoordinateFrames. Parameters ---------- naxes : int Number of axes. axes_type : str One of ["SPATIAL", "SPECTRAL", "TIME"] axes_order : tuple of int A dimension in the input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used with output_frame to convert to world coordinate objects). reference_position : str Reference position - one of `STANDARD_REFERENCE_POSITION` unit : list of astropy.units.Unit Unit for each axis. axes_names : list Names of the axes in this frame. name : str Name of this frame. """ def __init__(self, naxes, axes_type, axes_order, reference_frame=None, reference_position=None, unit=None, axes_names=None, name=None): self._naxes = naxes self._axes_order = tuple(axes_order) if isinstance(axes_type, six.string_types): self._axes_type = (axes_type,) else: self._axes_type = tuple(axes_type) self._reference_frame = reference_frame if unit is not None: if astutil.isiterable(unit): unit = tuple(unit) else: unit = (unit,) if len(unit) != naxes: raise ValueError("Number of units does not match number of axes.") else: self._unit = tuple([u.Unit(au) for au in unit]) if axes_names is not None: if isinstance(axes_names, six.string_types): axes_names = (axes_names,) else: axes_names = tuple(axes_names) if len(axes_names) != naxes: raise ValueError("Number of axes names does not match number of axes.") else: axes_names = tuple([""] * naxes) self._axes_names = axes_names if name is None: self._name = self.__class__.__name__ else: self._name = name if reference_position is not None: self._reference_position = reference_position else: self._reference_position = None super(CoordinateFrame, self).__init__() def __repr__(self): fmt = '<{0}(name="{1}", unit={2}, axes_names={3}, axes_order={4}'.format( self.__class__.__name__, self.name, self.unit, self.axes_names, self.axes_order) if self.reference_position is not None: fmt += ', reference_position="{0}"'.format(self.reference_position) if self.reference_frame is not None: fmt += ", reference_frame={0}".format(self.reference_frame) fmt += ")>" return fmt def __str__(self): if self._name is not None: return self._name else: return self.__class__.__name__ @property def name(self): """ A custom name of this frame.""" return self._name @name.setter def name(self, val): """ A custom name of this frame.""" self._name = val @property def naxes(self): """ The number of axes intheis frame.""" return self._naxes @property def unit(self): """The unit of this frame.""" return self._unit @property def axes_names(self): """ Names of axes in the frame.""" return self._axes_names @property def axes_order(self): """ A tuple of indices which map inputs to axes.""" return self._axes_order @property def reference_frame(self): return self._reference_frame @property def reference_position(self): try: return self._reference_position except AttributeError: return None def input_axes(self, start_frame=None): """ Computes which axes in `start_frame` contribute to each axis in the current frame. Parameters ---------- start_frame : ~gwcs.coordinate_frames.CoordinateFrame A frame in the WCS pipeline The transform between start_frame and the current frame is used to compute the mapping inputs: outputs. """ sep = self._separable(start_frame) inputs = [] for ax in self.axes_order: inputs.append(list(sep[ax].nonzero()[0])) return inputs @property def axes_type(self): """ Type of this frame : 'SPATIAL', 'SPECTRAL', 'TIME'. """ return self._axes_type def coordinates(self, *args): """ Create world coordinates object""" raise NotImplementedError("Subclasses may implement this") class CelestialFrame(CoordinateFrame): """ Celestial Frame Representation Parameters ---------- axes_order : tuple of int A dimension in the input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames A reference frame. reference_position : str Reference position. unit : str or units.Unit instance or iterable of those Units on axes. axes_names : list Names of the axes in this frame. name : str Name of this frame. """ def __init__(self, axes_order=None, reference_frame=None, unit=None, axes_names=None, name=None): naxes = 2 if reference_frame is not None: if reference_frame.name.upper() in STANDARD_REFERENCE_FRAMES: _axes_names = list(reference_frame.representation_component_names.values()) if 'distance' in _axes_names: _axes_names.remove('distance') if axes_names is None: axes_names = _axes_names naxes = len(_axes_names) _unit = list(reference_frame.representation_component_units.values()) if unit is None and _unit: unit = _unit if axes_order is None: axes_order = tuple(range(naxes)) if unit is None: unit = tuple([u.degree] * naxes) axes_type = ['SPATIAL'] * naxes super(CelestialFrame, self).__init__(naxes=naxes, axes_type=axes_type, axes_order=axes_order, reference_frame=reference_frame, unit=unit, axes_names=axes_names, name=name) def coordinates(self, *args): """ Create a SkyCoord object. Parameters ---------- args : float inputs to wcs.input_frame """ # Reorder axes if necesary. try: return coord.SkyCoord(*args, unit=self.unit, frame=self._reference_frame) except: raise class SpectralFrame(CoordinateFrame): """ Represents Spectral Frame Parameters ---------- axes_order : tuple or int A dimension in the input data that corresponds to this axis. reference_frame : astropy.coordinates.builtin_frames Reference frame (usually used with output_frame to convert to world coordinate objects). unit : str or units.Unit instance Spectral unit. axes_names : str Spectral axis name. name : str Name for this frame. """ def __init__(self, axes_order=(0,), reference_frame=None, unit=None, axes_names=None, name=None, reference_position=None): super(SpectralFrame, self).__init__(naxes=1, axes_type="SPECTRAL", axes_order=axes_order, axes_names=axes_names, reference_frame=reference_frame, unit=unit, name=name, reference_position=reference_position) def coordinates(self, *args): if np.isscalar(args): return args * self.unit[0] else: return args[0] * self.unit[0] class CompositeFrame(CoordinateFrame): """ Represents one or more frames. Parameters ---------- frames : list List of frames (TimeFrame, CelestialFrame, SpectralFrame, CoordinateFrame). name : str Name for this frame. """ def __init__(self, frames, name=None): self._frames = frames[:] naxes = sum([frame._naxes for frame in self._frames]) axes_type = list(range(naxes)) unit = list(range(naxes)) axes_names = list(range(naxes)) axes_order = [] for frame in frames: axes_order.extend(frame.axes_order) for frame in frames: for ind, axtype, un, n in zip(frame.axes_order, frame.axes_type, frame.unit, frame.axes_names): axes_type[ind] = axtype axes_names[ind] = n unit[ind] = un if len(np.unique(axes_order)) != len(axes_order): raise ValueError("Incorrect numbering of axes, " "axes_order should contain unique numbers, " "got {}.".format(axes_order)) super(CompositeFrame, self).__init__(naxes, axes_type=axes_type, axes_order=axes_order, unit=unit, axes_names=axes_names, name=name) @property def frames(self): return self._frames def __repr__(self): return repr(self.frames) def coordinates(self, *args): coo = [] for frame in self.frames: fargs = [args[i] for i in frame.axes_order] print(frame, fargs, frame.axes_order) coo.append(frame.coordinates(*fargs)) return coo class Frame2D(CoordinateFrame): """ A 2D coordinate frame. Parameters ---------- axes_order : tuple of int A dimension in the input data that corresponds to this axis. unit : list of astropy.units.Unit Unit for each axis. axes_names : list Names of the axes in this frame. name : str Name of this frame. """ def __init__(self, axes_order=(0, 1), unit=(u.pix, u.pix), axes_names=('x', 'y'), name=None): super(Frame2D, self).__init__(2, ["SPATIAL", "SPATIAL"], axes_order, name=name, axes_names=axes_names, unit=unit) def coordinates(self, *args): args = [args[i] for i in self.axes_order] coo = tuple([arg * un for arg, un in zip(args, self.unit)]) return coo

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100

32.805634

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false

0

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0.015464

0.28866

0.010309

0

null

0

null

0

1

0

0aeade2b44478bdc750fc6e4297d377345ef5136

500

py

Python

brownie_fund_me/scripts/fund_and_withdraw.py

WangCHEN9/solidity_demos

cf28111a1e972ab9dde70f6d3fac22c897d8b660

[ "MIT" ]

null

brownie_fund_me/scripts/fund_and_withdraw.py

WangCHEN9/solidity_demos

cf28111a1e972ab9dde70f6d3fac22c897d8b660

[ "MIT" ]

null

brownie_fund_me/scripts/fund_and_withdraw.py

WangCHEN9/solidity_demos

cf28111a1e972ab9dde70f6d3fac22c897d8b660

[ "MIT" ]

null

from brownie import FundMe from scripts.helpful_scripts import get_account def fund(): fund_me = FundMe[-1] account = get_account() entrance_fee = fund_me.getEntranceFee() print(f"entrance is {entrance_fee}") print("funding..") fund_me.fund({"from": account, "value": entrance_fee}) def withdraw(): fund_me = FundMe[-1] account = get_account() fund_me.withdraw({"from": account}) def main(): fund() withdraw() if __name__ == "__main__": main()

18.518519

58

0.654

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500

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0.380952

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0.084691

0.19544

0

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0.204

500

26

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false

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0.111111

0

null

0

null

0

1

0

0aeb5c0e9a64382d41d3447557ec9fb64a32a973

409

py

Python

ex019.py

jefernathan/Python

2f840a625e8d46d41ab36df07ef50ae15a03c5ab

[ "MIT" ]

null

ex019.py

jefernathan/Python

2f840a625e8d46d41ab36df07ef50ae15a03c5ab

[ "MIT" ]

null

ex019.py

jefernathan/Python

2f840a625e8d46d41ab36df07ef50ae15a03c5ab

[ "MIT" ]

null

# Um professor quer sortear um dos seus quatro alunos para apagar o quadro. Faça um programa que ajude ele, lendo o nome dos alunos e escrevendo na tela o nome do escolhido. from random import choice nome1 = input('Digite um nome: ') nome2 = input('Digite outro nome: ') nome3 = input('Digite mais um nome: ') nome4 = input('Digite o último nome: ') nome = [nome1, nome2, nome3, nome4] print(choice(nome))

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null

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null

0

1

0

0aeb7979679122962a3fff866f48391b6b9c9278

489

py

Python

contacts/admin.py

liviamendes/agenda-django-project

d602bb5e762ea477c3c97b5a475ad79036c0c93d

[ "MIT" ]

null

contacts/admin.py

liviamendes/agenda-django-project

d602bb5e762ea477c3c97b5a475ad79036c0c93d

[ "MIT" ]

null

contacts/admin.py

liviamendes/agenda-django-project

d602bb5e762ea477c3c97b5a475ad79036c0c93d

[ "MIT" ]

null

from django.contrib import admin from .models import Categoria, Contact class ContactAdmin(admin.ModelAdmin): list_display = ('id', 'name', 'last_name', 'phone', 'email', 'creation_date', 'categoria', 'show') list_display_links = ('id', 'name', 'last_name') list_filter = ('categoria',) list_per_page = 10 search_fields = ('name', 'last_name', 'phone') list_editable = ('phone', 'show') admin.site.register(Categoria) admin.site.register(Contact, ContactAdmin)

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null

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null

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0

0aec7fad0f474867079a857e5fa0aa0966e20a00

2,472

py

Python

upload_from_folder.py

robinrobinzon/fastpic

966f1aa8c6d7e98651727e7ed7f6b25970d5da11

[ "MIT" ]

null

upload_from_folder.py

robinrobinzon/fastpic

966f1aa8c6d7e98651727e7ed7f6b25970d5da11

[ "MIT" ]

null

upload_from_folder.py

robinrobinzon/fastpic

966f1aa8c6d7e98651727e7ed7f6b25970d5da11

[ "MIT" ]

null

import datetime import os import shutil import tempfile from joblib import Parallel, delayed from fastpic_upload import upload_file_to_fastpic _n_jobs_for_upload = 20 _root_folders_set = ( '/path/to/folder', ) _spoiler_for_each_file = True def process_one_pic(result_key, pic_path, tmp_dir): pic_url, pic_link = upload_file_to_fastpic(pic_path, tmp_dir) print(pic_url) return result_key, (pic_url, pic_link) def upload_from_folder(folder_path): pics_to_upload = {} for root, dirs, files in os.walk(folder_path): for file in files: if file.split('.')[-1] not in ('jpg', 'jpeg', 'bmp', 'png'): continue file_path = os.path.join(root, file) pics_to_upload[file] = file_path print(pics_to_upload) print('Need upload {} photo'.format(len(pics_to_upload))) result = {} tmp_dir = tempfile.mkdtemp() try: sub_results = Parallel(n_jobs=_n_jobs_for_upload, backend='threading')( delayed(process_one_pic)(key, pics_to_upload[key], tmp_dir) for key in sorted(pics_to_upload)) for sub_result in sub_results: result[sub_result[0]] = sub_result[1] finally: shutil.rmtree(tmp_dir) return result def print_result_to_file(result, result_file_path): with open(result_file_path, 'w', encoding='utf8', newline='') as codes_file: codes_file.write('[spoiler="Скриншоты"]') codes_file.write(os.linesep) codes_file.write(os.linesep) for result_key in sorted(result): if _spoiler_for_each_file: codes_file.write('[spoiler="{}"]'.format(result_key)) codes_file.write(os.linesep) url, link = result[result_key] codes_file.write('[url={}][img]{}[/img][/url]'.format(link, url)) if _spoiler_for_each_file: codes_file.write(os.linesep) codes_file.write('[/spoiler]') codes_file.write(os.linesep) codes_file.write(os.linesep) codes_file.write('[/spoiler]') def main(): for root_folder in _root_folders_set: result = upload_from_folder(root_folder) print_result_to_file(result, os.path.join(root_folder, 'result_codes.txt')) if __name__ == '__main__': started = datetime.datetime.now() print(started, 'started') main() finished = datetime.datetime.now() print(finished, 'all done in', finished - started)

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null

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null

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0aecc3617c0fed4d5c58d568836e4b90d9b9886f

1,994

py

Python

tools/accuracy_checker/openvino/tools/accuracy_checker/postprocessor/clip_segmentation_mask.py

TolyaTalamanov/open_model_zoo

1697e60712df4ca72635a2080a197b9d3bc24129

[ "Apache-2.0" ]

2,201

2018-10-15T14:37:19.000Z

2020-07-16T02:05:51.000Z

tools/accuracy_checker/openvino/tools/accuracy_checker/postprocessor/clip_segmentation_mask.py

Pandinosaurus/open_model_zoo

2543996541346418919c5cddfb71e33e2cdef080

[ "Apache-2.0" ]

759

2018-10-18T07:43:55.000Z

2020-07-16T01:23:12.000Z

tools/accuracy_checker/openvino/tools/accuracy_checker/postprocessor/clip_segmentation_mask.py

Pandinosaurus/open_model_zoo

2543996541346418919c5cddfb71e33e2cdef080

[ "Apache-2.0" ]

808

2018-10-16T14:03:49.000Z

2020-07-15T11:41:45.000Z

""" Copyright (c) 2018-2022 Intel Corporation Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import numpy as np from .postprocessor import PostprocessorWithSpecificTargets from ..representation import BrainTumorSegmentationAnnotation, BrainTumorSegmentationPrediction from ..config import NumberField, ConfigError class ClipSegmentationMask(PostprocessorWithSpecificTargets): __provider__ = 'clip_segmentation_mask' annotation_types = (BrainTumorSegmentationAnnotation, ) prediction_types = (BrainTumorSegmentationPrediction, ) @classmethod def parameters(cls): parameters = super().parameters() parameters.update({ 'min_value': NumberField(value_type=int, min_value=0, optional=True, default=0, description="Min value"), 'max_value': NumberField(value_type=int, description="Max value") }) return parameters def configure(self): self.min_value = self.get_value_from_config('min_value') self.max_value = self.get_value_from_config('max_value') if self.max_value < self.min_value: raise ConfigError('max_value should be greater than min_value') def process_image(self, annotation, prediction): for target in annotation: target.mask = np.clip(target.mask, a_min=self.min_value, a_max=self.max_value) for target in prediction: target.mask = np.clip(target.mask, a_min=self.min_value, a_max=self.max_value) return annotation, prediction

38.346154

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1,994

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0.080668

0

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1,994

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0.481481

0

null

0

null

0

1

0

0aee1a078e80effb05eed8b8321db099a4b35623

1,925

py

Python

tests/test_utils.py

isabella232/pynacl

b3f6c320569d858ba61d4bdf2ac788564528c1c9

[ "Apache-2.0" ]

756

2015-01-03T17:49:44.000Z

2022-03-31T13:54:33.000Z

tests/test_utils.py

isabella232/pynacl

b3f6c320569d858ba61d4bdf2ac788564528c1c9

[ "Apache-2.0" ]

540

2015-01-02T10:54:33.000Z

2022-03-05T18:47:01.000Z

tests/test_utils.py

isabella232/pynacl

b3f6c320569d858ba61d4bdf2ac788564528c1c9

[ "Apache-2.0" ]

217

2015-01-09T00:48:01.000Z

2022-03-26T08:53:32.000Z

# Copyright 2013 Donald Stufft and individual contributors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import pytest import nacl.secret import nacl.utils def test_random_bytes_produces(): assert len(nacl.utils.random(16)) == 16 def test_random_bytes_produces_different_bytes(): assert nacl.utils.random(16) != nacl.utils.random(16) def test_string_fixer(): assert str(nacl.secret.SecretBox(b"\x00" * 32)) == str(b"\x00" * 32) def test_deterministic_random_bytes(): expected = ( b"0d8e6cc68715648926732e7ea73250cfaf2d58422083904c841a8ba" b"33b986111f346ba50723a68ae283524a6bded09f83be6b80595856f" b"72e25b86918e8b114bafb94bc8abedd73daab454576b7c5833eb0bf" b"982a1bb4587a5c970ff0810ca3b791d7e12" ) seed = ( b"\x00\x01\x02\x03\x04\x05\x06\x07\x08\x09\x0a\x0b\x0c\x0d" b"\x0e\x0f\x10\x11\x12\x13\x14\x15\x16\x17\x18\x19\x1a\x1b" b"\x1c\x1d\x1e\x1f" ) assert ( nacl.utils.randombytes_deterministic( 100, seed, encoder=nacl.utils.encoding.HexEncoder ) == expected ) def test_deterministic_random_bytes_invalid_seed_length(): expected = "Deterministic random bytes must be generated from 32 bytes" seed = b"\x00\x01\x02\x03\x04\x05\x06\x07\x08\x09\x0a" with pytest.raises(TypeError) as e: nacl.utils.randombytes_deterministic(100, seed) assert expected in str(e.value)

32.083333

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0.196364

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1,925

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0

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0.090909

0

0.242424

0

null

0

null

0

1

0

0aefad001e36b9eae9b3eb392972175239563b8d

2,893

py

Python

guesstheword.py

Cha0sNation/RandomPython

7ba41d78f27bd90e9c09efcd4d5c26eac93e74ec

[ "MIT" ]

null

guesstheword.py

Cha0sNation/RandomPython

7ba41d78f27bd90e9c09efcd4d5c26eac93e74ec

[ "MIT" ]

null

guesstheword.py

Cha0sNation/RandomPython

7ba41d78f27bd90e9c09efcd4d5c26eac93e74ec

[ "MIT" ]

null

#! /home/cha0snation/anaconda3/bin/python import random def setup(): words = ["banana", "apple", "orange", "peach", "grape", "watermelon"] output = [] word = words[random.randint(0, len(words) - 1)] playing = True tries = 5 return [words, output, word, tries, playing] def check_finished(output, tries): if tries == 0: print("You ran out of tries") print() return True count = 0 for letter in output: if letter != "_": count += 1 if count == len(output): print_output(output) print() print() return True return False def check_letter(letter, word, tries): correct = False for index, letter in enumerate(word): if letter == guess: output[index] = guess correct = True if index == len(word) - 1: if not correct: print("Incorrect guess") print() return tries - 1 else: return tries def check_same(guess, output): same = False for i in output: if i == guess: same = True if same: print("You already found that letter") print() print_output(output) print() print() while True: guess = str(input("Guess: ")) if len(guess) == 1: break return guess else: return guess def print_output(output): for i in output: print("{0} ".format(i), end="") if __name__ == "__main__": words, output, word, tries, playing = setup() while playing: print("Try to guess the word:") if tries == 1: print("You have {0} try left.".format(tries)) else: print("You have {0} tries left.".format(tries)) # print("DEBUG: word is {0}".format(word)) if output == []: for i in word: output.append("_") for i in range(len(output)): print("_ ", end="") else: print_output(output) print() print() try: while True: guess = str(input("Guess: ")) if len(guess) == 1: break except (EOFError, KeyboardInterrupt): print() break except ValueError: print("Invalid guess") break print() guess = check_same(guess, output) tries = check_letter(guess, word, tries) if check_finished(output, tries): choice = input("Do you want to play again ? (y or n): ") print() if choice.lower().startswith("y"): words, output, word, tries, playing = setup() else: playing = False

24.726496

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2,893

116

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0.263158

0

null

0

null

0

1

0

0af0f43e75ad092a7a05698be61aa6dca9c4178e

2,131

py

Python

web_app/index.py

svakulenk0/ArtDATIS

29e646f7bcb931e733ee248cc973411ffb18be64

[ "MIT" ]

null

web_app/index.py

svakulenk0/ArtDATIS

29e646f7bcb931e733ee248cc973411ffb18be64

[ "MIT" ]

9

2020-03-24T17:57:03.000Z

2022-03-12T00:08:07.000Z

web_app/index.py

svakulenk0/ArtDATIS

29e646f7bcb931e733ee248cc973411ffb18be64

[ "MIT" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- ''' Created on Dec 8, 2019 .. codeauthor: svitlana vakulenko <svitlana.vakulenko@gmail.com> Index docs into ES https://qbox.io/blog/building-an-elasticsearch-index-with-python ''' from settings import * import glob import re # n first characters for the doc preview LIMIT_START = 100 txts_path = '%s/artdatis/tagging/OCRed/typed/' % DATA_PATH text_corpus = [] def corpus_iterator(): # filter out and collect text files for file_path in glob.glob(txts_path+'*_text.txt'): with open(file_path, encoding="utf-8") as file: text = file.read() # filter duplicates if text not in text_corpus: text_corpus.append(text) text = re.sub(' +', ' ', text) start_text = text.lstrip()[:LIMIT_START] with open(file_path.split('_text.txt')[0]+'_path.txt') as path_file: path = path_file.read().strip().replace(DATA_PATH, '/images') yield { "_index": INDEX_NAME, "_type": TYPE_NAME, "_source": {"file_path": path, "text": text, "start_text": start_text}, } print("Loaded %d documents"%len(text_corpus)) from elasticsearch import Elasticsearch from elasticsearch.helpers import bulk # create ES client, create index es = Elasticsearch(hosts = [ES_HOST]) if es.indices.exists(INDEX_NAME): print("deleting '%s' index..." % (INDEX_NAME)) res = es.indices.delete(index = INDEX_NAME) print(" response: '%s'" % (res)) request_body = { "settings" : { "number_of_shards": 1, "number_of_replicas": 0 } } print("creating '%s' index..." % (INDEX_NAME)) res = es.indices.create(index = INDEX_NAME, body = request_body) print(" response: '%s'" % (res)) # bulk index the data print("bulk indexing...") bulk(es, corpus_iterator()) # sanity check res = es.search(index = INDEX_NAME, size=2, body={"query": {"match_all": {}}}) print("results:") for hit in res['hits']['hits']: print(hit["_source"])

30.014085

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null

0

null

0

1

0

0af106828dec53475f13db7b60f12e654896ac46

277

py

Python

src/tokens.py

PythonIsMagic/ponyup

3b2630d573cd46d0569f713c6d4c3790688dc62d

[ "MIT" ]

1

2022-03-22T12:41:35.000Z

src/tokens.py

PythonIsMagic/ponyup

3b2630d573cd46d0569f713c6d4c3790688dc62d

[ "MIT" ]

null

src/tokens.py

PythonIsMagic/ponyup

3b2630d573cd46d0569f713c6d4c3790688dc62d

[ "MIT" ]

1

2022-03-22T12:41:37.000Z

""" A Token is a button or other object on the table that represents a position, a game state, layer state, or some other piece of info """ class Token(object): def __init__(self, name, table): self.table = table self.name = name self.seat = None

25.181818

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277

10

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0

0.4

0

null

0

null

0

1

0

0af1366c588c694d1d5fccc2c589b64a4b89883f

1,089

py

Python

Chapter09/interpolation_search.py

Xiangs18/Algorithms-with-Python-Second-Edition

96844e1ae7054e099772dc691c1f41f15c2bfba5

[ "MIT" ]

null

Chapter09/interpolation_search.py

Xiangs18/Algorithms-with-Python-Second-Edition

96844e1ae7054e099772dc691c1f41f15c2bfba5

[ "MIT" ]

null

Chapter09/interpolation_search.py

Xiangs18/Algorithms-with-Python-Second-Edition

96844e1ae7054e099772dc691c1f41f15c2bfba5

[ "MIT" ]

null

def nearest_mid(input_list, lower_bound_index, upper_bound_index, search_value): return lower_bound_index + ( (upper_bound_index - lower_bound_index) // (input_list[upper_bound_index] - input_list[lower_bound_index]) ) * (search_value - input_list[lower_bound_index]) def interpolation_search(ordered_list, term): size_of_list = len(ordered_list) - 1 index_of_first_element = 0 index_of_last_element = size_of_list while index_of_first_element <= index_of_last_element: mid_point = nearest_mid( ordered_list, index_of_first_element, index_of_last_element, term ) if mid_point > index_of_last_element or mid_point < index_of_first_element: return None if ordered_list[mid_point] == term: return mid_point if term > ordered_list[mid_point]: index_of_first_element = mid_point + 1 else: index_of_last_element = mid_point - 1 store = [2, 4, 5, 12, 43, 54, 60, 77] a = interpolation_search(store, 2) print("Index position of value 2 is ", a)

37.551724

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0.693297

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0.102639

0.109971

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0.409091

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0.108504

0

0.0227

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0.041667

0

null

0

null

0

1

0

0af230c3ec87bec2b40fe4cc74ba6765304b22f0

13,752

py

Python

src/macro_pack.py

lulinsheng/macro_pack

4e9d0178354bad2aa557298f44ba5d4385a72a2b

[ "Apache-2.0" ]

null

src/macro_pack.py

lulinsheng/macro_pack

4e9d0178354bad2aa557298f44ba5d4385a72a2b

[ "Apache-2.0" ]

null

src/macro_pack.py

lulinsheng/macro_pack

4e9d0178354bad2aa557298f44ba5d4385a72a2b

[ "Apache-2.0" ]

null

#!/usr/bin/python3 # encoding: utf-8 import os import sys import getopt import logging import shutil import psutil from modules.com_run import ComGenerator from modules.web_server import ListenServer from modules.Wlisten_server import WListenServer from modules.payload_builder_factory import PayloadBuilderFactory from common import utils, mp_session, help from common.utils import MSTypes from common.definitions import VERSION, LOGLEVEL if sys.platform == "win32": try: import win32com.client #@UnresolvedImport @UnusedImport except: print("Error: Could not find win32com.") sys.exit(1) MP_TYPE="Pro" if utils.checkModuleExist("pro_core"): from pro_modules.utilities.dcom_run import DcomGenerator from pro_modules.payload_builders.containers import ContainerGenerator from pro_core.payload_builder_factory_pro import PayloadBuilderFactoryPro from pro_core import arg_mgt_pro, mp_session_pro else: MP_TYPE="Community" from colorama import init from termcolor import colored # {PyArmor Protection Code} # {PyArmor Plugins} # use Colorama to make Termcolor work on Windows too init() WORKING_DIR = "temp" BANNER = help.getToolPres() def main(argv): global MP_TYPE logLevel = LOGLEVEL # initialize macro_pack session object working_directory = os.path.join(os.getcwd(), WORKING_DIR) if MP_TYPE == "Pro": mpSession = mp_session_pro.MpSessionPro(working_directory, VERSION, MP_TYPE) else: mpSession = mp_session.MpSession(working_directory, VERSION, MP_TYPE) try: longOptions = ["embed=", "listen=", "port=", "webdav-listen=", "generate=", "quiet", "input-file=", "encode", "obfuscate", "obfuscate-form", "obfuscate-names", "obfuscate-declares", "obfuscate-strings", "obfuscate-names-charset=", "obfuscate-names-minlen=", "obfuscate-names-maxlen=", "file=","template=","listtemplates","listformats","icon=", "start-function=","uac-bypass", "unicode-rtlo=", "dde", "print", "force-yes", "help"] shortOptions= "e:l:w:s:f:t:G:hqmop" # only for Pro release if MP_TYPE == "Pro": longOptions.extend(arg_mgt_pro.proArgsLongOptions) shortOptions += arg_mgt_pro.proArgsShortOptions # Only enabled on windows if sys.platform == "win32": longOptions.extend(["run=", "run-visible"]) opts, args = getopt.getopt(argv, shortOptions, longOptions) # @UnusedVariable except getopt.GetoptError: help.printUsage(BANNER, sys.argv[0]) sys.exit(2) for opt, arg in opts: if opt in ("-o", "--obfuscate"): mpSession.obfuscateForm = True mpSession.obfuscateNames = True mpSession.obfuscateStrings = True mpSession.obfuscateDeclares = True elif opt=="--obfuscate-form": mpSession.obfuscateForm = True elif opt=="--obfuscate-declares": mpSession.obfuscateDeclares = True elif opt=="--obfuscate-names": mpSession.obfuscateNames = True elif opt=="--obfuscate-names-charset": try: mpSession.obfuscatedNamesCharset = arg except ValueError: help.printUsage(BANNER, sys.argv[0]) sys.exit(0) elif opt=="--obfuscate-names-minlen": try: mpSession.obfuscatedNamesMinLen = int(arg) except ValueError: help.printUsage(BANNER, sys.argv[0]) sys.exit(0) if mpSession.obfuscatedNamesMinLen < 4 or mpSession.obfuscatedNamesMinLen > 255: help.printUsage(BANNER, sys.argv[0]) sys.exit(0) elif opt=="--obfuscate-names-maxlen": try: mpSession.obfuscatedNamesMaxLen = int(arg) except ValueError: help.printUsage(BANNER, sys.argv[0]) sys.exit(0) if mpSession.obfuscatedNamesMaxLen < 4 or mpSession.obfuscatedNamesMaxLen > 255: help.printUsage(BANNER, sys.argv[0]) sys.exit(0) elif opt=="--obfuscate-strings": mpSession.obfuscateStrings = True elif opt=="-s" or opt=="--start-function": mpSession.startFunction = arg elif opt=="-l" or opt=="--listen": mpSession.listen = True mpSession.listenRoot = os.path.abspath(arg) elif opt=="--port": mpSession.listenPort = int(arg) mpSession.WlistenPort = int(arg) elif opt=="--icon": mpSession.icon = arg elif opt=="-w" or opt=="--webdav-listen": mpSession.Wlisten = True mpSession.WRoot = os.path.abspath(arg) elif opt == "-f" or opt== "--input-file": mpSession.fileInput = arg elif opt == "-e" or opt== "--embed": mpSession.embeddedFilePath = os.path.abspath(arg) elif opt=="-t" or opt=="--template": mpSession.template = arg elif opt == "--listtemplates": help.printTemplatesUsage(BANNER, sys.argv[0]) sys.exit(0) elif opt=="-q" or opt=="--quiet": logLevel = "WARN" elif opt=="-p" or opt=="--print": mpSession.printFile = True elif opt == "--dde": if sys.platform == "win32": mpSession.ddeMode = True elif opt == "--run": if sys.platform == "win32": mpSession.runTarget = os.path.abspath(arg) elif opt == "--run-visible": if sys.platform == "win32": mpSession.runVisible = True elif opt == "--force-yes": mpSession.forceYes = True elif opt=="--uac-bypass": mpSession.uacBypass = True elif opt == "--unicode-rtlo": mpSession.unicodeRtlo = arg elif opt in ("-G", "--generate"): mpSession.outputFilePath = os.path.abspath(arg) elif opt == "--listformats": help.printAvailableFormats(BANNER) sys.exit(0) elif opt=="-h" or opt=="--help": help.printUsage(BANNER, sys.argv[0]) sys.exit(0) else: if MP_TYPE == "Pro": arg_mgt_pro.processProArg(opt, arg, mpSession, BANNER) else: help.printUsage(BANNER, sys.argv[0]) sys.exit(0) if logLevel == "INFO": os.system('cls' if os.name == 'nt' else 'clear') # Logging logging.basicConfig(level=getattr(logging, logLevel),format="%(message)s", handlers=[utils.ColorLogFiler()]) logging.info(colored(BANNER, 'green')) logging.info(" [+] Preparations...") # check input args if mpSession.fileInput is None: # Argument not supplied, try to get file content from stdin if not os.isatty(0): # check if something is being piped logging.info(" [-] Waiting for piped input feed...") mpSession.stdinContent = sys.stdin.readlines() # Close Stdin pipe, so we can call input() later without triggering EOF #sys.stdin.close() if sys.platform == "win32": sys.stdin = open("conIN$") else: sys.stdin = sys.__stdin__ else: if not os.path.isfile(mpSession.fileInput): logging.error(" [!] ERROR: Could not find %s!" % mpSession.fileInput) sys.exit(2) else: logging.info(" [-] Input file path: %s" % mpSession.fileInput) if MP_TYPE == "Pro": if mpSession.communityMode: logging.warning(" [!] Running in community mode (pro features not applied)") MP_TYPE="Community" else: arg_mgt_pro.verify(mpSession) # Check output file format if mpSession.outputFilePath: if not os.path.isdir(os.path.dirname(mpSession.outputFilePath)): logging.error(" [!] Could not find output folder %s." % os.path.dirname(mpSession.outputFilePath)) sys.exit(2) if mpSession.outputFileType == MSTypes.UNKNOWN: logging.error(" [!] %s is not a supported extension. Use --listformats to view supported MacroPack formats." % os.path.splitext(mpSession.outputFilePath)[1]) sys.exit(2) else: logging.info(" [-] Target output format: %s" % mpSession.outputFileType) elif not mpSession.listen and not mpSession.Wlisten and mpSession.runTarget is None and (MP_TYPE != "Pro" or mpSession.dcomTarget is None): logging.error(" [!] You need to provide an output file! (get help using %s -h)" % os.path.basename(utils.getRunningApp())) sys.exit(2) if not mpSession.isTrojanMode: # verify that output file does not already exist if os.path.isfile(mpSession.outputFilePath): logging.error(" [!] ERROR: Output file %s already exist!" % mpSession.outputFilePath) sys.exit(2) #Create temporary folder logging.info(" [-] Temporary working dir: %s" % working_directory) if not os.path.exists(working_directory): os.makedirs(working_directory) try: # Create temporary work file. if mpSession.ddeMode or mpSession.template or (mpSession.outputFileType not in MSTypes.VB_FORMATS+[MSTypes.VBA] and not mpSession.htaMacro): inputFile = os.path.join(working_directory, "command.cmd") else: inputFile = os.path.join(working_directory, utils.randomAlpha(9)) + ".vba" if mpSession.stdinContent is not None: import time time.sleep(0.4) # Needed to avoid some weird race condition logging.info(" [-] Store std input in file...") f = open(inputFile, 'w') f.writelines(mpSession.stdinContent) f.close() else: # Create temporary work file if mpSession.fileInput is not None: # Check there are not binary chars in input fil if utils.isBinaryString(open(mpSession.fileInput, 'rb').read(1024)): logging.error(" [!] ERROR: Invalid format for %s. Input should be text format containing your VBA script." % mpSession.fileInput) logging.info(" [+] Cleaning...") if os.path.isdir(working_directory): shutil.rmtree(working_directory) sys.exit(2) logging.info(" [-] Store input file...") shutil.copy2(mpSession.fileInput, inputFile) if os.path.isfile(inputFile): logging.info(" [-] Temporary input file: %s" % inputFile) # Edit outputfile name to spoof extension if unicodeRtlo option is enabled if mpSession.unicodeRtlo: # Reminder; mpSession.unicodeRtlo contains the extension we want to spoof, such as "jpg" logging.info(" [+] Inject %s false extension with unicode RTLO" % mpSession.unicodeRtlo) # Separate document path and extension (fileName, fileExtension) = os.path.splitext(mpSession.outputFilePath) logging.info(" [-] Extension %s " % fileExtension) # Append unicode RTLO to file name fileName += '\u202e' # Append extension to spoof in reverse order fileName += '\u200b' + mpSession.unicodeRtlo[::-1] # Prepend invisible space so filename does not end with flagged extension # Append file extension fileName += fileExtension mpSession.outputFilePath = fileName logging.info(" [-] File name modified to: %s" % mpSession.outputFilePath) # Retrieve the right payload builder if mpSession.outputFileType != MSTypes.UNKNOWN: if MP_TYPE == "Pro" and not mpSession.communityMode: payloadBuilder = PayloadBuilderFactoryPro().getPayloadBuilder(mpSession) else: payloadBuilder = PayloadBuilderFactory().getPayloadBuilder(mpSession) # Build payload if payloadBuilder is not None: payloadBuilder.run() if MP_TYPE == "Pro": generator = ContainerGenerator(mpSession) generator.run() #run com attack if mpSession.runTarget: generator = ComGenerator(mpSession) generator.run() if MP_TYPE == "Pro": #run dcom attack if mpSession.dcom: generator = DcomGenerator(mpSession) generator.run() # Activate Web server if mpSession.listen: listener = ListenServer(mpSession) listener.run() # Activate WebDav server if mpSession.Wlisten: Wlistener = WListenServer(mpSession) Wlistener.run() except Exception: logging.exception(" [!] Exception caught!") except KeyboardInterrupt: logging.error(" [!] Keyboard interrupt caught!") logging.info(" [+] Cleaning...") if os.path.isdir(working_directory): shutil.rmtree(working_directory) logging.info(" Done!\n") sys.exit(0) if __name__ == '__main__': # check if running from explorer, if yes restart from cmd line # running_from = psutil.Process(os.getpid()).parent().parent().name() # if running_from == 'explorer.exe': # os.system("cmd.exe /k \"%s\"" % utils.getRunningApp()) # PyArmor Plugin: checkPlug() main(sys.argv[1:])

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0.053846

0

null

0

null

0

1

0

0af340336c716992b681bade66c39e840439919b

6,148

py

Python

etl/load/elasticsearch.py

bilalelhoudaigui/plant-brapi-etl-data-lookup-gnpis

973dc444eac6d1cc80c020dd8b9a4656f70eeafb

[ "BSD-3-Clause" ]

3

2018-06-04T09:14:55.000Z

2018-10-25T14:32:03.000Z

etl/load/elasticsearch.py

bilalelhoudaigui/plant-brapi-etl-data-lookup-gnpis

973dc444eac6d1cc80c020dd8b9a4656f70eeafb

[ "BSD-3-Clause" ]

18

2020-06-04T07:08:17.000Z

2022-02-02T17:02:17.000Z

etl/load/elasticsearch.py

bilalelhoudaigui/plant-brapi-etl-data-lookup-gnpis

973dc444eac6d1cc80c020dd8b9a4656f70eeafb

[ "BSD-3-Clause" ]

4

2019-04-18T12:53:19.000Z

2019-11-22T08:53:19.000Z

# Load json bulk files into elasticsearch import json import os import time import traceback import elasticsearch from etl.common.store import list_entity_files from etl.common.utils import get_folder_path, get_file_path, create_logger, first, replace_template class ElasticSearchException(Exception): pass # Init Elasticsearch and test connection def init_es_client(url, logger): es_client = elasticsearch.Elasticsearch([url]) try: info = es_client.info() logger.debug('Connected to node "{}" of cluster "{}" on "{}"'.format(info['name'], info['cluster_name'], url)) except elasticsearch.exceptions.ConnectionError as e: logger.error('Connection error: Elasticsearch unavailable on "{}".\nPlease check your configuration'.format(url)) raise e return es_client def check_error(response): if response.get('errors'): raise ElasticSearchException(response) def create_index(es_client, index_name, logger): logger.debug('Creating index "{}"...'.format(index_name)) check_error(es_client.indices.create(index_name)) def delete_index(es_client, index_name, logger): logger.debug('Deleting index "{}"...'.format(index_name)) check_error(es_client.indices.delete(index_name)) def create_template(es_client, es_config, document_type, base_index_name, logger): template_name = 'template_elixir_' + base_index_name template_pattern = base_index_name + '-d*' mapping = es_config['document-mappings'].get(document_type+"_mapping") if not mapping: return logger.debug('Creating template "{}" on pattern "{}"...'.format(template_name, template_pattern)) template_body = {'template': template_pattern, 'mappings': mapping} if 'index-settings' in es_config: template_body['settings'] = es_config['index-settings'] check_error(es_client.indices.put_template(name=template_name, body=template_body)) def bulk_index(es_client, index_name, file_path, logger): file_name = os.path.basename(file_path) logger.debug('Bulk indexing file "{}" in index "{}"...'.format(file_name, index_name)) with open(file_path, 'r') as file: check_error(es_client.bulk(index=index_name, body=file.read(), timeout='2000ms')) def create_alias(es_client, alias_name, base_index_name, logger): logger.debug('Creating alias "{}" for index "{}"'.format(alias_name, base_index_name)) check_error(es_client.indices.put_alias(alias_name, base_index_name)) def get_indices(es_client, base_index_name): indices = es_client.cat.indices(base_index_name + '-d*', params={'h': 'index'}) index_names = list(map(lambda i: i['index'], indices)) index_names.sort(reverse=True) return index_names def load_source(source, config, source_bulk_dir, log_dir): """ Full Elasticsearch documents indexing """ source_name = source['schema:identifier'] action = 'load-elasticsearch-' + source_name log_file = get_file_path([log_dir, action], ext='.log', recreate=True) logger = create_logger(source_name, log_file, config['options']['verbose']) load_config = config['load-elasticsearch'] es_client = init_es_client(load_config['url'], logger) logger.info("Loading '{}' into elasticsearch '{}'...".format(source_bulk_dir, load_config['url'])) try: if not os.path.exists(source_bulk_dir): raise FileNotFoundError( 'No such file or directory: \'{}\'.\n' 'Please make sure you have run the BrAPI extraction and Elasticsearch document transformation' ' before trying to launch the transformation process.' .format(source_bulk_dir)) bulk_files = list(list_entity_files(source_bulk_dir)) all_document_types = set(map(first, bulk_files)) document_types = load_config.get('document-types') or all_document_types document_types = document_types.intersection(all_document_types) index_by_document = dict() logger.info("Preparing index with template mapping...") timestamp = int(time.time()) for document_type in document_types: base_index_name = replace_template( load_config['index-template'], {'source': source['schema:identifier'], 'documentType': document_type} ).lower() create_template(es_client, load_config, document_type, base_index_name, logger) index_name = base_index_name + '-d' + str(timestamp) create_index(es_client, index_name, logger) index_by_document[document_type] = base_index_name, index_name logger.info("Bulk indexing...") for document_type, file_path in bulk_files: if document_type in index_by_document: base_index_name, index_name = index_by_document[document_type] bulk_index(es_client, index_name, file_path, logger) logger.info("Creating index aliases and deleting old indices...") for document_type, (base_index_name, index_name) in index_by_document.items(): create_alias(es_client, index_name, base_index_name, logger) new_index, *old_indices = get_indices(es_client, base_index_name) for old_index in old_indices[1:]: delete_index(es_client, old_index, logger) logger.info("SUCCEEDED Loading {}.".format(source_name)) except Exception as e: logger.debug(traceback.format_exc()) logger.debug(getattr(e, 'long_message', '')) logger.info("FAILED Loading {} Elasticsearch documents.\n" "=> Check the logs ({}) for more details." .format(source_name, log_file)) def main(config): log_dir = config['log-dir'] bulk_dir = os.path.join(config['data-dir'], 'json-bulk') if not os.path.exists(bulk_dir): raise Exception('No json bulk folder found in ' + bulk_dir) sources = config['sources'] for (source_name, source) in sources.items(): source_bulk_dir = get_folder_path([bulk_dir, source_name]) load_source(source, config, source_bulk_dir, log_dir)

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0.192661

0

null

0

null

0

1

0

0af3b89835e63f3225a17831847f039cebf091f8

6,798

py

Python

geoplot/crs.py

redfrexx/geoplot

8231baab0e286f1dec870dd5e8c6c8218e5b5da7

[ "MIT" ]

null

geoplot/crs.py

redfrexx/geoplot

8231baab0e286f1dec870dd5e8c6c8218e5b5da7

[ "MIT" ]

null

geoplot/crs.py

redfrexx/geoplot

8231baab0e286f1dec870dd5e8c6c8218e5b5da7

[ "MIT" ]

null

""" This module defines the ``geoplot`` coordinate reference system classes, wrappers on ``cartopy.crs`` objects meant to be used as parameters to the ``projection`` parameter of all front-end ``geoplot`` outputs. For the list of Cartopy CRS objects this module derives from, refer to http://scitools.org.uk/cartopy/docs/latest/crs/projections.html. """ import cartopy.crs as ccrs import geopandas as gpd class Base: # TODO: RotatedPole """ Generate instances of ``cartopy.crs``.*name* where *name* matches the instance's class name. Parameters ---------- `load` : Return a Cartopy CRS initialized with defaults from the `centerings` dictionary, overridden by initialization parameters. `_as_mpl_axes` : Return the result of calling cartopy's ``_as_mpl_axes`` for `self.load` called with empty `df` and `centerings`. """ def __init__(self, **kwargs): """Save parameters that initialize Cartopy CRSs.""" self.args = kwargs def load(self, df, centerings): """ A meta-method which abstracts the internals of individual projections' load procedures. Parameters ---------- df : GeoDataFrame The GeoDataFrame which has been passed as input to the plotter at the top level. This data is needed to calculate reasonable centering variables in cases in which the user does not already provide them; which is, incidentally, the reason behind all of this funny twice-instantiation loading in the first place. centerings: dict A dictionary containing names and centering methods. Certain projections have certain centering parameters whilst others lack them. For example, the geospatial projection contains both ``central_longitude`` and ``central_latitude`` instance parameter, which together control the center of the plot, while the North Pole Stereo projection has only a ``central_longitude`` instance parameter, implying that latitude is fixed (as indeed it is, as this projection is centered on the North Pole!). A top-level centerings method is provided in each of the ``geoplot`` top-level plot functions; each of the projection wrapper classes defined here in turn selects the functions from this list relevent to this particular instance and passes them to the ``_generic_load`` method here. We then in turn execute these functions to get defaults for our ``df`` and pass them off to our output ``cartopy.crs`` instance. Returns ------- crs : ``cartopy.crs`` object instance Returns a ``cartopy.crs`` object instance whose appropriate instance variables have been set to reasonable defaults wherever not already provided by the user. """ return getattr(ccrs, self.__class__.__name__)(**{**centerings, **self.args}) def _as_mpl_axes(self): """ When ``matplotlib`` is provided a projection via a ``projection`` keyword argument, it expects to get something with a callable ``as_mpl_axes`` method. The precise details of what this method does, exactly, are not important: it suffices to know that every ``cartopy`` coordinate reference system object has one. When we pass a ``geoplot.crs`` crs object to a ``geoplot`` function, the loading and centering of the data occurs automatically (using the function defined immediately above). Since we control what ``geoplot`` does at execution, we gracefully integrate this two-step procedure into the function body. But there are also use cases outside of our control in which we are forced to pass a ``geoplot.crs`` object without having first called ``load``: most prominently, when creating a plot containing subplots, the "overall" projection must be pre-loaded. It's possible to get around this by using ``cartopy.crs`` objects instead, but this is inelegant. This method is a better way: when a ``geoplot.crs`` object called by ``matplotlib``, it silently swaps itself out for a vanilla version of its ``cartopy.crs`` mirror, and calls that function's ``_as_mpl_axes`` instead. Parameters ---------- proj : geoplot.crs projection instance The instance in question (self, in the method body). Returns ------- Mutates into a ``cartopy.crs`` object and returns the result of executing ``_as_mpl_axes`` on that object instead. """ proj = self.load(gpd.GeoDataFrame(), dict()) return proj._as_mpl_axes() class Filtering(Base): """CRS that `load`s with `centering` restricted to keys in `self.filter_`.""" def load(self, df, centerings): """Call `load` method with `centerings` filtered to keys in `self.filter_`.""" return super().load( df, {key: value for key, value in centerings.items() if key in self.filter_} ) class LongitudeCentering(Filtering): """Form a CRS that centers by longitude.""" filter_ = {'central_longitude'} class LatitudeCentering(Filtering): """For a CRS that centers by latitude.""" filter_ = {'central_latitude'} PlateCarree,\ LambertCylindrical,\ Mercator,\ Miller,\ Mollweide,\ Robinson,\ Sinusoidal,\ InterruptedGoodeHomolosine,\ Geostationary,\ NorthPolarStereo,\ SouthPolarStereo = tuple( type(name, (LongitudeCentering,), {}) for name in ('PlateCarree', 'LambertCylindrical', 'Mercator', 'Miller', 'Mollweide', 'Robinson', 'Sinusoidal', 'InterruptedGoodeHomolosine', 'Geostationary', 'NorthPolarStereo', 'SouthPolarStereo') ) Gnomonic = type('Gnomonic', (LatitudeCentering,), {}) AlbersEqualArea,\ AzimuthalEquidistant,\ LambertConformal,\ Orthographic,\ Stereographic,\ TransverseMercator,\ LambertAzimuthalEqualArea,\ UTM,\ OSGB,\ EuroPP,\ OSNI = tuple( type(name, (Base,), {}) for name in ('AlbersEqualArea', 'AzimuthalEquidistant', 'LambertConformal', 'Orthographic', 'Stereographic', 'TransverseMercator', 'LambertAzimuthalEqualArea', 'UTM', 'OSGB', 'EuroPP', 'OSNI') )

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null

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null

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1

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0af473baeece942d5629ff430bbc40a3d23df7c3

559

py

Python

tmoga/utils/SDE.py

zjg540066169/tmoga

a3c3ecd0d72fc7c57fd5e5a624780e7ebf199c61

[ "Apache-2.0" ]

2

2021-10-06T04:45:52.000Z

2022-03-20T01:18:05.000Z

tmoga/utils/SDE.py

zjg540066169/tmoga

a3c3ecd0d72fc7c57fd5e5a624780e7ebf199c61

[ "Apache-2.0" ]

1

2022-03-20T01:45:09.000Z

2022-03-21T15:17:21.000Z

tmoga/utils/SDE.py

zjg540066169/tmoga

a3c3ecd0d72fc7c57fd5e5a624780e7ebf199c61

[ "Apache-2.0" ]

3

2021-10-09T08:08:44.000Z

2022-03-20T01:18:07.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Provide function to calculate SDE distance @auth: Jungang Zou @date: 2021/05/05 """ def SDE(front, values1, values2): shifted_dict = {} for i in front: shifted_dict[i] = [(values1[i], values2[i])] shifted_list = [] for j in front: if i == j: continue else: shifted_list.append((min(values1[i], values1[j]), min(values2[i], values2[j]))) shifted_dict[i].append(shifted_list) return shifted_dict

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null

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1

0

0af54c84e47849c156e92dd294fed072b3ed4861

1,183

py

Python

tests/v3_validation/cattlevalidationtest/core/test_logs_api.py

bmdepesa/validation-tests

23e7ab95ce76744483a0657f790b42a88a93436d

[ "Apache-2.0" ]

7

2015-11-18T17:43:08.000Z

2021-07-14T09:48:18.000Z

tests/v3_validation/cattlevalidationtest/core/test_logs_api.py

bmdepesa/validation-tests

23e7ab95ce76744483a0657f790b42a88a93436d

[ "Apache-2.0" ]

175

2015-07-09T18:41:24.000Z

2021-06-10T21:23:27.000Z

tests/v3_validation/cattlevalidationtest/core/test_logs_api.py

bmdepesa/validation-tests

23e7ab95ce76744483a0657f790b42a88a93436d

[ "Apache-2.0" ]

25

2015-08-08T04:54:24.000Z

2021-05-25T21:10:37.000Z

from common_fixtures import * # NOQA import websocket as ws import pytest def get_logs(client): hosts = client.list_host(kind='docker', removed_null=True) assert len(hosts) > 0 in_log = random_str() cmd = '/bin/bash -c "echo {}; sleep 2"'.format(in_log) c = client.create_container(image=TEST_IMAGE_UUID, command=cmd) c = client.wait_success(c) logs = c.logs() return logs, in_log, c def test_logs_token(client): logs, in_log, c = get_logs(client) conn = ws.create_connection(logs.url + '?token='+logs.token) result = conn.recv() assert result is not None assert in_log in result delete_all(client, [c]) def test_logs_no_token(client): logs, _, c = get_logs(client) with pytest.raises(Exception) as excinfo: ws.create_connection(logs.url) assert 'Handshake status 401' in str(excinfo.value) delete_all(client, [c]) def test_host_api_garbage_token(client): logs, _, c = get_logs(client) with pytest.raises(Exception) as excinfo: ws.create_connection(logs.url+'?token=random.garbage.token') assert 'Handshake status 401' in str(excinfo.value) delete_all(client, [c])

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null

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null

0

1

0

0af634a53b2ebcc4683b0c1863c9043af5a4905d

1,090

py

Python

drybell/drybell_lfs_spark.py

jsnlp/snorkel-tutorials

b4cda9f918daf77f4011ec1598c08d9bd7e51c39

[ "Apache-2.0" ]

315

2019-07-27T22:49:20.000Z

2022-03-30T10:02:02.000Z

drybell/drybell_lfs_spark.py

jsnlp/snorkel-tutorials

b4cda9f918daf77f4011ec1598c08d9bd7e51c39

[ "Apache-2.0" ]

133

2019-07-25T02:07:37.000Z

2022-03-29T12:08:32.000Z

drybell/drybell_lfs_spark.py

jsnlp/snorkel-tutorials

b4cda9f918daf77f4011ec1598c08d9bd7e51c39

[ "Apache-2.0" ]

173

2019-08-13T02:27:11.000Z

2022-03-30T05:26:40.000Z

from pyspark.sql import Row from snorkel.labeling.lf import labeling_function from snorkel.labeling.lf.nlp_spark import spark_nlp_labeling_function from snorkel.preprocess import preprocessor from drybell_lfs import load_celebrity_knowledge_base ABSTAIN = -1 NEGATIVE = 0 POSITIVE = 1 @preprocessor() def combine_text(x): return Row(title=x.title, body=x.body, article=f"{x.title} {x.body}") @spark_nlp_labeling_function(text_field="article", pre=[combine_text]) def article_mentions_person(x): for ent in x.doc.ents: if ent.label_ == "PERSON": return ABSTAIN return NEGATIVE @spark_nlp_labeling_function( text_field="article", pre=[combine_text], resources=dict(celebrity_knowledge_base=load_celebrity_knowledge_base()), ) def person_in_db(x, celebrity_knowledge_base): for ent in x.doc.ents: if ent.label_ == "PERSON" and ent.text.lower() in celebrity_knowledge_base: return POSITIVE return ABSTAIN @labeling_function() def body_contains_fortune(x): return POSITIVE if "fortune" in x.body else ABSTAIN

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1,090

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0.159633

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null

0

null

0

1

0

0af886d3e8e59b20a8f0a8f86ad88dbe765599d2

14,441

py

Python

python/influx/database_tables.py

SA-22C-smoothswing/spectrum-protect-sppmon

8a9c70f65d9faf6ffc35f3400383dcaa6e0fcbc6

[ "Apache-2.0" ]

null

python/influx/database_tables.py

SA-22C-smoothswing/spectrum-protect-sppmon

8a9c70f65d9faf6ffc35f3400383dcaa6e0fcbc6

[ "Apache-2.0" ]

null

python/influx/database_tables.py

SA-22C-smoothswing/spectrum-protect-sppmon

8a9c70f65d9faf6ffc35f3400383dcaa6e0fcbc6

[ "Apache-2.0" ]

null

"""Provides all database and table structures used for the influx database. Classes: Datatype Database Table RetentionPolicy """ from __future__ import annotations from enum import Enum, unique import re import json from typing import Any, Dict, List, Set, Tuple, Union import influx.influx_queries as Queries from utils.execption_utils import ExceptionUtils from utils.influx_utils import InfluxUtils from utils.spp_utils import SppUtils @unique class Datatype(Enum): """ This enum differentiates between the different Influx-Types. By declaring the type SPPMon will automatically insert the data in the right format. The order of the types within the enum is important: bool is a int, but a int is not a bool. Important: only use `TIME` for epoch timestamps, *NOT* for durations or counts. `TIME` is automatically converted into second format. Note: The return type is just a helper and not of a big use. Methods: get_auto_datatype - get Datatype enum by value typ analysis """ NONE = type(None) """Undeclared, only use as a placeholder.""" STRING = str """Special symbols and \" will be escaped.""" BOOL = bool """Any boolean, be aware it is a subtype of int. TODO Untested, saves as Boolean within Influx. """ INT = int """Appends a 'i' at end of number to declare. Fails if the data is mixed with any other type.""" FLOAT = float """Unchanged value. Default Influx numeric data type. Mixing with ints works.""" TIMESTAMP = type(int) """Automatic transform a timestamp into seconds. Important: Only use for Epoch timestamps, not duration or counter. Caution: Type is just a placeholder, do not set to int - causing problems! """ @staticmethod def get_auto_datatype(value: Any) -> Datatype: """get Datatype enum by value typ analysis. Usage should be avoided. Only use if no datatype is declared. It skips time-type and fails if ints are mixed with floats. If no type is detected emits a warning and returns `NONE`. Arguments: value {Union[str, float, int, bool, None]} -- Value to be analyzed Returns: Datatype -- type of value or `NONE`. """ for enum in Datatype: if(enum is Datatype.TIMESTAMP): continue if(isinstance(value, enum.value)): return enum ExceptionUtils.error_message(f"No auto type found for {value}") return Datatype.NONE class RetentionPolicy: """Represents a influxdb retention policy. By this policy it is declared afer which ammount of time a dataset is deleted from the DB. Attributes name - name of RP database - associated database duration - time until the data is purged replication - How often the date is replicated shard_duration - Size of memory-groups default - whether this is the default RP Methods to_dict - creates a dict out of the values """ @property def name(self) -> str: """name of the Retention Policy""" return self.__name @property def database(self) -> Database: """associated database""" return self.__database @property def duration(self) -> str: """time until the data is purged""" return self.__duration @property def replication(self) -> int: """How often the date is replicated. We only have 1 db instance so replication is always 1""" return self.__replication @property def shard_duration(self) -> str: """Size of memory-groups. Default time is 0s, then the db decides what to take""" return self.__shard_duration @property def default(self) -> bool: """ whether this is the default RP""" return self.__default def __init__(self, name: str, database: Database, duration: str, replication: int = 1, shard_duration: str = "0s", default: bool = False) -> None: if(not name): raise ValueError("need retention policy name for creation") if(not database): raise ValueError("need retention policy database for creation") if(not duration): raise ValueError("need retention policy duration for creation") if(not replication): raise ValueError("need retention policy replication factor for creation") if(not shard_duration): raise ValueError("need retention policy shard duration for creation") if(default is None): raise ValueError("need retention policy default setting for creation") self.__name = name self.__database = database self.__replication = replication self.__shard_duration = shard_duration self.__default = default try: # str due usage of method self.__duration: str = InfluxUtils.transform_time_literal(duration, single_vals=False) except ValueError as error: ExceptionUtils.exception_info(error) raise ValueError(f"duration for retention policy {name} is not in the correct time format") try: # str due usage of method self.__shard_duration: str = InfluxUtils.transform_time_literal(shard_duration, single_vals=False) except ValueError as error: ExceptionUtils.exception_info(error) raise ValueError(f"shard duration for retention policy {name} is not in the correct time format") def to_dict(self) -> Dict[str, Union[str, int, bool]]: """Used to create a dict out of the values, able to compare to influxdb-created dict""" return { 'name': self.name, 'duration': self.duration, 'shardGroupDuration': self.__shard_duration, 'replicaN': self.__replication, 'default': self.default } def __str__(self) -> str: return f"{self.database.name}.{self.name}" def __repr__(self) -> str: return f"Retention Policy: {self.name}" def __eq__(self, o: object) -> bool: if(isinstance(o, RetentionPolicy)): return o.to_dict() == self.to_dict() return False def __hash__(self) -> int: return hash(json.dumps(self.to_dict(), sort_keys=True)) class Table: """Represents a measurement in influx. Contains pre-defined tag and field definitions. Attributes name - name of table fields - dict of field name with datatype tags - tags as list of str time_key - key name of the timestamp field retention_policy - retention policy associated with this table database - table is declared within this database Methods split_by_table_def - Split the given dict into a pre-defined set of tags, fields and a timestamp. """ @property def fields(self) -> Dict[str, Datatype]: """fields of the table, name is key, value is datatype""" return self.__fields @property def tags(self) -> List[str]: """tags of the table, datatype always string""" return self.__tags @property def time_key(self) -> str: """name of the timestamp key""" return self.__time_key @property def name(self) -> str: """name of the table""" return self.__name @property def retention_policy(self) -> RetentionPolicy: """retention policy associated with this table""" return self.__retention_policy @property def database(self) -> Database: """table is declared within this database""" return self.__database __bad_measurement_characters: List[str] = [' ', ','] """those chars need to be escaped within a measurement/table name""" def __init__(self, database: Database, name: str, fields: Dict[str, Datatype] = None, tags: List[str] = None, time_key: str = 'time', retention_policy: RetentionPolicy = None) -> None: if(not database): raise ValueError("need database to create table") if(not name): raise ValueError("need str name to create table") if(not time_key): raise ValueError("time key cannot be None") if(not fields): fields = {} if(not tags): tags = [] if(not retention_policy): retention_policy = next(filter(lambda rp: rp.default, database.retention_policies)) self.__database: Database = database self.__fields: Dict[str, Datatype] = fields self.__tags: List[str] = tags self.__time_key: str = time_key self.__retention_policy = retention_policy # escape not allowed characters in Measurement for bad_character in self.__bad_measurement_characters: if(re.search(bad_character, name)): name = name.replace(bad_character, '\\%c'% bad_character) self.__name: str = name def __str__(self) -> str: return f"{self.database.name}.{self.retention_policy.name}.{self.name}" def __repr__(self) -> str: return f"Table: {self.name}" def split_by_table_def(self, mydict: Dict[str, Any]) -> Tuple[ Dict[str, Any], Dict[str, Any], Union[str, int, None]]: """Split the given dict into a pre-defined set of tags, fields and a timestamp. None-Values and empty strings are ignored. If there are no fields declared, it will split by a default pattern. Undeclared collums will produce a warning. This function uses the tag/field and timestamp definiton declared within this table. Arguments: self {Table} -- Table with predefined set of tags and fields mydict {Dict[str, Any]} -- dict with colums as keys. None-Values are ignored Raises: ValueError: If no dict is given or not of type dict. Returns: (Dict[str, Any], Dict[str, Any], int) -- Tuple of: tags, fields, timestamp """ if(not mydict): raise ValueError("need at least one value in dict to split") # if table is not defined use default split if(not self.fields): return InfluxUtils.default_split(mydict=mydict) # fill dicts # table.fields is a dict, we only need the keys fields: Dict[str, Any] = dict.fromkeys(self.fields.keys(), None) tags: Dict[str, Any] = dict.fromkeys(self.tags, None) # what field should be recorded as time time_stamp_field = self.time_key # helper variable to only overwrite if it is not the time_stamp_field time_overwrite_allowed = True # actualy timestamp saved time_stamp: Union[str, int, None] = None for (key, value) in mydict.items(): # Ignore empty entrys if(value is None or (isinstance(value, str) and not value)): continue # Check timestamp value if it matches any of predefined time names if(key in time_stamp_field or key in InfluxUtils.time_key_names): # sppmonCTS has lowest priority, only set if otherwise None if(time_stamp is None and key == SppUtils.capture_time_key): time_stamp = value # time_stamp_field is highest priority. Do not overwrite it. elif(key is time_stamp_field): time_overwrite_allowed: bool = False time_stamp = value # if time_stamp_field is not used yet, overwrite sppmonCaptureTime or others elif(time_overwrite_allowed): time_stamp = value # if no overwrite allowed, continue and drop field else: continue # Otherwise check for Keys or Fields if(key in fields): fields[key] = value elif(key in tags): tags[key] = value elif(key in InfluxUtils.time_key_names or key in time_stamp_field): continue else: ExceptionUtils.error_message(f"Not all columns for table {self.name} are declared: {key}") # before key+"MISSING" : Removed to avoid death-circle on repeated queries. fields[key] = value return (tags, fields, time_stamp) class Database: """ Represents a instance of influx database. Define all table definitions within the init method. Attributes name - name of the database tables - tables with predefined tags & fields retention_policies - Set of all provided Retention Policies continuous_queries - Set of all provided Continuous Queries Methods __getitem__ - [] access on the tables via name. Creates empty table if missing. """ @property def tables(self) -> Dict[str, Table]: """Dict with table definitions to look up""" return self.__tables @property def retention_policies(self) -> Set[RetentionPolicy]: """Set of all provided Retention Policies""" return self.__retention_policies @property def continuous_queries(self) -> Set[Queries.ContinuousQuery]: """Set of all provided Continuous Queries""" return self.__continuous_queries @property def name(self) -> str: """name of the database, also used as reference""" return self.__name def __getitem__(self, table_name: str) -> Table: """Aquire a instance of a predefined table, returns a empty table if it was not defined. []-Access. Arguments: table_name {str} -- name of the table you want to aquire Returns: Table -- Instance of a predefined table, otherwise new empty table """ return self.tables.get(table_name, Table(self, table_name)) def __str__(self) -> str: return self.name def __repr__(self) -> str: return f'Database: {self.name}' def __init__(self, name: str): self.__name: str = name self.__tables: Dict[str, Table] = {} self.__retention_policies: Set[RetentionPolicy] = set() self.__continuous_queries: Set[Queries.ContinuousQuery] = set()

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null

0

null

0

1

0

0af95702c3886ad24fef9b7d2bef0b353d7f0d8a

5,779

py

Python

eval_encoder.py

lithium0003/Image2UTF8-Transformer

2620af2a8bdaf332e25b39ce05d610e21e6492fc

[ "MIT" ]

null

eval_encoder.py

lithium0003/Image2UTF8-Transformer

2620af2a8bdaf332e25b39ce05d610e21e6492fc

[ "MIT" ]

null

eval_encoder.py

lithium0003/Image2UTF8-Transformer

2620af2a8bdaf332e25b39ce05d610e21e6492fc

[ "MIT" ]

null

#!/usr/bin/env python3 import tensorflow as tf physical_devices = tf.config.list_physical_devices('GPU') try: tf.config.experimental.set_memory_growth(physical_devices[0], True) except: # Invalid device or cannot modify virtual devices once initialized. pass import numpy as np import os, time, csv import tqdm import umap import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import datetime import signal import net from matplotlib import rcParams rcParams['font.family'] = 'sans-serif' rcParams['font.sans-serif'] = ['Hiragino Maru Gothic Pro', 'Yu Gothic', 'Meirio', 'Takao', 'IPAexGothic', 'IPAPGothic', 'Noto Sans CJK JP'] import net class SimpleEncodeDecoder: def __init__(self): self.save_dir = './result/step1/' self.result_dir = './result/plot/' os.makedirs(self.result_dir, exist_ok=True) checkpoint_dir = self.save_dir self.max_epoch = 300 self.steps_per_epoch = 1000 self.batch_size = 64 lr = tf.keras.optimizers.schedules.ExponentialDecay(1e-3, 1e5, 0.5) self.optimizer = tf.keras.optimizers.Adam(lr) self.encoder = net.FeatureBlock() self.encoder.summary() self.decoder = net.SimpleDecoderBlock() self.decoder.summary() inputs = { 'image': tf.keras.Input(shape=(128,128,3)), } feature_out = self.encoder(inputs) outputs = self.decoder(feature_out) self.model = tf.keras.Model(inputs, outputs, name='SimpleEncodeDecoder') checkpoint = tf.train.Checkpoint(optimizer=self.optimizer, model=self.model) last = tf.train.latest_checkpoint(checkpoint_dir) checkpoint.restore(last) self.manager = tf.train.CheckpointManager( checkpoint, directory=checkpoint_dir, max_to_keep=2) if not last is None: self.init_epoch = int(os.path.basename(last).split('-')[1]) print('loaded %d epoch'%self.init_epoch) else: self.init_epoch = 0 self.model.summary() def eval(self): self.data = net.FontData() print("Plot: ", self.init_epoch + 1) acc = self.make_plot(self.data.test_data(self.batch_size), (self.init_epoch + 1)) print('acc', acc) @tf.function def eval_substep(self, inputs): input_data = { 'image': inputs['input'], } feature = self.encoder(input_data) outputs = self.decoder(feature) target_id = inputs['index'] target_id1 = inputs['idx1'] target_id2 = inputs['idx2'] pred_id1 = tf.nn.softmax(outputs['id1'], -1) pred_id2 = tf.nn.softmax(outputs['id2'], -1) return { 'feature': feature, 'pred_id1': pred_id1, 'pred_id2': pred_id2, 'target_id': target_id, 'target_id1': target_id1, 'target_id2': target_id2, } def make_plot(self, test_ds, epoch): result = [] labels = [] with open(os.path.join(self.result_dir,'test_result-%d.txt'%epoch),'w') as txt: correct_count = 0 failed_count = 0 with tqdm.tqdm(total=len(self.data.test_keys)) as pbar: for inputs in test_ds: pred = self.eval_substep(inputs) result += [pred['feature']] labels += [pred['target_id']] for i in range(pred['target_id1'].shape[0]): txt.write('---\n') target = pred['target_id'][i].numpy() txt.write('target: id %d = %s\n'%(target, self.data.glyphs[target-1])) predid1 = np.argmax(pred['pred_id1'][i]) predid2 = np.argmax(pred['pred_id2'][i]) predid = predid1 * 100 + predid2 if predid == 0: txt.write('predict: id %d nothing (p=%f)\n'%(predid, pred['pred_id1'][i][predid1] * pred['pred_id2'][i][predid2])) elif predid > self.data.id_count + 1: txt.write('predict: id %d nothing (p=%f)\n'%(predid, pred['pred_id1'][i][predid1] * pred['pred_id2'][i][predid2])) else: txt.write('predict: id %d = %s (p=%f)\n'%(predid, self.data.glyphs[predid-1], pred['pred_id1'][i][predid1] * pred['pred_id2'][i][predid2])) if target == predid: txt.write('Correct!\n') correct_count += 1 else: txt.write('Failed!\n') failed_count += 1 pbar.update(1) acc = correct_count / (correct_count + failed_count) txt.write('==============\n') txt.write('Correct = %d\n'%correct_count) txt.write('Failed = %d\n'%failed_count) txt.write('accuracy = %f\n'%acc) result = np.concatenate(result) labels = np.concatenate(labels) print('run UMAP') X_reduced = umap.UMAP(metric='cosine').fit_transform(result) fig, ax = plt.subplots(figsize=(50, 50)) ax.scatter(X_reduced[:, 0], X_reduced[:, 1], c=labels, cmap=plt.get_cmap('hsv')) print('plot UMAP') for i, label in enumerate(labels): ax.annotate(self.data.glyphs[label-1], (X_reduced[i,0], X_reduced[i,1])) plt.savefig(os.path.join(self.result_dir,'test_result-%d.png'%epoch), dpi=300) plt.close('all') return acc def eval(): encoder = SimpleEncodeDecoder() encoder.eval() if __name__ == '__main__': eval()

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0.038168

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null

0

null

0

1

0

0afa87a4b421519306afb64f3b1e1263669a468c

22,351

py

Python

clipper_admin/clipper_admin/clipper_admin.py

SimonZsx/clipper

457088be2ebe68c68b94d90389d1308e35b4c844

[ "Apache-2.0" ]

2

2019-04-24T13:46:28.000Z

2019-05-28T06:59:26.000Z

clipper_admin/clipper_admin/clipper_admin.py

SimonZsx/clipper

457088be2ebe68c68b94d90389d1308e35b4c844

[ "Apache-2.0" ]

null

clipper_admin/clipper_admin/clipper_admin.py

SimonZsx/clipper

457088be2ebe68c68b94d90389d1308e35b4c844

[ "Apache-2.0" ]

4

2019-04-03T11:03:57.000Z

2019-06-26T08:22:38.000Z

from __future__ import absolute_import, division, print_function import logging import docker import tempfile import requests from requests.exceptions import RequestException import json import pprint import time import re import os import tarfile import sys from cloudpickle import CloudPickler import pickle import numpy as np from google.protobuf.json_format import MessageToDict if sys.version_info < (3, 0): try: from cStringIO import StringIO except ImportError: from StringIO import StringIO PY3 = False else: from io import BytesIO as StringIO PY3 = True import grpc from .rpc import model_pb2_grpc from .rpc import model_pb2 from .rpc import prediction_pb2_grpc from .rpc import prediction_pb2 from .rpc import management_pb2 from .rpc import management_pb2_grpc from .container_manager import CONTAINERLESS_MODEL_IMAGE, ClusterAdapter from .exceptions import ClipperException, UnconnectedException from .version import __version__, __registry__ from . import graph_parser DEFAULT_LABEL = [] DEFAULT_PREDICTION_CACHE_SIZE_BYTES = 33554432 CLIPPER_TEMP_DIR = "/tmp/clipper" # Used Internally for Test; Not Windows Compatible logging.basicConfig( format='%(asctime)s %(levelname)-8s %(message)s', datefmt='%y-%m-%d:%H:%M:%S', level=logging.INFO) # logging.basicConfig( # format='%(asctime)s %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s', # datefmt='%y-%m-%d:%H:%M:%S', # level=logging.INFO) logger = logging.getLogger(__name__) deploy_regex_str = "[a-z0-9]([-a-z0-9]*[a-z0-9])?\Z" deployment_regex = re.compile(deploy_regex_str) def _validate_versioned_model_name(name, version): if deployment_regex.match(name) is None: raise ClipperException( "Invalid value: {name}: a model name must be a valid DNS-1123 " " subdomain. It must consist of lower case " "alphanumeric characters, '-' or '.', and must start and end with " "an alphanumeric character (e.g. 'example.com', regex used for " "validation is '{reg}'".format(name=name, reg=deploy_regex_str)) if deployment_regex.match(version) is None: raise ClipperException( "Invalid value: {version}: a model version must be a valid DNS-1123 " " subdomain. It must consist of lower case " "alphanumeric characters, '-' or '.', and must start and end with " "an alphanumeric character (e.g. 'example.com', regex used for " "validation is '{reg}'".format( version=version, reg=deploy_regex_str)) class ClipperConnection(object): def __init__(self, container_manager): self.connected = False self.cm = container_manager #############TEST################ self.runtime_dag = "" self.lock = False ################################# self.logger = ClusterAdapter(logger, { 'cluster_name': self.cm.cluster_identifier }) def start_clipper(self, mgmt_frontend_image='{}/management_frontend:{}'.format( __registry__, __version__), cache_size=DEFAULT_PREDICTION_CACHE_SIZE_BYTES): try: self.cm.start_clipper(mgmt_frontend_image) # while True: # try: # query_frontend_url = "http://{host}/metrics".format( # host=self.cm.get_query_addr()) # mgmt_frontend_url = "http://{host}/admin/ping".format( # host=self.cm.get_admin_addr()) # for name, url in [('query frontend', query_frontend_url), # ('management frontend', mgmt_frontend_url)]: # r = requests.get(url, timeout=5) # if r.status_code != requests.codes.ok: # raise RequestException( # "{name} end point {url} health check failed".format(name=name, url=url)) # break # except RequestException as e: # self.logger.info("Clipper still initializing: \n {}".format(e)) # time.sleep(1) self.logger.info("Clipper is running") self.connected = True except ClipperException as e: self.logger.warning("Error starting Clipper: {}".format(e.msg)) raise e def connect(self): """Connect to a running Clipper cluster.""" self.cm.connect() self.connected = True self.logger.info( "Successfully connected to Clipper cluster at {}".format( self.cm.get_query_addr())) def build_and_deploy_DAG(self, name, version, dag_description, labels): if not self.connected: raise UnconnectedException() def build_and_deploy_model(self, name, version, input_type, model_data_path, base_image, labels=None, container_registry=None, num_replicas=1, batch_size=-1, pkgs_to_install=None): if not self.connected: raise UnconnectedException() image = self.build_model(name, version, model_data_path, base_image, container_registry, pkgs_to_install) self.deploy_model(name, version, input_type, image, labels, num_replicas, batch_size) def build_model(self, name, version, model_data_path, base_image, container_registry=None, pkgs_to_install=None): version = str(version) _validate_versioned_model_name(name, version) run_cmd = '' if pkgs_to_install: run_as_lst = 'RUN apt-get -y install build-essential && pip install'.split( ' ') run_cmd = ' '.join(run_as_lst + pkgs_to_install) with tempfile.NamedTemporaryFile( mode="w+b", suffix="tar") as context_file: # Create build context tarfile with tarfile.TarFile( fileobj=context_file, mode="w") as context_tar: context_tar.add(model_data_path) # From https://stackoverflow.com/a/740854/814642 try: df_contents = StringIO( str.encode( "FROM {container_name}\n{run_command}\nCOPY {data_path} /model/\n". format( container_name=base_image, data_path=model_data_path, run_command=run_cmd))) df_tarinfo = tarfile.TarInfo('Dockerfile') df_contents.seek(0, os.SEEK_END) df_tarinfo.size = df_contents.tell() df_contents.seek(0) context_tar.addfile(df_tarinfo, df_contents) except TypeError: df_contents = StringIO( "FROM {container_name}\n{run_command}\nCOPY {data_path} /model/\n". format( container_name=base_image, data_path=model_data_path, run_command=run_cmd)) df_tarinfo = tarfile.TarInfo('Dockerfile') df_contents.seek(0, os.SEEK_END) df_tarinfo.size = df_contents.tell() df_contents.seek(0) context_tar.addfile(df_tarinfo, df_contents) # Exit Tarfile context manager to finish the tar file # Seek back to beginning of file for reading context_file.seek(0) image = "{cluster}-{name}:{version}".format( cluster=self.cm.cluster_identifier, name=name, version=version) if container_registry is not None: image = "{reg}/{image}".format( reg=container_registry, image=image) docker_client = docker.from_env() self.logger.info( "Building model Docker image with model data from {}".format( model_data_path)) image_result, build_logs = docker_client.images.build( fileobj=context_file, custom_context=True, tag=image) for b in build_logs: if 'stream' in b and b['stream'] != '\n': #log build steps only self.logger.info(b['stream'].rstrip()) self.logger.info("Pushing model Docker image to {}".format(image)) for line in docker_client.images.push(repository=image, stream=True): self.logger.debug(line) return image def deploy_model(self, name, version, input_type, image, labels=None, num_replicas=1, batch_size=-1): if not self.connected: raise UnconnectedException() version = str(version) _validate_versioned_model_name(name, version) self.cm.deploy_model( name=name, version=version, input_type=input_type, image=image, num_replicas=num_replicas) # self.register_model( # name, # version, # input_type, # image=image, # labels=labels, # batch_size=batch_size) self.logger.info("Done deploying model {name}:{version}.".format( name=name, version=version)) def connect_host(self, host_ip, host_port): self.cm.connect_host(host_ip, "2375") def add_model(self, model_name, model_version, image, input_type="string", output_type="string", stateful=False): modelinfo = management_pb2.ModelInfo(modelname=model_name, modelversion=model_version, image=image, inputtype=input_type, outputtype=output_type, stateful=stateful).SerializeToString() self.cm.grpc_client("zsxhku/grpcclient", "--addmodel %s %s %s "%("localhost","33333", modelinfo)) return def deploy_DAG(self, name, version, dag_description=None, runtime=""): if not self.connected: raise UnconnectedException() # model_info = self.get_all_models() dag_description_ = dag_description #self.logger.info("dag_description: %s"%(dag_description_)) #if(dag_description==None): # dag_description_=self.get_dag_description() nodes_list = graph_parser.get_all_nodes(dag_description_) container_info = [] proxy_info = [] backup_info = [] count = 1 for model_info in nodes_list: model_name,model_version,model_image = graph_parser.get_name_version(model_info) container_name, container_id, host = self.cm.add_replica(model_name, model_version, "22222", model_image, runtime=runtime) self.logger.info("Started %s with container %s:%s (HOST:%s)"%(model_name, container_name, container_id, host)) container_ip = self.cm.get_container_ip(host, container_id) proxy_name, proxy_id = self.cm.set_proxy("mxschen/ai-proxy:latest", container_name, container_ip, host) ## get the ip of the instances proxy_ip = self.cm.get_container_ip(host, proxy_id) proxy_info.append([proxy_name,proxy_id,proxy_ip]) container_info.append([container_name, container_id, container_ip]) if graph_parser.is_stateful(model_info): backup_name, backup_id, backup_host = self.cm.add_replica(model_name, model_version, "22222", model_image) self.logger.info("[Backup] Started %s with container %s:%s (HOST:%s)"%(model_name, backup_name, backup_id, backup_host)) backup_ip = self.cm.get_container_ip(backup_host, backup_id) backup_proxy_name, backup_proxy_id = self.cm.set_proxy("mxschen/ai-proxy:latest", backup_name, backup_ip, backup_host) backup_proxy_ip= self.cm.get_container_ip(backup_host, backup_proxy_id) backup_info.append([backup_name, backup_id, backup_ip, backup_proxy_name, backup_proxy_id, backup_proxy_ip]) else: backup_info.append([]) #self.cm.check_container_status(host, container_id, 0.3, 20) #self.cm.check_container_status(host, proxy_id, 0.3, 20) #time.sleep(25) #self.logger.info("proxy_ip:%s"%(proxy_ip)) self.cm.grpc_client("zsxhku/grpcclient", "--setmodel %s %s %s %s %s %s"%(proxy_ip, "22223", container_name, count, container_ip, "22222" )) self.logger.info('[DEPLOYMENT] Finished setting model info to proxy') if(graph_parser.is_stateful(model_info)): self.cm.grpc_client("zsxhku/grpcclient", "--setmodel %s %s %s %s %s %s"%(backup_info[-1][-1], "22223", backup_info[-1][0], count, backup_info[-1][2], "22222" )) self.logger.info('[DEPLOYMENT][Backup] Finished setting model info to proxy') count += 1 # self.cm.grpc_client("zsxhku/grpcclient", "--setproxy %s %s %s %s"%(container_ip, "22222", proxy_name, "22223")) # self.logger.info('[DEPLOYMENT] Finished setting proxy info to model') # if(graph_parser.is_stateful(model_info)): # self.cm.grpc_client("zsxhku/grpcclient", "--setproxy %s %s %s %s"%(backup_info[-1][2], "22222", backup_info[-1][3], "22223")) # self.logger.info('[DEPLOYMENT][Backup] Finished setting proxy info to model') runtime_dag_id = name+version+str(1) ## Starting frontend frontend_name, frontend_container_id = self.cm.add_frontend("localhost", "mxschen/frontend",runtime_dag_id, proxy_info[0][2], "22223", max_workers=2048) frontend_ip = self.cm.get_container_ip("localhost", frontend_container_id) frontend_info = [frontend_name, frontend_container_id, frontend_ip] self.logger.info("[DEPLOYMENT] ################ Started Frontend #################") #expand the dag description with the model/proxy instances info expanded_dag = graph_parser.expand_dag(dag_description_, name, version, container_info, proxy_info, backup_info, frontend_info) self.runtime_dag = expanded_dag # TODO: need to modularize self.cm.grpc_client("zsxhku/grpcclient", "--addruntimedag %s %s %s %s %s %s %s"%('1', name, version, 'old' , self.cm.admin_ip, self.cm.admin_port, expanded_dag)) self.logger.info("Added new runtime DAG to admin daemon\n%s"%(expanded_dag)) #tells the proxy runtime dag info for tup in proxy_info: proxy_name = tup[0] proxy_id = tup[1] proxy_ip = tup[2] self.cm.grpc_client("zsxhku/grpcclient", "--setdag %s %s %s"%(proxy_ip, "22223", expanded_dag)) self.logger.info('[DEPLOYMENT] Finished setting DAG for proxy {proxy_name} '.format(proxy_name=proxy_name)) #tells the backups runtime dag info for tup in backup_info: if tup: self.cm.grpc_client("zsxhku/grpcclient", "--setdag %s %s %s"%(tup[-1], "22223", expanded_dag)) self.logger.info('[DEPLOYMENT][Backup] Finished setting DAG for proxy {proxy_name} '.format(proxy_name=tup[-1])) return def inspect_instance(self): """Fetches performance metrics from the running Clipper cluster. Returns ------- str The JSON string containing the current set of metrics for this instance. On error, the string will be an error message (not JSON formatted). Raises ------ :py:exc:`clipper.UnconnectedException` :py:exc:`clipper.ClipperException` """ def get_query_addr(self): """Get the IP address at which the query frontend can be reached request predictions. Returns ------- str The address as an IP address or hostname. Raises ------ :py:exc:`clipper.UnconnectedException` versions. All replicas for each version of each model will be stopped. """ if not self.connected: raise UnconnectedException() return self.cm.get_query_addr() def stop_models(self, model_names): """Stops all versions of the specified models. This is a convenience method to avoid the need to explicitly list all versions of a model when calling :py:meth:`clipper_admin.ClipperConnection.stop_versioned_models`. Parameters ---------- model_names : list(str) A list of model names. All replicas of all versions of each model specified in the list will be stopped. Raises ------ :py:exc:`clipper.UnconnectedException` versions. All replicas for each version of each model will be stopped. """ # if not self.connected: # raise UnconnectedException() # model_info = self.get_all_models(verbose=True) # model_dict = {} # for m in model_info: # if m["model_name"] in model_names: # if m["model_name"] in model_dict: # model_dict[m["model_name"]].append(m["model_version"]) # else: # model_dict[m["model_name"]] = [m["model_version"]] # self.cm.stop_models(model_dict) # pp = pprint.PrettyPrinter(indent=4) # self.logger.info( # "Stopped all containers for these models and versions:\n{}".format( # pp.pformat(model_dict))) def stop_versioned_models(self, model_versions_dict): """Stops the specified versions of the specified models. Parameters ---------- model_versions_dict : dict(str, list(str)) For each entry in the dict, the key is a model name and the value is a list of model Raises ------ :py:exc:`clipper.UnconnectedException` versions. All replicas for each version of each model will be stopped. Note ---- This method will stop the currently deployed versions of models if you specify them. You almost certainly want to use one of the other stop_* methods. Use with caution. """ # if not self.connected: # raise UnconnectedException() # self.cm.stop_models(model_versions_dict) # pp = pprint.PrettyPrinter(indent=4) # self.logger.info( # "Stopped all containers for these models and versions:\n{}".format( # pp.pformat(model_versions_dict))) def stop_inactive_model_versions(self, model_names): """Stops all model containers serving stale versions of the specified models. For example, if you have deployed versions 1, 2, and 3 of model "music_recommender" and version 3 is the current version:: clipper_conn.stop_inactive_model_versions(["music_recommender"]) will stop any containers serving versions 1 and 2 but will leave containers serving version 3 untouched. Parameters ---------- model_names : list(str) The names of the models whose old containers you want to stop. Raises ------ :py:exc:`clipper.UnconnectedException` """ # if not self.connected: # raise UnconnectedException() # model_info = self.get_all_models(verbose=True) # model_dict = {} # for m in model_info: # if m["model_name"] in model_names and not m["is_current_version"]: # if m["model_name"] in model_dict: # model_dict[m["model_name"]].append(m["model_version"]) # else: # model_dict[m["model_name"]] = [m["model_version"]] # self.cm.stop_models(model_dict) # pp = pprint.PrettyPrinter(indent=4) # self.logger.info( # "Stopped all containers for these models and versions:\n{}".format( # pp.pformat(model_dict))) def stop_all_model_containers(self): """Stops all model containers started via Clipper admin commands. This method can be used to clean up leftover Clipper model containers even if the Clipper management frontend or Redis has crashed. It can also be called without calling ``connect`` first. """ self.cm.stop_all_model_containers() self.logger.info("Stopped all Clipper model containers") def stop_all(self, graceful=True): """Stops all processes that were started via Clipper admin commands. This includes the query and management frontend Docker containers and all model containers. If you started Redis independently, this will not affect Redis. It can also be called without calling ``connect`` first. If graceful=False, Clipper will issue Docker Kill if it's in the Docker Mode. This parameter will take not effect in Kubernetes. """ self.cm.stop_all(graceful=graceful) self.logger.info( "Stopped all Clipper cluster and all model containers")

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Python

chevah/compat/testing/testcase.py

chevah/compat

d22e5f551a628f8a1652c9f2eea306e17930cb8f

[ "BSD-3-Clause" ]

5

2016-12-03T22:54:50.000Z

2021-11-17T11:17:39.000Z

chevah/compat/testing/testcase.py

chevah/compat

d22e5f551a628f8a1652c9f2eea306e17930cb8f

[ "BSD-3-Clause" ]

76

2015-01-22T16:00:31.000Z

2022-02-09T22:13:34.000Z

chevah/compat/testing/testcase.py

chevah/compat

d22e5f551a628f8a1652c9f2eea306e17930cb8f

[ "BSD-3-Clause" ]

1

2016-12-10T15:57:31.000Z

# -*- coding: utf-8 -*- # Copyright (c) 2011 Adi Roiban. # See LICENSE for details. """ TestCase used for Chevah project. """ from __future__ import print_function from __future__ import division from __future__ import absolute_import from six import text_type from six.moves import range import contextlib import inspect import threading import os import platform import socket import sys import time from bunch import Bunch from mock import patch, Mock from nose import SkipTest try: from twisted.internet.defer import Deferred from twisted.internet.posixbase import ( _SocketWaker, _UnixWaker, _SIGCHLDWaker ) from twisted.python.failure import Failure except ImportError: # Twisted support is optional. _SocketWaker = None _UnixWaker = None _SIGCHLDWaker = None from chevah.compat import ( DefaultAvatar, LocalFilesystem, process_capabilities, system_users, SuperAvatar, ) from chevah.compat.administration import os_administration from chevah.compat.testing.assertion import AssertionMixin from chevah.compat.testing.mockup import mk from chevah.compat.testing.constant import ( TEST_NAME_MARKER, ) from chevah.compat.testing.filesystem import LocalTestFilesystem # For Python below 2.7 we use the separate unittest2 module. # It comes by default in Python 2.7. if sys.version_info[0:2] < (2, 7): from unittest2 import TestCase # Shut up you linter. TestCase else: from unittest import TestCase try: # Import reactor last in case some other modules are changing the reactor. from twisted.internet import reactor except ImportError: reactor = None def _get_hostname(): """ Return hostname as resolved by default DNS resolver. """ return socket.gethostname() class TwistedTestCase(TestCase): """ Test case for Twisted specific code. Provides support for running deferred and start/stop the reactor during tests. """ # Number of second to wait for a deferred to have a result. DEFERRED_TIMEOUT = 1 # List of names for delayed calls which should not be considered as # required to wait for them when running the reactor. EXCEPTED_DELAYED_CALLS = [] EXCEPTED_READERS = [ _UnixWaker, _SocketWaker, _SIGCHLDWaker, ] # Scheduled event to stop waiting for a deferred. _reactor_timeout_call = None def setUp(self): super(TwistedTestCase, self).setUp() self._timeout_reached = False self._reactor_timeout_failure = None @property def _caller_success_member(self): """ Retrieve the 'success' member from the None test case. """ success = None for i in range(2, 6): try: success = inspect.stack()[i][0].f_locals['success'] break except KeyError: success = None if success is None: raise AssertionError('Failed to find "success" attribute.') return success def tearDown(self): try: if self._caller_success_member: # Check for a clean reactor at shutdown, only if test # passed. self.assertIsNone(self._reactor_timeout_failure) self._assertReactorIsClean() finally: self._cleanReactor() super(TwistedTestCase, self).tearDown() def _reactorQueueToString(self): """ Return a string representation of all delayed calls from reactor queue. """ result = [] for delayed in reactor.getDelayedCalls(): # noqa:cover result.append(text_type(delayed.func)) return '\n'.join(result) def _threadPoolQueue(self): """ Return current tasks of thread Pool, or [] when threadpool does not exists. This should only be called at cleanup as it removes elements from the Twisted thread queue, which will never be called. """ if not reactor.threadpool: return [] result = [] while len(reactor.threadpool._team._pending): result.append(reactor.threadpool._team._pending.pop()) return result def _threadPoolThreads(self): """ Return current threads from pool, or empty list when threadpool does not exists. """ if not reactor.threadpool: return [] else: return reactor.threadpool.threads def _threadPoolWorking(self): """ Return working thread from pool, or empty when threadpool does not exists or has no job. """ if not reactor.threadpool: return [] else: return reactor.threadpool.working @classmethod def _cleanReactor(cls): """ Remove all delayed calls, readers and writers from the reactor. This is only for cleanup purpose and should not be used by normal tests. """ if not reactor: return try: reactor.removeAll() except (RuntimeError, KeyError): # FIXME:863: # When running threads tests the reactor touched from the test # case itself which run in one tread and from the fixtures/cleanup # code which is executed from another thread. # removeAll might fail since it detects that internal state # is changed from other source. pass reactor.threadCallQueue = [] for delayed_call in reactor.getDelayedCalls(): try: delayed_call.cancel() except (ValueError, AttributeError): # AlreadyCancelled and AlreadyCalled are ValueError. # Might be canceled from the separate thread. # AttributeError can occur when we do multi-threading. pass def _raiseReactorTimeoutError(self, timeout): """ Signal an timeout error while executing the reactor. """ self._timeout_reached = True failure = AssertionError( 'Reactor took more than %.2f seconds to execute.' % timeout) self._reactor_timeout_failure = failure def _initiateTestReactor(self, timeout): """ Do the steps required to initiate a reactor for testing. """ self._timeout_reached = False # Set up timeout. self._reactor_timeout_call = reactor.callLater( timeout, self._raiseReactorTimeoutError, timeout) # Don't start the reactor if it is already started. # This can happen if we prevent stop in a previous run. if reactor._started: return reactor._startedBefore = False reactor._started = False reactor._justStopped = False reactor.startRunning() def _iterateTestReactor(self, debug=False): """ Iterate the reactor. """ reactor.runUntilCurrent() if debug: # noqa:cover # When debug is enabled with iterate using a small delay in steps, # to have a much better debug output. # Otherwise the debug messages will flood the output. print ( u'delayed: %s\n' u'threads: %s\n' u'writers: %s\n' u'readers: %s\n' u'threadpool size: %s\n' u'threadpool threads: %s\n' u'threadpool working: %s\n' u'\n' % ( self._reactorQueueToString(), reactor.threadCallQueue, reactor.getWriters(), reactor.getReaders(), reactor.getThreadPool().q.qsize(), self._threadPoolThreads(), self._threadPoolWorking(), ) ) t2 = reactor.timeout() # For testing we want to force to reactor to wake at an # interval of at most 1 second. if t2 is None or t2 > 1: t2 = 0.1 t = reactor.running and t2 reactor.doIteration(t) else: # FIXME:4428: # When not executed in debug mode, some test will fail as they # will not spin the reactor. # To not slow down all the tests, we run with a very small value. reactor.doIteration(0.000001) def _shutdownTestReactor(self, prevent_stop=False): """ Called at the end of a test reactor run. When prevent_stop=True, the reactor will not be stopped. """ if not self._timeout_reached: # Everything fine, disable timeout. if ( self._reactor_timeout_call and not self._reactor_timeout_call.cancelled ): self._reactor_timeout_call.cancel() if prevent_stop: # Don't continue with stop procedure. return # Let the reactor know that we want to stop reactor. reactor.stop() # Let the reactor run one more time to execute the stop code. reactor.iterate() # Set flag to fake a clean reactor. reactor._startedBefore = False reactor._started = False reactor._justStopped = False reactor.running = False # Start running has consumed the startup events, so we need # to restore them. reactor.addSystemEventTrigger( 'during', 'startup', reactor._reallyStartRunning) def _assertReactorIsClean(self): """ Check that the reactor has no delayed calls, readers or writers. This should only be called at teardown. """ if reactor is None: return def raise_failure(location, reason): raise AssertionError( 'Reactor is not clean. %s: %s' % (location, reason)) if reactor._started: # noqa:cover # Reactor was not stopped, so stop it before raising the error. self._shutdownTestReactor() raise AssertionError('Reactor was not stopped.') # Look at threads queue. if len(reactor.threadCallQueue) > 0: raise_failure('queued threads', reactor.threadCallQueue) if reactor.threadpool and len(reactor.threadpool.working) > 0: raise_failure('active threads', reactor.threadCallQueue) pool_queue = self._threadPoolQueue() if pool_queue: raise_failure('threadpoool queue', pool_queue) if self._threadPoolWorking(): raise_failure('threadpoool working', self._threadPoolWorking()) if self._threadPoolThreads(): raise_failure('threadpoool threads', self._threadPoolThreads()) if len(reactor.getWriters()) > 0: # noqa:cover raise_failure('writers', text_type(reactor.getWriters())) for reader in reactor.getReaders(): excepted = False for reader_type in self.EXCEPTED_READERS: if isinstance(reader, reader_type): excepted = True break if not excepted: # noqa:cover raise_failure('readers', text_type(reactor.getReaders())) for delayed_call in reactor.getDelayedCalls(): if delayed_call.active(): delayed_str = self._getDelayedCallName(delayed_call) if delayed_str in self.EXCEPTED_DELAYED_CALLS: continue raise_failure('delayed calls', delayed_str) def _runDeferred( self, deferred, timeout=None, debug=False, prevent_stop=False): """ This is low level method. In most tests you would like to use `getDeferredFailure` or `getDeferredResult`. Run the deferred in the reactor loop. Starts the reactor, waits for deferred execution, raises error in timeout, stops the reactor. This will do recursive calls, in case the original deferred returns another deferred. Usage:: checker = mk.credentialsChecker() credentials = mk.credentials() deferred = checker.requestAvatarId(credentials) self._runDeferred(deferred) self.assertIsNotFailure(deferred) self.assertEqual('something', deferred.result) """ if not isinstance(deferred, Deferred): raise AssertionError('This is not a deferred.') if timeout is None: timeout = self.DEFERRED_TIMEOUT try: self._initiateTestReactor(timeout=timeout) self._executeDeferred(deferred, timeout, debug=debug) finally: self._shutdownTestReactor( prevent_stop=prevent_stop) def _executeDeferred(self, deferred, timeout, debug): """ Does the actual deferred execution. """ if not deferred.called: deferred_done = False while not deferred_done: self._iterateTestReactor(debug=debug) deferred_done = deferred.called if self._timeout_reached: raise AssertionError( 'Deferred took more than %d to execute.' % timeout) # Check executing all deferred from chained callbacks. result = deferred.result while isinstance(result, Deferred): self._executeDeferred(result, timeout=timeout, debug=debug) result = deferred.result def executeReactor(self, timeout=None, debug=False, run_once=False): """ Run reactor until no more delayed calls, readers or writers or threads are in the queues. Set run_once=True to only run the reactor once. This is useful if you have persistent deferred which will be removed only at the end of test. Only use this for very high level integration code, where you don't have the change to get a "root" deferred. In most tests you would like to use one of the `getDeferredFailure` or `getDeferredResult`. Usage:: protocol = mk.makeFTPProtocol() transport = mk.makeStringTransportProtocol() protocol.makeConnection(transport) transport.protocol = protocol protocol.lineReceived('FEAT') self.executeReactor() result = transport.value() self.assertStartsWith('211-Features:\n', result) """ if timeout is None: timeout = self.DEFERRED_TIMEOUT self._initiateTestReactor(timeout=timeout) # Set it to True to enter the first loop. have_callbacks = True while have_callbacks and not self._timeout_reached: self._iterateTestReactor(debug=debug) have_callbacks = False # Check for active jobs in thread pool. if reactor.threadpool: if ( reactor.threadpool.working or (reactor.threadpool.q.qsize() > 0) ): time.sleep(0.01) have_callbacks = True continue # Look at delayed calls. for delayed in reactor.getDelayedCalls(): # We skip our own timeout call. if delayed is self._reactor_timeout_call: continue if not delayed.func: # Was already called. continue delayed_str = self._getDelayedCallName(delayed) is_exception = False for excepted_callback in self.EXCEPTED_DELAYED_CALLS: if excepted_callback in delayed_str: is_exception = True if not is_exception: # No need to look for other delayed calls. have_callbacks = True break # No need to look for other things as we already know that we need # to wait at least for delayed calls. if have_callbacks: continue if run_once: if have_callbacks: raise AssertionError( 'Reactor queue still contains delayed deferred.\n' '%s' % (self._reactorQueueToString())) break # Look at writers buffers: if len(reactor.getWriters()) > 0: have_callbacks = True continue for reader in reactor.getReaders(): have_callbacks = True for excepted_reader in self.EXCEPTED_READERS: if isinstance(reader, excepted_reader): have_callbacks = False break if have_callbacks: break if have_callbacks: continue # Look at threads queue and active thread. if len(reactor.threadCallQueue) > 0: have_callbacks = True continue if reactor.threadpool and len(reactor.threadpool.working) > 0: have_callbacks = True continue self._shutdownTestReactor() def executeDelayedCalls(self, timeout=None, debug=False): """ Run the reactor until no more delayed calls are scheduled. This will wait for delayed calls to be executed and will not stop the reactor. """ if timeout is None: timeout = self.DEFERRED_TIMEOUT self._initiateTestReactor(timeout=timeout) while not self._timeout_reached: self._iterateTestReactor(debug=debug) delayed_calls = reactor.getDelayedCalls() try: delayed_calls.remove(self._reactor_timeout_call) except ValueError: # noqa:cover # Timeout might be no longer be there. pass if not delayed_calls: break self._shutdownTestReactor(prevent_stop=True) if self._reactor_timeout_failure is not None: self._reactor_timeout_failure = None # We stop the reactor on failures. self._shutdownTestReactor() raise AssertionError( 'executeDelayedCalls took more than %s' % (timeout,)) def executeReactorUntil( self, callable, timeout=None, debug=False, prevent_stop=True): """ Run the reactor until callable returns `True`. """ if timeout is None: timeout = self.DEFERRED_TIMEOUT self._initiateTestReactor(timeout=timeout) while not self._timeout_reached: self._iterateTestReactor(debug=debug) if callable(reactor): break self._shutdownTestReactor(prevent_stop=prevent_stop) def iterateReactor(self, count=1, timeout=None, debug=False): """ Iterate the reactor without stopping it. """ iterations = [False] * (count - 1) iterations.append(True) self.executeReactorUntil( lambda _: iterations.pop(0), timeout=timeout, debug=debug) def iterateReactorWithStop(self, count=1, timeout=None, debug=False): """ Iterate the reactor and stop it at the end. """ iterations = [False] * (count - 1) iterations.append(True) self.executeReactorUntil( lambda _: iterations.pop(0), timeout=timeout, debug=debug, prevent_stop=False, ) def iterateReactorForSeconds(self, duration=1, debug=False): """ Iterate the reactor for `duration` seconds.. """ start = time.time() self.executeReactorUntil( lambda _: time.time() - start > duration, timeout=duration + 0.1, debug=debug, prevent_stop=False, ) def _getDelayedCallName(self, delayed_call): """ Return a string representation of the delayed call. """ raw_name = text_type(delayed_call.func) raw_name = raw_name.replace('<function ', '') raw_name = raw_name.replace('<bound method ', '') return raw_name.split(' ', 1)[0] def getDeferredFailure( self, deferred, timeout=None, debug=False, prevent_stop=False): """ Run the deferred and return the failure. Usage:: checker = mk.credentialsChecker() credentials = mk.credentials() deferred = checker.requestAvatarId(credentials) failure = self.getDeferredFailure(deferred) self.assertFailureType(AuthenticationError, failure) """ self._runDeferred( deferred, timeout=timeout, debug=debug, prevent_stop=prevent_stop, ) self.assertIsFailure(deferred) failure = deferred.result self.ignoreFailure(deferred) return failure def successResultOf(self, deferred): """ Return the current success result of C{deferred} or raise C{self.failException}. @param deferred: A L{Deferred<twisted.internet.defer.Deferred>} which has a success result. This means L{Deferred.callback<twisted.internet.defer.Deferred.callback>} or L{Deferred.errback<twisted.internet.defer.Deferred.errback>} has been called on it and it has reached the end of its callback chain and the last callback or errback returned a non-L{failure.Failure}. @type deferred: L{Deferred<twisted.internet.defer.Deferred>} @raise SynchronousTestCase.failureException: If the L{Deferred<twisted.internet.defer.Deferred>} has no result or has a failure result. @return: The result of C{deferred}. """ # FIXME:1370: # Remove / re-route this code after upgrading to Twisted 13.0. result = [] deferred.addBoth(result.append) if not result: self.fail( "Success result expected on %r, found no result instead" % ( deferred,)) elif isinstance(result[0], Failure): self.fail( "Success result expected on %r, " "found failure result instead:\n%s" % ( deferred, result[0].getBriefTraceback().decode( 'utf-8', errors='replace'))) else: return result[0] def failureResultOf(self, deferred, *expectedExceptionTypes): """ Return the current failure result of C{deferred} or raise C{self.failException}. @param deferred: A L{Deferred<twisted.internet.defer.Deferred>} which has a failure result. This means L{Deferred.callback<twisted.internet.defer.Deferred.callback>} or L{Deferred.errback<twisted.internet.defer.Deferred.errback>} has been called on it and it has reached the end of its callback chain and the last callback or errback raised an exception or returned a L{failure.Failure}. @type deferred: L{Deferred<twisted.internet.defer.Deferred>} @param expectedExceptionTypes: Exception types to expect - if provided, and the the exception wrapped by the failure result is not one of the types provided, then this test will fail. @raise SynchronousTestCase.failureException: If the L{Deferred<twisted.internet.defer.Deferred>} has no result, has a success result, or has an unexpected failure result. @return: The failure result of C{deferred}. @rtype: L{failure.Failure} """ # FIXME:1370: # Remove / re-route this code after upgrading to Twisted 13 result = [] deferred.addBoth(result.append) if not result: self.fail( "Failure result expected on %r, found no result instead" % ( deferred,)) elif not isinstance(result[0], Failure): self.fail( "Failure result expected on %r, " "found success result (%r) instead" % (deferred, result[0])) elif (expectedExceptionTypes and not result[0].check(*expectedExceptionTypes)): expectedString = " or ".join([ '.'.join((t.__module__, t.__name__)) for t in expectedExceptionTypes]) self.fail( "Failure of type (%s) expected on %r, " "found type %r instead: %s" % ( expectedString, deferred, result[0].type, result[0].getBriefTraceback().decode( 'utf-8', errors='replace'))) else: return result[0] def assertNoResult(self, deferred): """ Assert that C{deferred} does not have a result at this point. If the assertion succeeds, then the result of C{deferred} is left unchanged. Otherwise, any L{failure.Failure} result is swallowed. @param deferred: A L{Deferred<twisted.internet.defer.Deferred>} without a result. This means that neither L{Deferred.callback<twisted.internet.defer.Deferred.callback>} nor L{Deferred.errback<twisted.internet.defer.Deferred.errback>} has been called, or that the L{Deferred<twisted.internet.defer.Deferred>} is waiting on another L{Deferred<twisted.internet.defer.Deferred>} for a result. @type deferred: L{Deferred<twisted.internet.defer.Deferred>} @raise SynchronousTestCase.failureException: If the L{Deferred<twisted.internet.defer.Deferred>} has a result. """ # FIXME:1370: # Remove / re-route this code after upgrading to Twisted 13 result = [] def cb(res): result.append(res) return res deferred.addBoth(cb) if result: # If there is already a failure, the self.fail below will # report it, so swallow it in the deferred deferred.addErrback(lambda _: None) self.fail( "No result expected on %r, found %r instead" % ( deferred, result[0])) def getDeferredResult( self, deferred, timeout=None, debug=False, prevent_stop=False): """ Run the deferred and return the result. Usage:: checker = mk.credentialsChecker() credentials = mk.credentials() deferred = checker.requestAvatarId(credentials) result = self.getDeferredResult(deferred) self.assertEqual('something', result) """ self._runDeferred( deferred, timeout=timeout, debug=debug, prevent_stop=prevent_stop, ) self.assertIsNotFailure(deferred) return deferred.result def assertWasCalled(self, deferred): """ Check that deferred was called. """ if not deferred.called: raise AssertionError('This deferred was not called yet.') def ignoreFailure(self, deferred): """ Ignore the current failure on the deferred. It transforms an failure into result `None` so that the failure will not be raised at reactor shutdown for not being handled. """ deferred.addErrback(lambda failure: None) def assertIsFailure(self, deferred): """ Check that deferred is a failure. """ if not isinstance(deferred.result, Failure): raise AssertionError('Deferred is not a failure.') def assertIsNotFailure(self, deferred): """ Raise assertion error if deferred is a Failure. The failed deferred is handled by this method, to avoid propagating the error into the reactor. """ self.assertWasCalled(deferred) if isinstance(deferred.result, Failure): error = deferred.result self.ignoreFailure(deferred) raise AssertionError( 'Deferred contains a failure: %s' % (error)) def _get_os_version(): """ On non-Linux this is just the os_name. On Linux is the distribution name and the version. On Windows it is the `nt` followed by the major and minor NT version. It is not the marketing name. We only support the Windows NT family. See: https://en.wikipedia.org/wiki/Windows_NT#Releases On OSX it returns `osx` followed by the version. It is not the version of the underlying Darwin OS. See: https://en.wikipedia.org/wiki/MacOS#Release_history """ if os.name == 'nt': parts = platform.version().split('.') return 'nt-%s.%s' % (parts[0], parts[1]) # We are now in Unix zone. os_name = os.uname()[0].lower() if os_name == 'darwin': parts = platform.mac_ver()[0].split('.') return 'osx-%s.%s' % (parts[0], parts[1]) if os_name == 'sunos': parts = platform.release().split('.') return 'solaris-%s' % (parts[1],) if os_name == 'aix': # noqa:cover return 'aix-%s.%s' % (platform.version(), platform.release()) if os_name != 'linux': return process_capabilities.os_name # We delay the import as it will call lsb_release. import ld distro_name = ld.id() if distro_name == 'arch': # Arch has no version. return 'arch' if distro_name in ['centos', 'ol']: # Normalize all RHEL variants. distro_name = 'rhel' distro_version = ld.version().split('.', 1)[0] return '%s-%s' % (distro_name, distro_version) def _get_cpu_type(): """ Return the CPU type as used in the brink.sh script. """ base = platform.processor() if base == 'aarch64': return 'arm64' if base == 'x86_64': return 'x64' return base _CI_NAMES = Bunch( LOCAL='local', GITHUB='github-actions', TRAVIS='travis', BUILDBOT='buildbot', UNKNOWN='unknown-ci', AZURE='azure-pipelines', ) def _get_ci_name(): """ Return the name of the CI on which the tests are currently executed. """ if os.environ.get('BUILDBOT', '').lower() == 'true': return _CI_NAMES.BUILDBOT if os.environ.get('GITHUB_ACTIONS', '').lower() == 'true': return _CI_NAMES.GITHUB if os.environ.get('TRAVIS', '').lower() == 'true': return _CI_NAMES.TRAVIS if os.environ.get('INFRASTRUCTURE', '') == 'AZUREPIPELINES': return _CI_NAMES.AZURE if os.environ.get('CI', '').lower() == 'true': return _CI_NAMES.UNKNOWN return _CI_NAMES.LOCAL class ChevahTestCase(TwistedTestCase, AssertionMixin): """ Test case for Chevah tests. Checks that temporary folder is clean at exit. """ os_name = process_capabilities.os_name os_family = process_capabilities.os_family os_version = _get_os_version() cpu_type = process_capabilities.cpu_type ci_name = _get_ci_name() CI = _CI_NAMES TEST_LANGUAGE = os.getenv('TEST_LANG', 'EN') # List of partial thread names to ignore during the tearDown. # No need for the full thread name excepted_threads = [ 'MainThread', 'threaded_reactor', 'GlobalPool-WorkerHandler', 'GlobalPool-TaskHandler', 'GlobalPool-ResultHandler', 'PoolThread-twisted.internet.reactor', ] # We assume that hostname does not change during test and this # should save a few DNS queries. hostname = _get_hostname() Bunch = Bunch Mock = Mock #: Obsolete. Please use self.patch and self.patchObject. Patch = patch _environ_user = None _drop_user = '-' def setUp(self): super(ChevahTestCase, self).setUp() self.__cleanup__ = [] self._cleanup_stack = [] self._teardown_errors = [] self.test_segments = None def tearDown(self): self.callCleanup() self._checkTemporaryFiles() threads = threading.enumerate() if len(threads) > 1: for thread in threads: thread_name = thread.getName() if self._isExceptedThread(thread_name): continue self._teardown_errors.append(AssertionError( 'There are still active threads, ' 'beside the main thread: %s - %s' % ( thread_name, threads))) super(ChevahTestCase, self).tearDown() errors, self._teardown_errors = self._teardown_errors, None if errors: raise AssertionError('Cleanup errors: %r' % (errors,)) def _isExceptedThread(self, name): """ Return `True` if is OK for thread to exist after test is done. """ for exception in self.excepted_threads: if name in exception: return True if exception in name: return True return False def addCleanup(self, function, *args, **kwargs): """ Overwrite unit-test behaviour to run cleanup method before tearDown. """ self.__cleanup__.append((function, args, kwargs)) def callCleanup(self): """ Call all cleanup methods. If a cleanup fails, the next cleanups will continue to be called and the first failure is raised. """ for function, args, kwargs in reversed(self.__cleanup__): try: function(*args, **kwargs) except Exception as error: # noqa:cover self._teardown_errors.append(error) self.__cleanup__ = [] def enterCleanup(self): """ Called when start using stacked cleanups. """ self._cleanup_stack.append(self.__cleanup__) self.__cleanup__ = [] def exitCleanup(self): """ To be called at the end of a stacked cleanup. """ self.callCleanup() self.__cleanup__ = self._cleanup_stack.pop() @contextlib.contextmanager def stackedCleanup(self): """ Context manager for stacked cleanups. """ try: self.enterCleanup() yield finally: self.exitCleanup() def _checkTemporaryFiles(self): """ Check that no temporary files or folders are present. """ # FIXME:922: # Move all filesystem checks into a specialized class if self.test_segments: if mk.fs.isFolder(self.test_segments): mk.fs.deleteFolder( self.test_segments, recursive=True) else: mk.fs.deleteFile(self.test_segments) checks = [ self.assertTempIsClean, self.assertWorkingFolderIsClean, ] errors = [] for check in checks: try: check() except AssertionError as error: errors.append(error.message) if errors: # noqa:cover self._teardown_errors.append(AssertionError( 'There are temporary files or folders left over.\n %s' % ( '\n'.join(errors)))) def shortDescription(self): # noqa:cover """ The short description for the test. bla.bla.tests. is removed. The format is customized for Chevah Nose runner. This is only called when we run with -v or we show the error. """ class_name = text_type(self.__class__)[8:-2] class_name = class_name.replace('.Test', ':Test') tests_start = class_name.find('.tests.') + 7 class_name = class_name[tests_start:] return "%s - %s.%s" % ( self._testMethodName, class_name, self._testMethodName) def assertRaises(self, exception_class, callback=None, *args, **kwargs): """ Wrapper around the stdlib call to allow non-context usage. """ super_assertRaises = super(ChevahTestCase, self).assertRaises if callback is None: return super_assertRaises(exception_class) with super_assertRaises(exception_class) as context: callback(*args, **kwargs) return context.exception def assertSequenceEqual(self, first, second, msg, seq_type): super(ChevahTestCase, self).assertSequenceEqual( first, second, msg, seq_type) for first_element, second_element in zip(first, second): self.assertEqual(first_element, second_element) def assertDictEqual(self, first, second, msg): super(ChevahTestCase, self).assertDictEqual(first, second, msg) first_keys = sorted(first.keys()) second_keys = sorted(second.keys()) first_values = [first[key] for key in first_keys] second_values = [second[key] for key in second_keys] self.assertSequenceEqual(first_keys, second_keys, msg, list) self.assertSequenceEqual(first_values, second_values, msg, list) def assertSetEqual(self, first, second, msg): super(ChevahTestCase, self).assertSetEqual(first, second, msg) first_elements = sorted(first) second_elements = sorted(second) self.assertSequenceEqual(first_elements, second_elements, msg, list) def _baseAssertEqual(self, first, second, msg=None): """ Update to stdlib to make sure we don't compare str with unicode. """ if ( isinstance(first, text_type) and not isinstance(second, text_type) ): # noqa:cover if not msg: msg = u'First is unicode while second is str for "%s".' % ( first,) raise AssertionError(msg.encode('utf-8')) if ( not isinstance(first, text_type) and isinstance(second, text_type) ): # noqa:cover if not msg: msg = u'First is str while second is unicode for "%s".' % ( first,) raise AssertionError(msg.encode('utf-8')) return super(ChevahTestCase, self)._baseAssertEqual( first, second, msg=msg) @staticmethod def getHostname(): """ Return the hostname of the current system. """ return _get_hostname() @classmethod def initialize(cls, drop_user): """ Initialize the testing environment. """ cls._drop_user = drop_user os.environ['DROP_USER'] = drop_user if 'LOGNAME' in os.environ and 'USER' not in os.environ: os.environ['USER'] = os.environ['LOGNAME'] if 'USER' in os.environ and 'USERNAME' not in os.environ: os.environ['USERNAME'] = os.environ['USER'] if 'USERNAME' in os.environ and 'USER' not in os.environ: os.environ['USER'] = os.environ['USERNAME'] cls._environ_user = os.environ['USER'] cls.cleanTemporaryFolder() @classmethod def dropPrivileges(cls): '''Drop privileges to normal users.''' if cls._drop_user == '-': return os.environ['USERNAME'] = cls._drop_user os.environ['USER'] = cls._drop_user # Test suite should be started as root and we drop effective user # privileges. system_users.dropPrivileges(username=cls._drop_user) @staticmethod def skipTest(message=''): '''Return a SkipTest exception.''' return SkipTest(message) @property def _caller_success_member(self): '''Retrieve the 'success' member from the test case.''' success_state = None # We search starting with second stack, since first stack is the # current stack and we don't care about it. for level in inspect.stack()[1:]: try: success_state = level[0].f_locals['success'] break except KeyError: success_state = None if success_state is None: raise AssertionError('Failed to find "success" attribute.') return success_state @staticmethod def patch(*args, **kwargs): """ Helper for generic patching. """ return patch(*args, **kwargs) @staticmethod def patchObject(*args, **kwargs): """ Helper for patching objects. """ return patch.object(*args, **kwargs) def now(self): """ Return current Unix timestamp. """ return time.time() @classmethod def cleanTemporaryFolder(cls): """ Clean all test files from temporary folder. Return a list of members which were removed. """ return cls._cleanFolder(mk.fs.temp_segments) @classmethod def cleanWorkingFolder(cls): path = mk.fs.getAbsoluteRealPath('.') segments = mk.fs.getSegmentsFromRealPath(path) return cls._cleanFolder(segments, only_marked=True) @classmethod def _cleanFolder(cls, folder_segments, only_marked=False): """ Clean all test files from folder_segments. Return a list of members which were removed. """ if not mk.fs.exists(folder_segments): return [] # In case we are running the test suite as super user, # we use super filesystem for cleaning. if cls._environ_user == cls._drop_user: temp_avatar = SuperAvatar() else: temp_avatar = DefaultAvatar() temp_filesystem = LocalFilesystem(avatar=temp_avatar) temp_members = [] for member in (temp_filesystem.getFolderContent(folder_segments)): if only_marked and member.find(TEST_NAME_MARKER) == -1: continue temp_members.append(member) segments = folder_segments[:] segments.append(member) if temp_filesystem.isFolder(segments): temp_filesystem.deleteFolder(segments, recursive=True) else: temp_filesystem.deleteFile(segments) return temp_members @classmethod def getPeakMemoryUsage(cls): """ Return maximum memory usage in kilo bytes. """ if cls.os_family == 'posix': import resource return resource.getrusage(resource.RUSAGE_SELF).ru_maxrss elif cls.os_family == 'nt': from wmi import WMI local_wmi = WMI('.') query = ( u'SELECT PeakWorkingSetSize ' u'FROM Win32_Process ' u'WHERE Handle=%d' % os.getpid()) result = local_wmi.query(query.encode('utf-8')) peak_working_set_size = int(result[0].PeakWorkingSetSize) # FIXME:2099: # Windows XP reports value in bytes, instead of Kilobytes. return int(peak_working_set_size) else: raise AssertionError('OS not supported.') def folderInTemp(self, *args, **kwargs): """ Create a folder in the default temp folder and mark it for cleanup. """ kwargs['cleanup'] = self.addCleanup return mk.fs.folderInTemp(*args, **kwargs) def fileInTemp(self, *args, **kwargs): """ Create a file in the default temp folder and mark it for cleanup. """ kwargs['cleanup'] = self.addCleanup return mk.fs.fileInTemp(*args, **kwargs) def assertIn(self, target, source): """ Overwrite stdlib to swap the arguments. """ if source not in target: message = u'%s not in %s.' % (repr(source), repr(target)) raise AssertionError(message.encode('utf-8')) def assertIsInstance(self, expected_type, value, msg=None): """ Raise an exception if `value` is not an instance of `expected_type` """ # In Python 2.7 isInstance is already defined, but with swapped # arguments. if not inspect.isclass(expected_type): expected_type, value = value, expected_type if not isinstance(value, expected_type): raise AssertionError( "Expecting type %s, but got %s. %s" % ( expected_type, type(value), msg)) def tempPath(self, prefix='', suffix=''): """ Return (path, segments) for a path which is not created yet. """ return mk.fs.makePathInTemp(prefix=prefix, suffix=suffix) def tempPathCleanup(self, prefix='', suffix=''): """ Return (path, segments) for a path which is not created yet but which will be automatically removed. """ return mk.fs.pathInTemp( cleanup=self.addCleanup, prefix=prefix, suffix=suffix) def tempFile(self, content='', prefix='', suffix='', cleanup=True): """ Return (path, segments) for a new file created in temp which is auto cleaned. """ segments = mk.fs.createFileInTemp(prefix=prefix, suffix=suffix) path = mk.fs.getRealPathFromSegments(segments) if cleanup: self.addCleanup(mk.fs.deleteFile, segments) try: opened_file = mk.fs.openFileForWriting(segments) opened_file.write(content) finally: opened_file.close() return (path, segments) def tempFolder(self, name=None, prefix='', suffix=''): """ Create a new temp folder and return its path and segments, which is auto cleaned. """ segments = mk.fs.createFolderInTemp( foldername=name, prefix=prefix, suffix=suffix) path = mk.fs.getRealPathFromSegments(segments) self.addCleanup(mk.fs.deleteFolder, segments, recursive=True) return (path, segments) class FileSystemTestCase(ChevahTestCase): """ Common test case for all file-system tests using a real OS account. """ @classmethod def setUpClass(cls): # FIXME:924: # Disabled when we can not find the home folder path. if not process_capabilities.get_home_folder: raise cls.skipTest() super(FileSystemTestCase, cls).setUpClass() cls.os_user = cls.setUpTestUser() home_folder_path = system_users.getHomeFolder( username=cls.os_user.name, token=cls.os_user.token) cls.avatar = mk.makeFilesystemOSAvatar( name=cls.os_user.name, home_folder_path=home_folder_path, token=cls.os_user.token, ) cls.filesystem = LocalFilesystem(avatar=cls.avatar) @classmethod def tearDownClass(cls): if not cls.os_user.windows_create_local_profile: os_administration.deleteHomeFolder(cls.os_user) os_administration.deleteUser(cls.os_user) super(FileSystemTestCase, cls).tearDownClass() @classmethod def setUpTestUser(cls): """ Set-up OS user for file system testing. """ from chevah.compat.testing import TEST_ACCOUNT_GROUP user = mk.makeTestUser(home_group=TEST_ACCOUNT_GROUP) os_administration.addUser(user) return user def setUp(self): super(FileSystemTestCase, self).setUp() # Initialized only to clean the home folder. test_filesystem = LocalTestFilesystem(avatar=self.avatar) test_filesystem.cleanHomeFolder() class OSAccountFileSystemTestCase(FileSystemTestCase): """ Test case for tests that need a dedicated local OS account present. """ #: User will be created before running the test case and removed on #: teardown. CREATE_TEST_USER = None @classmethod def setUpTestUser(cls): """ Add `CREATE_TEST_USER` to local OS. """ os_administration.addUser(cls.CREATE_TEST_USER) return cls.CREATE_TEST_USER

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0.002481

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null

0

null

0

1

0

0afe13064838542a197bda7a6f3924d3d020b310

1,912

py

Python

generative_deep_learning/build_network.py

slaily/deep-learning-bits

cb9ce7ec539efbdfcaa023d141466f919bd31b71

[ "MIT" ]

null

generative_deep_learning/build_network.py

slaily/deep-learning-bits

cb9ce7ec539efbdfcaa023d141466f919bd31b71

[ "MIT" ]

null

generative_deep_learning/build_network.py

slaily/deep-learning-bits

cb9ce7ec539efbdfcaa023d141466f919bd31b71

[ "MIT" ]

null

from keras import layers # Single-layer LSTM model for next-character prediction model = keras.models.Sequential() model.add(layers.LSTM(128, input_shape=(maxlen, len(chars)))) model.add(layers.Dense(len(chars), activation='softmax')) # Model compilation configuration optimizer = keras.optimizers.RMSprop(lr=0.01) model.compile(loss='categorical_crossentropy', optimizer=optimizer) # Function to sample the next character given the model’s predictions def sample(preds, temperature=1.0): preds = np.asarray(preds).astype('float64') preds = np.log(preds) / temperature exp_preds = np.exp(preds) preds = exp_preds / np.sum(exp_preds) probas = np.random.multinominal(1, preds, 1) return np.argmax(probas) # Text-generation loop import sys import random # Trains the model for 60 epochs for epoch in range(1, 60): print(f'Epoch: {epoch}') model.fit(x, y, batch_size=128, epochs=1) # Selects a text seed at random start_index = random.randint(0, len(text) - maxlen - 1) generated_text = text[start_index: start_index + maxlen] print(f'--- Generating with seed: {generated_text} ---') # Tries a range of different sampling temperatures for temperature in [0.2, 0.5, 1.0, 1.2]: print(f'--- Temperature {temperature} ---') sys.stdout.write(generated_text) # Generates 400 characters, starting from the seed text for i in range(400): sampled = np.zeros((1, maxlen, len(chars))) for t, char in enumerate(generated_text): sampled[0, t, char_indices[char]] = 1. # Samples the next character preds = model.predict(sampled, verbose=0)[0] next_index = sample(preds, temperature) next_char = chars[next_index] generated_text += next_char generated_text = generated_text[1:] sys.stdout.write(next_char)

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0.147059

0.088235

0

null

0

null

0

1

0

0afe544e807773d996329c44f23a45f84862abbe

2,610

py

Python

examples/MDF/states.py

29riyasaxena/MDF

476e6950d0f14f29463eb4f6e3be518dfb2160a5

[ "Apache-2.0" ]

12

2021-01-18T20:38:21.000Z

2022-03-29T15:01:10.000Z

examples/MDF/states.py

29riyasaxena/MDF

476e6950d0f14f29463eb4f6e3be518dfb2160a5

[ "Apache-2.0" ]

101

2020-12-14T15:23:07.000Z

2022-03-31T17:06:19.000Z

examples/MDF/states.py

29riyasaxena/MDF

476e6950d0f14f29463eb4f6e3be518dfb2160a5

[ "Apache-2.0" ]

15

2020-12-04T22:37:14.000Z

2022-03-31T09:48:03.000Z

""" Example of ModECI MDF - Testing state variables """ from modeci_mdf.mdf import * import sys def main(): mod = Model(id="States") mod_graph = Graph(id="state_example") mod.graphs.append(mod_graph) ## Counter node counter_node = Node(id="counter_node") p1 = Parameter(id="increment", value=1) counter_node.parameters.append(p1) p2 = Parameter(id="count", value="count + increment") counter_node.parameters.append(p2) op1 = OutputPort(id="out_port", value=p2.id) counter_node.output_ports.append(op1) mod_graph.nodes.append(counter_node) ## Sine node... sine_node = Node(id="sine_node") sine_node.parameters.append(Parameter(id="amp", value=3)) sine_node.parameters.append(Parameter(id="period", value=0.4)) s1 = Parameter( id="level", default_initial_value=0, time_derivative="6.283185 * rate / period" ) sine_node.parameters.append(s1) s2 = Parameter( id="rate", default_initial_value=1, time_derivative="-1 * 6.283185 * level / period", ) sine_node.parameters.append(s2) op1 = OutputPort(id="out_port", value="amp * level") sine_node.output_ports.append(op1) mod_graph.nodes.append(sine_node) new_file = mod.to_json_file("%s.json" % mod.id) new_file = mod.to_yaml_file("%s.yaml" % mod.id) if "-run" in sys.argv: verbose = True # verbose = False from modeci_mdf.utils import load_mdf, print_summary from modeci_mdf.execution_engine import EvaluableGraph eg = EvaluableGraph(mod_graph, verbose) dt = 0.01 duration = 2 t = 0 recorded = {} times = [] s = [] while t <= duration: times.append(t) print("====== Evaluating at t = %s ======" % (t)) if t == 0: eg.evaluate() # replace with initialize? else: eg.evaluate(time_increment=dt) s.append(eg.enodes["sine_node"].evaluable_outputs["out_port"].curr_value) t += dt if "-nogui" not in sys.argv: import matplotlib.pyplot as plt plt.plot(times, s) plt.show() if "-graph" in sys.argv: mod.to_graph_image( engine="dot", output_format="png", view_on_render=False, level=3, filename_root="states", only_warn_on_fail=True, # Makes sure test of this doesn't fail on Windows on GitHub Actions ) return mod_graph if __name__ == "__main__": main()

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0.081191

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0.058187

0

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2,610

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false

0

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0.102941

0.029412

0

null

0

null

0

1

0

e40074d263a071da246090065d0ad8ae39b4da28

20,118

py

Python

gaia_tools/xmatch/__init__.py

henrysky/gaia_tools

c151a1d8f6896d8ef5a379291baa8a1f027bd53b

[ "MIT" ]

44

2016-09-13T06:37:46.000Z

2022-02-03T20:59:56.000Z

gaia_tools/xmatch/__init__.py

henrysky/gaia_tools

c151a1d8f6896d8ef5a379291baa8a1f027bd53b

[ "MIT" ]

24

2016-10-18T23:26:15.000Z

2020-12-08T18:24:27.000Z

gaia_tools/xmatch/__init__.py

henrysky/gaia_tools

c151a1d8f6896d8ef5a379291baa8a1f027bd53b

[ "MIT" ]

18

2016-10-18T22:26:45.000Z

2021-08-20T09:07:31.000Z

# Tools for cross-matching catalogs import csv import sys import os import os.path import platform import shutil import subprocess import tempfile import warnings WIN32= platform.system() == 'Windows' import numpy import astropy.coordinates as acoords from astropy.table import Table from astropy import units as u from ..load.download import _ERASESTR def xmatch(cat1,cat2,maxdist=2, colRA1='RA',colDec1='DEC',epoch1=None, colRA2='RA',colDec2='DEC',epoch2=None, colpmRA2='pmra',colpmDec2='pmdec', swap=False, col_field=None): """ NAME: xmatch PURPOSE: cross-match two catalogs (incl. proper motion in cat2 if epochs are different) INPUT: cat1 - First catalog cat2 - Second catalog maxdist= (2) maximum distance in arcsec colRA1= ('RA') name of the tag in cat1 with the right ascension in degree in cat1 (assumed to be ICRS) colDec1= ('DEC') name of the tag in cat1 with the declination in degree in cat1 (assumed to be ICRS) epoch1= (2000.) epoch of the coordinates in cat1 colRA2= ('RA') name of the tag in cat2 with the right ascension in degree in cat2 (assumed to be ICRS) colDec2= ('DEC') name of the tag in cat2 with the declination in degree in cat2 (assumed to be ICRS) epoch2= (2000.) epoch of the coordinates in cat2 colpmRA2= ('pmra') name of the tag in cat2 with the proper motion in right ascension in degree in cat2 (assumed to be ICRS; includes cos(Dec)) [only used when epochs are different] colpmDec2= ('pmdec') name of the tag in cat2 with the proper motion in declination in degree in cat2 (assumed to be ICRS) [only used when epochs are different] swap= (False) if False, find closest matches in cat2 for each cat1 source, if False do the opposite (important when one of the catalogs has duplicates) col_field= (None) if None, simply cross-match on RA and Dec; if a string, then cross-match on RA and Dec with additional matching in the data tag specified by the string OUTPUT: (index into cat1 of matching objects, index into cat2 of matching objects, angular separation between matching objects) HISTORY: 2016-09-12 - Written - Bovy (UofT) 2016-09-21 - Account for Gaia epoch 2015 - Bovy (UofT) 2019-07-07 - add additional catalog field matching - Leung (UofT) """ if epoch1 is None: if 'ref_epoch' in cat1.dtype.fields: epoch1= cat1['ref_epoch'] else: epoch1= 2000. if epoch2 is None: if 'ref_epoch' in cat2.dtype.fields: epoch2= cat2['ref_epoch'] else: epoch2= 2000. _check_epoch(cat1,epoch1) _check_epoch(cat2,epoch2) depoch= epoch2-epoch1 if numpy.any(depoch != 0.): # Use proper motion to get both catalogs at the same time dra=cat2[colpmRA2]/numpy.cos(cat2[colDec2]/180.*numpy.pi)\ /3600000.*depoch ddec= cat2[colpmDec2]/3600000.*depoch # Don't shift objects with non-existing proper motion dra[numpy.isnan(cat2[colpmRA2])]= 0. ddec[numpy.isnan(cat2[colpmDec2])]= 0. else: dra= 0. ddec= 0. mc1= acoords.SkyCoord(cat1[colRA1],cat1[colDec1], unit=(u.degree, u.degree),frame='icrs') mc2= acoords.SkyCoord(cat2[colRA2]-dra,cat2[colDec2]-ddec, unit=(u.degree, u.degree),frame='icrs') if col_field is not None: try: # check if the field actually exists in both cat1/cat2 cat1[col_field] cat2[col_field] except KeyError: # python 2/3 format string raise KeyError("'%s' does not exist in both catalog" % col_field) uniques = numpy.unique(cat1[col_field]) if swap: # times neg one to indicate those indices untouch will be noticed at the end and filtered out d2d = numpy.ones(len(cat2)) * -1. idx = numpy.zeros(len(cat2), dtype=int) else: d2d = numpy.ones(len(cat1)) * -1. idx = numpy.zeros(len(cat1), dtype=int) for unique in uniques: # loop over the class idx_1 = numpy.arange(cat1[colRA1].shape[0])[cat1[col_field] == unique] idx_2 = numpy.arange(cat2[colRA2].shape[0])[cat2[col_field] == unique] if idx_1.shape[0] == 0 or idx_2.shape[0] == 0: # the case where a class only exists in one but not the other continue if swap: temp_idx, temp_d2d, d3d = mc2[idx_2].match_to_catalog_sky(mc1[idx_1]) m1 = numpy.arange(len(cat2)) idx[cat2[col_field] == unique] = idx_1[temp_idx] d2d[cat2[col_field] == unique] = temp_d2d else: temp_idx, temp_d2d, d3d = mc1[idx_1].match_to_catalog_sky(mc2[idx_2]) m1 = numpy.arange(len(cat1)) idx[cat1[col_field] == unique] = idx_2[temp_idx] d2d[cat1[col_field] == unique] = temp_d2d d2d = d2d * temp_d2d.unit # make sure finally we have an unit on d2d array s.t. "<" operation can complete else: if swap: idx,d2d,d3d = mc2.match_to_catalog_sky(mc1) m1= numpy.arange(len(cat2)) else: idx,d2d,d3d = mc1.match_to_catalog_sky(mc2) m1= numpy.arange(len(cat1)) # to make sure filtering out all neg ones which are untouched mindx= ((d2d < maxdist*u.arcsec) & (0.*u.arcsec <= d2d)) m1= m1[mindx] m2= idx[mindx] if swap: return (m2,m1,d2d[mindx]) else: return (m1,m2,d2d[mindx]) def cds(cat,xcat='vizier:I/350/gaiaedr3',maxdist=2,colRA='RA',colDec='DEC', selection='best',epoch=None,colpmRA='pmra',colpmDec='pmdec', savefilename=None,gaia_all_columns=False): """ NAME: cds PURPOSE: Cross-match against a catalog in the CDS archive using the CDS cross-matching service (http://cdsxmatch.u-strasbg.fr/xmatch); uses the curl interface INPUT: cat - a catalog to cross match, requires 'RA' and 'DEC' keywords (see below) xcat= ('vizier:I/350/gaiaedr3') name of the catalog to cross-match against, in a format understood by the CDS cross-matching service (see http://cdsxmatch.u-strasbg.fr/xmatch/doc/available-tables.html; things like 'vizier:Tycho2' or 'vizier:I/345/gaia2') maxdist= (2) maximum distance in arcsec colRA= ('RA') name of the tag in cat with the right ascension colDec= ('DEC') name of the tag in cat with the declination selection= ('best') select either all matches or the best match according to CDS (see 'selection' at http://cdsxmatch.u-strasbg.fr/xmatch/doc/API-calls.html) epoch= (2000.) epoch of the coordinates in cat colpmRA= ('pmra') name of the tag in cat with the proper motion in right ascension in degree in cat (assumed to be ICRS; includes cos(Dec)) [only used when epoch != 2000.] colpmDec= ('pmdec') name of the tag in cat with the proper motion in declination in degree in cat (assumed to be ICRS) [only used when epoch != 2000.] gaia_all_columns= (False) set to True if you are matching against Gaia DR2 and want *all* columns returned; this runs a query at the Gaia Archive, which may or may not work... savefilename= (None) if set, save the output from CDS to this path; can match back using cds_matchback OUTPUT: (xcat entries for those that match, indices into cat of matching sources: index[0] is cat index of xcat[0]) HISTORY: 2016-09-12 - Written based on RC catalog code - Bovy (UofT) 2016-09-21 - Account for Gaia epoch 2015 - Bovy (UofT) 2018-05-08 - Added gaia_all_columns - Bovy (UofT) """ if epoch is None: if 'ref_epoch' in cat.dtype.fields: epoch= cat['ref_epoch'] else: epoch= 2000. _check_epoch(cat,epoch) depoch= epoch-2000. if numpy.any(depoch != 0.): # Use proper motion to get both catalogs at the same time dra=cat[colpmRA]/numpy.cos(cat[colDec]/180.*numpy.pi)\ /3600000.*depoch ddec= cat[colpmDec]/3600000.*depoch # Don't shift objects with non-existing proper motion dra[numpy.isnan(cat[colpmRA])]= 0. ddec[numpy.isnan(cat[colpmDec])]= 0. else: dra= numpy.zeros(len(cat)) ddec= numpy.zeros(len(cat)) if selection != 'all': selection= 'best' if selection == 'all': raise NotImplementedError("selection='all' CDS cross-match not currently implemented") # Write positions posfilename= tempfile.mktemp('.csv',dir=os.getcwd()) resultfilename= tempfile.mktemp('.csv',dir=os.getcwd()) with open(posfilename,'w') as csvfile: wr= csv.writer(csvfile,delimiter=',',quoting=csv.QUOTE_MINIMAL) wr.writerow(['RA','DEC']) for ii in range(len(cat)): wr.writerow([(cat[ii][colRA]-dra[ii]+360.) % 360., cat[ii][colDec]]-ddec[ii]) _cds_match_batched(resultfilename,posfilename,maxdist,selection,xcat) # Directly match on input RA ma= cds_load(resultfilename) if gaia_all_columns: from astroquery.gaia import Gaia # Write another temporary file with the XML output of the cross-match tab= Table(numpy.array([ma['source_id'],ma['RA'],ma['DEC']]).T, names=('source_id','RA','DEC'), dtype=('int64','float64','float64')) xmlfilename= tempfile.mktemp('.xml',dir=os.getcwd()) tab.write(xmlfilename,format='votable') #get the data release.... table_identifier = xcat.split('/')[-1] if table_identifier == 'gaia2': table_identifier = 'gaiadr2' try: job= Gaia.launch_job_async( """select g.*, m.RA as mRA, m.DEC as mDEC from %s.gaia_source as g inner join tap_upload.my_table as m on m.source_id = g.source_id""" % table_identifier, upload_resource=xmlfilename, upload_table_name="my_table") ma= job.get_results() except: print("gaia_tools.xmath.cds failed to retrieve all gaia columns, returning just the default returned by the CDS xMatch instead...") else: ma.rename_column('mra','RA') ma.rename_column('mdec','DEC') finally: os.remove(xmlfilename) # Remove temporary files os.remove(posfilename) if savefilename is None: os.remove(resultfilename) else: shutil.move(resultfilename,savefilename) # Match back to the original catalog mai= cds_matchback(cat,ma,colRA=colRA,colDec=colDec,epoch=epoch, colpmRA=colpmRA,colpmDec=colpmDec) return (ma,mai) def _cds_match_batched(resultfilename,posfilename,maxdist,selection,xcat, nruns_necessary=1): """CDS xMatch (sometimes?) fails for large matches, because of a time-out, so we recursively split until the batches are small enough to not fail""" # Figure out which of the hierarchy we are running try: runs= ''.join([str(int(r)-1) for r in posfilename.split('csv.')[-1].split('.')]) except ValueError: runs= '' nruns= 2**len(runs) if nruns >= nruns_necessary: # Only run this level's match if we don't already know that we should # be using smaller batches _cds_basic_match(resultfilename,posfilename,maxdist,selection,xcat) try: ma= cds_load(resultfilename) except ValueError: # Assume this is the time-out failure pass else: return nruns # xMatch failed because of time-out, split posfilename1= posfilename+'.1' posfilename2= posfilename+'.2' resultfilename1= resultfilename+'.1' resultfilename2= resultfilename+'.2' # Figure out which of the hierarchy we are running runs= ''.join([str(int(r)-1) for r in posfilename1.split('csv.')[-1].split('.')]) nruns= 2**len(runs) thisrun1= 1+int(runs,2) thisrun2= 1+int(''.join([str(int(r)-1) for r in posfilename2.split('csv.')[-1].split('.')]),2) # Count the number of objects with open(posfilename,'r') as posfile: num_lines= sum(1 for line in posfile) # Write the header line with open(posfilename1,'w') as posfile1: with open(posfilename,'r') as posfile: posfile1.write(posfile.readline()) with open(posfilename2,'w') as posfile2: with open(posfilename,'r') as posfile: posfile2.write(posfile.readline()) # Cut in half cnt= 0 with open(posfilename,'r') as posfile: with open(posfilename1,'a') as posfile1: with open(posfilename2,'a') as posfile2: for line in posfile: if cnt == 0: cnt+= 1 continue if cnt < num_lines//2: posfile1.write(line) cnt+= 1 # Can stop counting once this if is done else: posfile2.write(line) # Run each sys.stdout.write('\r'+"Working on CDS xMatch batch {} / {} ...\r"\ .format(thisrun1,nruns)) sys.stdout.flush() nruns_necessary= _cds_match_batched(resultfilename1,posfilename1, maxdist,selection,xcat, nruns_necessary=nruns_necessary) sys.stdout.write('\r'+"Working on CDS xMatch batch {} / {} ...\r"\ .format(thisrun2,nruns)) sys.stdout.flush() nruns_necessary= _cds_match_batched(resultfilename2,posfilename2, maxdist,selection,xcat, nruns_necessary=nruns_necessary) sys.stdout.write('\r'+_ERASESTR+'\r') sys.stdout.flush() # Combine results with open(resultfilename,'w') as resultfile: with open(resultfilename1,'r') as resultfile1: for line in resultfile1: resultfile.write(line) with open(resultfilename2,'r') as resultfile2: for line in resultfile2: if line[0] == 'a': continue resultfile.write(line) # Remove intermediate files os.remove(posfilename1) os.remove(posfilename2) os.remove(resultfilename1) os.remove(resultfilename2) return nruns_necessary def _cds_basic_match(resultfilename,posfilename,maxdist,selection,xcat): # Send to CDS for matching result= open(resultfilename,'w') try: subprocess.check_call(['curl', '-X','POST', '-F','request=xmatch', '-F','distMaxArcsec=%i' % maxdist, '-F','selection=%s' % selection, '-F','RESPONSEFORMAT=csv', '-F','cat1=@%s' % os.path.basename(posfilename), '-F','colRA1=RA', '-F','colDec1=DEC', '-F','cat2=%s' % xcat, 'http://cdsxmatch.u-strasbg.fr/xmatch/api/v1/sync'], stdout=result) except subprocess.CalledProcessError: os.remove(posfilename) if os.path.exists(resultfilename): result.close() os.remove(resultfilename) result.close() return None def cds_load(filename): if WIN32: # windows do not have float128, but source_id is double # get around this by squeezing precision from int64 on source_id as source_id is always integer anyway # first read everything as fp64 and then convert source_id to int64 will keep its precision data = numpy.genfromtxt(filename, delimiter=',', skip_header=0, filling_values=-9999.99, names=True, max_rows=1, dtype='float64') # only read the first row max to reduce workload to just get the column name to_list = list(data.dtype.names) # construct a list where everything is fp64 except 'source_id' being int64 dtype_list = [('{}'.format(i), numpy.float64) for i in to_list] dtype_list[dtype_list.index(('source_id', numpy.float64))] = ('source_id', numpy.uint64) return numpy.genfromtxt(filename, delimiter=',', skip_header=0, filling_values=-9999.99, names=True, dtype=dtype_list) else: return numpy.genfromtxt(filename, delimiter=',', skip_header=0, filling_values=-9999.99, names=True, dtype='float128') def cds_matchback(cat,xcat,colRA='RA',colDec='DEC',selection='best', epoch=None,colpmRA='pmra',colpmDec='pmdec',): """ NAME: cds_matchback PURPOSE: Match a matched catalog from xmatch.cds back to the original catalog INPUT cat - original catalog xcat - matched catalog returned by xmatch.cds colRA= ('RA') name of the tag in cat with the right ascension colDec= ('DEC') name of the tag in cat with the declination selection= ('best') select either all matches or the best match according to CDS (see 'selection' at http://cdsxmatch.u-strasbg.fr/xmatch/doc/API-calls.html) epoch= (2000.) epoch of the coordinates in cat colpmRA= ('pmra') name of the tag in cat with the proper motion in right ascension in degree in cat (assumed to be ICRS; includes cos(Dec)) [only used when epoch != 2000.] colpmDec= ('pmdec') name of the tag in cat with the proper motion in declination in degree in cat (assumed to be ICRS) [only used when epoch != 2000.] OUTPUT: Array indices into cat of xcat entries: index[0] is cat index of xcat[0] HISTORY: 2016-09-12 - Written - Bovy (UofT) 2018-05-04 - Account for non-zero epoch difference - Bovy (UofT) """ if selection != 'all': selection= 'best' if selection == 'all': raise NotImplementedError("selection='all' CDS cross-match not currently implemented") if epoch is None: if 'ref_epoch' in cat.dtype.fields: epoch= cat['ref_epoch'] else: epoch= 2000. _check_epoch(cat,epoch) depoch= epoch-2000. if numpy.any(depoch != 0.): # Use proper motion to get both catalogs at the same time dra=cat[colpmRA]/numpy.cos(cat[colDec]/180.*numpy.pi)\ /3600000.*depoch ddec= cat[colpmDec]/3600000.*depoch # Don't shift objects with non-existing proper motion dra[numpy.isnan(cat[colpmRA])]= 0. ddec[numpy.isnan(cat[colpmDec])]= 0. else: dra= numpy.zeros(len(cat)) ddec= numpy.zeros(len(cat)) # xmatch to v. small diff., because match is against *original* coords, # not matched coords in CDS mc1= acoords.SkyCoord(cat[colRA]-dra,cat[colDec]-ddec, unit=(u.degree, u.degree),frame='icrs') mc2= acoords.SkyCoord(xcat['RA'],xcat['DEC'], unit=(u.degree, u.degree),frame='icrs') idx,d2d,d3d = mc2.match_to_catalog_sky(mc1) mindx= d2d < 1e-5*u.arcsec return idx[mindx] def _check_epoch(cat,epoch): warn_about_epoch= False if 'ref_epoch' in cat.dtype.fields: if 'designation' not in cat.dtype.fields: # Assume this is DR1 if numpy.any(numpy.fabs(epoch-2015.) > 0.01): warn_about_epoch= True elif 'Gaia DR2' in cat['designation'][0].decode('utf-8'): if numpy.any(numpy.fabs(epoch-2015.5) > 0.01): warn_about_epoch= True if warn_about_epoch: warnings.warn("You appear to be using a Gaia catalog, but are not setting the epoch to 2015. (DR1) or 2015.5 (DR2), which may lead to incorrect matches") return None

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e400f6b243c2f7da007de4b3632bc30927997f62

14,873

py

Python

rllib/agents/dqn/dqn_torch_policy.py

ThomasLecat/ray

eb025ea8cb27583e8ef6287f5654f23d1ab270ef

[ "Apache-2.0" ]

null

rllib/agents/dqn/dqn_torch_policy.py

ThomasLecat/ray

eb025ea8cb27583e8ef6287f5654f23d1ab270ef

[ "Apache-2.0" ]

null

rllib/agents/dqn/dqn_torch_policy.py

ThomasLecat/ray

eb025ea8cb27583e8ef6287f5654f23d1ab270ef

[ "Apache-2.0" ]

null

from typing import Dict, List, Tuple import gym import ray from ray.rllib.agents.a3c.a3c_torch_policy import apply_grad_clipping from ray.rllib.agents.dqn.dqn_tf_policy import ( PRIO_WEIGHTS, Q_SCOPE, Q_TARGET_SCOPE, postprocess_nstep_and_prio) from ray.rllib.agents.dqn.dqn_torch_model import DQNTorchModel from ray.rllib.agents.dqn.simple_q_torch_policy import TargetNetworkMixin from ray.rllib.models.catalog import ModelCatalog from ray.rllib.models.modelv2 import ModelV2 from ray.rllib.models.torch.torch_action_dist import (TorchCategorical, TorchDistributionWrapper) from ray.rllib.policy.policy import Policy from ray.rllib.policy.sample_batch import SampleBatch from ray.rllib.policy.torch_policy import LearningRateSchedule from ray.rllib.policy.torch_policy_template import build_torch_policy from ray.rllib.utils.error import UnsupportedSpaceException from ray.rllib.utils.exploration.parameter_noise import ParameterNoise from ray.rllib.utils.framework import try_import_torch from ray.rllib.utils.torch_ops import (FLOAT_MIN, huber_loss, reduce_mean_ignore_inf, softmax_cross_entropy_with_logits) from ray.rllib.utils.typing import TensorType, TrainerConfigDict torch, nn = try_import_torch() F = None if nn: F = nn.functional class QLoss: def __init__(self, q_t_selected, q_logits_t_selected, q_tp1_best, q_probs_tp1_best, importance_weights, rewards, done_mask, gamma=0.99, n_step=1, num_atoms=1, v_min=-10.0, v_max=10.0): if num_atoms > 1: # Distributional Q-learning which corresponds to an entropy loss z = torch.range(0.0, num_atoms - 1, dtype=torch.float32) z = v_min + z * (v_max - v_min) / float(num_atoms - 1) # (batch_size, 1) * (1, num_atoms) = (batch_size, num_atoms) r_tau = torch.unsqueeze( rewards, -1) + gamma**n_step * torch.unsqueeze( 1.0 - done_mask, -1) * torch.unsqueeze(z, 0) r_tau = torch.clamp(r_tau, v_min, v_max) b = (r_tau - v_min) / ((v_max - v_min) / float(num_atoms - 1)) lb = torch.floor(b) ub = torch.ceil(b) # Indispensable judgement which is missed in most implementations # when b happens to be an integer, lb == ub, so pr_j(s', a*) will # be discarded because (ub-b) == (b-lb) == 0. floor_equal_ceil = (ub - lb < 0.5).float() # (batch_size, num_atoms, num_atoms) l_project = F.one_hot(lb.long(), num_atoms) # (batch_size, num_atoms, num_atoms) u_project = F.one_hot(ub.long(), num_atoms) ml_delta = q_probs_tp1_best * (ub - b + floor_equal_ceil) mu_delta = q_probs_tp1_best * (b - lb) ml_delta = torch.sum( l_project * torch.unsqueeze(ml_delta, -1), dim=1) mu_delta = torch.sum( u_project * torch.unsqueeze(mu_delta, -1), dim=1) m = ml_delta + mu_delta # Rainbow paper claims that using this cross entropy loss for # priority is robust and insensitive to `prioritized_replay_alpha` self.td_error = softmax_cross_entropy_with_logits( logits=q_logits_t_selected, labels=m) self.loss = torch.mean(self.td_error * importance_weights) self.stats = { # TODO: better Q stats for dist dqn "mean_td_error": torch.mean(self.td_error), } else: q_tp1_best_masked = (1.0 - done_mask) * q_tp1_best # compute RHS of bellman equation q_t_selected_target = rewards + gamma**n_step * q_tp1_best_masked # compute the error (potentially clipped) self.td_error = q_t_selected - q_t_selected_target.detach() self.loss = torch.mean( importance_weights.float() * huber_loss(self.td_error)) self.stats = { "mean_q": torch.mean(q_t_selected), "min_q": torch.min(q_t_selected), "max_q": torch.max(q_t_selected), "mean_td_error": torch.mean(self.td_error), } class ComputeTDErrorMixin: def __init__(self): def compute_td_error(obs_t, act_t, rew_t, obs_tp1, done_mask, importance_weights): input_dict = self._lazy_tensor_dict({SampleBatch.CUR_OBS: obs_t}) input_dict[SampleBatch.ACTIONS] = act_t input_dict[SampleBatch.REWARDS] = rew_t input_dict[SampleBatch.NEXT_OBS] = obs_tp1 input_dict[SampleBatch.DONES] = done_mask input_dict[PRIO_WEIGHTS] = importance_weights # Do forward pass on loss to update td error attribute build_q_losses(self, self.model, None, input_dict) return self.q_loss.td_error self.compute_td_error = compute_td_error def build_q_model_and_distribution( policy: Policy, obs_space: gym.Space, action_space: gym.Space, config: TrainerConfigDict) -> Tuple[ModelV2, TorchDistributionWrapper]: if not isinstance(action_space, gym.spaces.Discrete): raise UnsupportedSpaceException( "Action space {} is not supported for DQN.".format(action_space)) if config["hiddens"]: # try to infer the last layer size, otherwise fall back to 256 num_outputs = ([256] + config["model"]["fcnet_hiddens"])[-1] config["model"]["no_final_linear"] = True else: num_outputs = action_space.n # TODO(sven): Move option to add LayerNorm after each Dense # generically into ModelCatalog. add_layer_norm = ( isinstance(getattr(policy, "exploration", None), ParameterNoise) or config["exploration_config"]["type"] == "ParameterNoise") policy.q_model = ModelCatalog.get_model_v2( obs_space=obs_space, action_space=action_space, num_outputs=num_outputs, model_config=config["model"], framework="torch", model_interface=DQNTorchModel, name=Q_SCOPE, q_hiddens=config["hiddens"], dueling=config["dueling"], num_atoms=config["num_atoms"], use_noisy=config["noisy"], v_min=config["v_min"], v_max=config["v_max"], sigma0=config["sigma0"], # TODO(sven): Move option to add LayerNorm after each Dense # generically into ModelCatalog. add_layer_norm=add_layer_norm) policy.q_func_vars = policy.q_model.variables() policy.target_q_model = ModelCatalog.get_model_v2( obs_space=obs_space, action_space=action_space, num_outputs=num_outputs, model_config=config["model"], framework="torch", model_interface=DQNTorchModel, name=Q_TARGET_SCOPE, q_hiddens=config["hiddens"], dueling=config["dueling"], num_atoms=config["num_atoms"], use_noisy=config["noisy"], v_min=config["v_min"], v_max=config["v_max"], sigma0=config["sigma0"], # TODO(sven): Move option to add LayerNorm after each Dense # generically into ModelCatalog. add_layer_norm=add_layer_norm) policy.target_q_func_vars = policy.target_q_model.variables() return policy.q_model, TorchCategorical def get_distribution_inputs_and_class( policy: Policy, model: ModelV2, obs_batch: TensorType, *, explore: bool = True, is_training: bool = False, **kwargs) -> Tuple[TensorType, type, List[TensorType]]: q_vals = compute_q_values(policy, model, obs_batch, explore, is_training) q_vals = q_vals[0] if isinstance(q_vals, tuple) else q_vals policy.q_values = q_vals return policy.q_values, TorchCategorical, [] # state-out def build_q_losses(policy: Policy, model, _, train_batch: SampleBatch) -> TensorType: config = policy.config # Q-network evaluation. q_t, q_logits_t, q_probs_t = compute_q_values( policy, policy.q_model, train_batch[SampleBatch.CUR_OBS], explore=False, is_training=True) # Target Q-network evaluation. q_tp1, q_logits_tp1, q_probs_tp1 = compute_q_values( policy, policy.target_q_model, train_batch[SampleBatch.NEXT_OBS], explore=False, is_training=True) # Q scores for actions which we know were selected in the given state. one_hot_selection = F.one_hot(train_batch[SampleBatch.ACTIONS], policy.action_space.n) q_t_selected = torch.sum( torch.where(q_t > FLOAT_MIN, q_t, torch.tensor(0.0, device=policy.device)) * one_hot_selection, 1) q_logits_t_selected = torch.sum( q_logits_t * torch.unsqueeze(one_hot_selection, -1), 1) # compute estimate of best possible value starting from state at t + 1 if config["double_q"]: q_tp1_using_online_net, q_logits_tp1_using_online_net, \ q_dist_tp1_using_online_net = compute_q_values( policy, policy.q_model, train_batch[SampleBatch.NEXT_OBS], explore=False, is_training=True) q_tp1_best_using_online_net = torch.argmax(q_tp1_using_online_net, 1) q_tp1_best_one_hot_selection = F.one_hot(q_tp1_best_using_online_net, policy.action_space.n) q_tp1_best = torch.sum( torch.where(q_tp1 > FLOAT_MIN, q_tp1, torch.tensor(0.0, device=policy.device)) * q_tp1_best_one_hot_selection, 1) q_probs_tp1_best = torch.sum( q_probs_tp1 * torch.unsqueeze(q_tp1_best_one_hot_selection, -1), 1) else: q_tp1_best_one_hot_selection = F.one_hot( torch.argmax(q_tp1, 1), policy.action_space.n) q_tp1_best = torch.sum( torch.where(q_tp1 > FLOAT_MIN, q_tp1, torch.tensor(0.0, device=policy.device)) * q_tp1_best_one_hot_selection, 1) q_probs_tp1_best = torch.sum( q_probs_tp1 * torch.unsqueeze(q_tp1_best_one_hot_selection, -1), 1) policy.q_loss = QLoss( q_t_selected, q_logits_t_selected, q_tp1_best, q_probs_tp1_best, train_batch[PRIO_WEIGHTS], train_batch[SampleBatch.REWARDS], train_batch[SampleBatch.DONES].float(), config["gamma"], config["n_step"], config["num_atoms"], config["v_min"], config["v_max"]) return policy.q_loss.loss def adam_optimizer(policy: Policy, config: TrainerConfigDict) -> "torch.optim.Optimizer": return torch.optim.Adam( policy.q_func_vars, lr=policy.cur_lr, eps=config["adam_epsilon"]) def build_q_stats(policy: Policy, batch) -> Dict[str, TensorType]: return dict({ "cur_lr": policy.cur_lr, }, **policy.q_loss.stats) def setup_early_mixins(policy: Policy, obs_space, action_space, config: TrainerConfigDict) -> None: LearningRateSchedule.__init__(policy, config["lr"], config["lr_schedule"]) def after_init(policy: Policy, obs_space: gym.Space, action_space: gym.Space, config: TrainerConfigDict) -> None: ComputeTDErrorMixin.__init__(policy) TargetNetworkMixin.__init__(policy, obs_space, action_space, config) # Move target net to device (this is done autoatically for the # policy.model, but not for any other models the policy has). policy.target_q_model = policy.target_q_model.to(policy.device) def compute_q_values(policy: Policy, model: ModelV2, obs: TensorType, explore, is_training: bool = False): config = policy.config model_out, state = model({ SampleBatch.CUR_OBS: obs, "is_training": is_training, }, [], None) if config["num_atoms"] > 1: (action_scores, z, support_logits_per_action, logits, probs_or_logits) = model.get_q_value_distributions(model_out) else: (action_scores, logits, probs_or_logits) = model.get_q_value_distributions(model_out) if config["dueling"]: state_score = model.get_state_value(model_out) if policy.config["num_atoms"] > 1: support_logits_per_action_mean = torch.mean( support_logits_per_action, dim=1) support_logits_per_action_centered = ( support_logits_per_action - torch.unsqueeze( support_logits_per_action_mean, dim=1)) support_logits_per_action = torch.unsqueeze( state_score, dim=1) + support_logits_per_action_centered support_prob_per_action = nn.functional.softmax( support_logits_per_action) value = torch.sum(z * support_prob_per_action, dim=-1) logits = support_logits_per_action probs_or_logits = support_prob_per_action else: advantages_mean = reduce_mean_ignore_inf(action_scores, 1) advantages_centered = action_scores - torch.unsqueeze( advantages_mean, 1) value = state_score + advantages_centered else: value = action_scores return value, logits, probs_or_logits def grad_process_and_td_error_fn(policy: Policy, optimizer: "torch.optim.Optimizer", loss: TensorType) -> Dict[str, TensorType]: # Clip grads if configured. return apply_grad_clipping(policy, optimizer, loss) def extra_action_out_fn(policy: Policy, input_dict, state_batches, model, action_dist) -> Dict[str, TensorType]: return {"q_values": policy.q_values} DQNTorchPolicy = build_torch_policy( name="DQNTorchPolicy", loss_fn=build_q_losses, get_default_config=lambda: ray.rllib.agents.dqn.dqn.DEFAULT_CONFIG, make_model_and_action_dist=build_q_model_and_distribution, action_distribution_fn=get_distribution_inputs_and_class, stats_fn=build_q_stats, postprocess_fn=postprocess_nstep_and_prio, optimizer_fn=adam_optimizer, extra_grad_process_fn=grad_process_and_td_error_fn, extra_learn_fetches_fn=lambda policy: {"td_error": policy.q_loss.td_error}, extra_action_out_fn=extra_action_out_fn, before_init=setup_early_mixins, after_init=after_init, mixins=[ TargetNetworkMixin, ComputeTDErrorMixin, LearningRateSchedule, ])

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1,897

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0.02485

0.347227

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0.207387

0.20061

0

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0.272709

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39.873995

0.807433

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1

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false

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0.168966

0

null

0

null

0

1

0

e401cec76e2495c504bab2f84a98dc13530872c1

6,865

py

Python

tests/integration/states/test_cmd.py

l2ol33rt/salt

ff68bbd9f4bda992a3e039822fb32f141e94347c

[ "Apache-2.0" ]

null

tests/integration/states/test_cmd.py

l2ol33rt/salt

ff68bbd9f4bda992a3e039822fb32f141e94347c

[ "Apache-2.0" ]

null

tests/integration/states/test_cmd.py

l2ol33rt/salt

ff68bbd9f4bda992a3e039822fb32f141e94347c

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- ''' Tests for the file state ''' # Import python libs from __future__ import absolute_import import errno import os import textwrap import tempfile # Import Salt Testing libs from tests.support.case import ModuleCase from tests.support.paths import TMP_STATE_TREE from tests.support.mixins import SaltReturnAssertsMixin # Import salt libs import salt.utils IS_WINDOWS = salt.utils.is_windows() class CMDTest(ModuleCase, SaltReturnAssertsMixin): ''' Validate the cmd state ''' def test_run_simple(self): ''' cmd.run ''' cmd = 'dir' if IS_WINDOWS else 'ls' ret = self.run_state('cmd.run', name=cmd, cwd=tempfile.gettempdir()) self.assertSaltTrueReturn(ret) def test_test_run_simple(self): ''' cmd.run test interface ''' ret = self.run_state('cmd.run', name='ls', cwd=tempfile.gettempdir(), test=True) self.assertSaltNoneReturn(ret) class CMDRunRedirectTest(ModuleCase, SaltReturnAssertsMixin): ''' Validate the cmd state of run_redirect ''' def setUp(self): self.state_name = 'run_redirect' state_filename = self.state_name + '.sls' self.state_file = os.path.join(TMP_STATE_TREE, state_filename) # Create the testfile and release the handle fd, self.test_file = tempfile.mkstemp() try: os.close(fd) except OSError as exc: if exc.errno != errno.EBADF: raise exc # Create the testfile and release the handle fd, self.test_tmp_path = tempfile.mkstemp() try: os.close(fd) except OSError as exc: if exc.errno != errno.EBADF: raise exc super(CMDRunRedirectTest, self).setUp() def tearDown(self): for path in (self.state_file, self.test_tmp_path, self.test_file): try: os.remove(path) except OSError: # Not all of the tests leave files around that we want to remove # As some of the tests create the sls files in the test itself, # And some are using files in the integration test file state tree. pass super(CMDRunRedirectTest, self).tearDown() def test_run_unless(self): ''' test cmd.run unless ''' state_key = 'cmd_|-{0}_|-{0}_|-run'.format(self.test_tmp_path) with salt.utils.fopen(self.state_file, 'w') as fb_: fb_.write(textwrap.dedent(''' {0}: cmd.run: - unless: echo cheese > {1} '''.format(self.test_tmp_path, self.test_file))) ret = self.run_function('state.sls', [self.state_name]) self.assertTrue(ret[state_key]['result']) def test_run_unless_multiple_cmds(self): ''' test cmd.run using multiple unless options where the first cmd in the list will pass, but the second will fail. This tests the fix for issue #35384. (The fix is in PR #35545.) ''' sls = self.run_function('state.sls', mods='issue-35384') self.assertSaltTrueReturn(sls) # We must assert against the comment here to make sure the comment reads that the # command "echo "hello"" was run. This ensures that we made it to the last unless # command in the state. If the comment reads "unless execution succeeded", or similar, # then the unless state run bailed out after the first unless command succeeded, # which is the bug we're regression testing for. self.assertEqual(sls['cmd_|-cmd_run_unless_multiple_|-echo "hello"_|-run']['comment'], 'Command "echo "hello"" run') def test_run_creates_exists(self): ''' test cmd.run creates already there ''' state_key = 'cmd_|-echo >> {0}_|-echo >> {0}_|-run'.format(self.test_file) with salt.utils.fopen(self.state_file, 'w') as fb_: fb_.write(textwrap.dedent(''' echo >> {0}: cmd.run: - creates: {0} '''.format(self.test_file))) ret = self.run_function('state.sls', [self.state_name]) self.assertTrue(ret[state_key]['result']) self.assertEqual(len(ret[state_key]['changes']), 0) def test_run_creates_new(self): ''' test cmd.run creates not there ''' os.remove(self.test_file) state_key = 'cmd_|-echo >> {0}_|-echo >> {0}_|-run'.format(self.test_file) with salt.utils.fopen(self.state_file, 'w') as fb_: fb_.write(textwrap.dedent(''' echo >> {0}: cmd.run: - creates: {0} '''.format(self.test_file))) ret = self.run_function('state.sls', [self.state_name]) self.assertTrue(ret[state_key]['result']) self.assertEqual(len(ret[state_key]['changes']), 4) def test_run_redirect(self): ''' test cmd.run with shell redirect ''' state_key = 'cmd_|-echo test > {0}_|-echo test > {0}_|-run'.format(self.test_file) with salt.utils.fopen(self.state_file, 'w') as fb_: fb_.write(textwrap.dedent(''' echo test > {0}: cmd.run '''.format(self.test_file))) ret = self.run_function('state.sls', [self.state_name]) self.assertTrue(ret[state_key]['result']) class CMDRunWatchTest(ModuleCase, SaltReturnAssertsMixin): ''' Validate the cmd state of run_watch ''' def setUp(self): self.state_name = 'run_watch' state_filename = self.state_name + '.sls' self.state_file = os.path.join(TMP_STATE_TREE, state_filename) super(CMDRunWatchTest, self).setUp() def tearDown(self): os.remove(self.state_file) super(CMDRunWatchTest, self).tearDown() def test_run_watch(self): ''' test cmd.run watch ''' saltines_key = 'cmd_|-saltines_|-echo changed=true_|-run' biscuits_key = 'cmd_|-biscuits_|-echo biscuits_|-wait' with salt.utils.fopen(self.state_file, 'w') as fb_: fb_.write(textwrap.dedent(''' saltines: cmd.run: - name: echo changed=true - cwd: / - stateful: True biscuits: cmd.wait: - name: echo biscuits - cwd: / - watch: - cmd: saltines ''')) ret = self.run_function('state.sls', [self.state_name]) self.assertTrue(ret[saltines_key]['result']) self.assertTrue(ret[biscuits_key]['result'])

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0.57276

823

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0.207776

0.042139

0.031604

0.022123

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34.325

0.796614

0.174654

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0.133333

1

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false

0.008333

0.075

0

0.2

0

null

0

null

0

1

0

e402affb74681aeffbd7073f07e5537c7f847fc0

2,591

py

Python

mars/tensor/execution/datastore.py

ChenQuan/mars

46fc9747e99210cebfabfc2d85bcc8272440d1a3

[ "Apache-2.0" ]

null

mars/tensor/execution/datastore.py

ChenQuan/mars

46fc9747e99210cebfabfc2d85bcc8272440d1a3

[ "Apache-2.0" ]

null

mars/tensor/execution/datastore.py

ChenQuan/mars

46fc9747e99210cebfabfc2d85bcc8272440d1a3

[ "Apache-2.0" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- # Copyright 1999-2018 Alibaba Group Holding Ltd. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import numpy as np try: import tiledb except ImportError: # pragma: no cover tiledb = None from ...lib.sparse import SparseNDArray from ...lib.sparse.core import sps from ..expressions import datastore from .utils import get_tiledb_ctx def _store_tiledb(ctx, chunk): tiledb_ctx = get_tiledb_ctx(chunk.op.tiledb_config) uri = chunk.op.tiledb_uri key = chunk.op.tiledb_key timestamp = chunk.op.tiledb_timestamp axis_offsets = chunk.op.axis_offsets if not chunk.issparse(): # dense to_store = np.ascontiguousarray(ctx[chunk.op.input.key]) slcs = [] for axis in range(chunk.ndim): axis_offset = axis_offsets[axis] axis_length = chunk.op.input.shape[axis] slcs.append(slice(axis_offset, axis_offset + axis_length)) with tiledb.DenseArray(tiledb_ctx, uri, mode='w', key=key, timestamp=timestamp) as arr: arr[tuple(slcs)] = to_store ctx[chunk.key] = np.empty((0,) * chunk.ndim, dtype=chunk.dtype) else: # sparse to_store = ctx[chunk.op.input.key].spmatrix.tocoo() if to_store.nnz > 0: with tiledb.SparseArray(tiledb_ctx, uri, mode='w', key=key, timestamp=timestamp) as arr: if chunk.ndim == 1: vec = to_store.col if to_store.shape[0] == 1 else to_store.row vec += axis_offsets[0] arr[vec] = to_store.data else: i, j = to_store.row + axis_offsets[0], to_store.col + axis_offsets[1] arr[i, j] = to_store.data ctx[chunk.key] = SparseNDArray(sps.csr_matrix((0, 0), dtype=chunk.dtype), shape=chunk.shape) def register_data_store_handler(): from ...executor import register register(datastore.TensorTileDBDataStore, _store_tiledb)

37.550725

89

0.63296

349

2,591

4.581662

0.406877

0.048155

0.03252

0.020013

0.08005

0.057536

0

0.012144

0.269008

2,591

68

90

38.102941

0.832101

0.247781

0

0.093023

0

0.001036

0

1

0.046512

false

0

0.186047

0

0.232558

0

null

0

null

0

1

0

e4041f8f3f0e170375ff7b152259c16fb293ef71

1,689

py

Python

fastgc/model/mlp.py

ppmlguy/fastgradclip

0d8bff42ab13fa3471c520a2823050ccf0ff4a21

[ "MIT" ]

2

2020-10-16T10:14:25.000Z

2021-03-25T17:19:34.000Z

fastgc/model/mlp.py

ppmlguy/fastgradclip

0d8bff42ab13fa3471c520a2823050ccf0ff4a21

[ "MIT" ]

null

fastgc/model/mlp.py

ppmlguy/fastgradclip

0d8bff42ab13fa3471c520a2823050ccf0ff4a21

[ "MIT" ]

null

import torch import torch.nn as nn import torch.nn.functional as F from fastgc.model.penet import PeGradNet from fastgc.layers.linear import Linear from fastgc.activation import activation class MLP(PeGradNet): def __init__(self, input_size, hidden_sizes, output_size, act_func='sigmoid', train_alg='batch'): """ Parameters: ------------------ - input_size: integer, the number of features in the input - hidden_sizes: a list of integers, a list object containing number of units for hidden layers - output_size: an integer, the length of output vector - act_func: string, name of activation function to use for each hidden layer - train_alg: string, allowed values are {'batch', 'reweight', 'naive'} """ super(MLP, self).__init__() self.input_size = input_size layer_sizes = [input_size] + hidden_sizes self.linears = nn.ModuleList([Linear(in_size, out_size, bias=True) for in_size, out_size in zip(layer_sizes[:-1], layer_sizes[1:])]) self.output_layer = Linear(hidden_sizes[-1], output_size, bias=True) self.act = activation[act_func] self.train_alg=train_alg # list of layers in the network self.layers = [layer for layer in self.linears] self.layers.append(self.output_layer) def forward(self, x): x = x.view(-1, self.input_size) out = x for layer in self.linears: out = self.act(layer(out)) logits = self.output_layer(out) return logits

35.1875

102

0.605684

216

1,689

4.560185

0.342593

0.054822

0.039594

0.034518

0.04264

0

0.003378

0.298993

1,689

47

103

35.93617

0.828547

0.248668

0

0.010033

0

1

0.074074

false

0

0.222222

0

0.37037

0

null

0

null

0

1

0

e407a1b65cd96d68a622c0a025047b036e6148f4

21,659

py

Python

test_vector_handlers/src/awses_test_vectors/manifests/full_message/decrypt_generation.py

farleyb-amazon/aws-encryption-sdk-python

7950abd73ee333407d2dadd02ef2d57c3df464cf

[ "Apache-2.0" ]

95

2018-08-20T23:10:00.000Z

2022-02-17T02:54:32.000Z

test_vector_handlers/src/awses_test_vectors/manifests/full_message/decrypt_generation.py

farleyb-amazon/aws-encryption-sdk-python

7950abd73ee333407d2dadd02ef2d57c3df464cf

[ "Apache-2.0" ]

220

2018-08-01T20:56:29.000Z

2022-03-28T18:12:35.000Z

test_vector_handlers/src/awses_test_vectors/manifests/full_message/decrypt_generation.py

farleyb-amazon/aws-encryption-sdk-python

7950abd73ee333407d2dadd02ef2d57c3df464cf

[ "Apache-2.0" ]

63

2018-08-01T19:37:33.000Z

2022-03-20T17:14:15.000Z

# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"). You # may not use this file except in compliance with the License. A copy of # the License is located at # # http://aws.amazon.com/apache2.0/ # # or in the "license" file accompanying this file. This file is # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF # ANY KIND, either express or implied. See the License for the specific # language governing permissions and limitations under the License. """ AWS Encryption SDK Decrypt Message Generation manifest handler. Described in AWS Crypto Tools Test Vector Framework feature #0006 AWS Encryption SDK Decrypt Message Generation. """ import json import os import uuid from copy import copy import attr import six from aws_encryption_sdk.caches.local import LocalCryptoMaterialsCache from aws_encryption_sdk.materials_managers.base import CryptoMaterialsManager from aws_encryption_sdk.materials_managers.caching import CachingCryptoMaterialsManager from aws_encryption_sdk.materials_managers.default import DefaultCryptoMaterialsManager from awses_test_vectors.internal.defaults import ENCODING from awses_test_vectors.internal.util import ( dictionary_validator, file_reader, file_writer, iterable_validator, membership_validator, validate_manifest_type, ) from awses_test_vectors.manifests.full_message.decrypt import ( DecryptionMethod, MessageDecryptionManifest, MessageDecryptionTestResult, MessageDecryptionTestScenario, ) from awses_test_vectors.manifests.full_message.encrypt import MessageEncryptionTestScenario from awses_test_vectors.manifests.keys import KeysManifest try: from aws_encryption_sdk.identifiers import AlgorithmSuite except ImportError: from aws_encryption_sdk.identifiers import Algorithm as AlgorithmSuite from awses_test_vectors.manifests.master_key import MasterKeySpec, master_key_provider_from_master_key_specs try: # Python 3.5.0 and 3.5.1 have incompatible typing modules from typing import IO, Callable, Dict, Iterable, Optional # noqa pylint: disable=unused-import from awses_test_vectors.internal.mypy_types import ( # noqa pylint: disable=unused-import ENCRYPT_SCENARIO_SPEC, PLAINTEXTS_SPEC, ) except ImportError: # pragma: no cover # We only actually need these imports when running the mypy checks pass SUPPORTED_VERSIONS = (2,) class TamperingMethod: """Base class for all tampering methods.""" @classmethod def from_tampering_spec(cls, spec): """Load from a tampering specification""" if spec is None: return TamperingMethod() if spec == "truncate": return TruncateTamperingMethod() if spec == "mutate": return MutateTamperingMethod() if spec == "half-sign": return HalfSigningTamperingMethod() ((tampering_tag, tampering_values_spec),) = spec.items() if tampering_tag == "change-edk-provider-info": return ChangeEDKProviderInfoTamperingMethod.from_values_spec(tampering_values_spec) raise ValueError("Unrecognized tampering method tag: " + tampering_tag) # pylint: disable=R0201 def run_scenario_with_tampering(self, ciphertext_writer, generation_scenario, plaintext_uri): """ Run a given scenario, tampering with the input or the result. return: a list of (ciphertext, result) pairs """ materials_manager = DefaultCryptoMaterialsManager( generation_scenario.encryption_scenario.master_key_provider_fn() ) ciphertext_to_decrypt = generation_scenario.encryption_scenario.run(materials_manager) if generation_scenario.result: expected_result = generation_scenario.result else: expected_result = MessageDecryptionTestResult.expect_output( plaintext_uri=plaintext_uri, plaintext=generation_scenario.encryption_scenario.plaintext ) return [ generation_scenario.decryption_test_scenario_pair(ciphertext_writer, ciphertext_to_decrypt, expected_result) ] class ChangeEDKProviderInfoTamperingMethod(TamperingMethod): """Tampering method that changes the provider info on all EDKs.""" new_provider_infos = attr.ib(validator=iterable_validator(list, six.string_types)) def __init__(self, new_provider_infos): """Create a new instance for a given new provider info value.""" self.new_provider_infos = new_provider_infos @classmethod def from_values_spec(cls, values_spec): """Load from a tampering parameters specification""" return ChangeEDKProviderInfoTamperingMethod(values_spec) # pylint: disable=R0201 def run_scenario_with_tampering(self, ciphertext_writer, generation_scenario, _plaintext_uri): """ Run a given scenario, tampering with the input or the result. return: a list of (ciphertext, result) pairs. """ master_key_provider = generation_scenario.encryption_scenario.master_key_provider_fn() # Use a caching CMM to avoid generating a new data key every time. cache = LocalCryptoMaterialsCache(10) caching_cmm = CachingCryptoMaterialsManager( master_key_provider=master_key_provider, cache=cache, max_age=60.0, max_messages_encrypted=100, ) return [ self.run_scenario_with_new_provider_info( ciphertext_writer, generation_scenario, caching_cmm, new_provider_info ) for new_provider_info in self.new_provider_infos ] def run_scenario_with_new_provider_info( self, ciphertext_writer, generation_scenario, materials_manager, new_provider_info ): """Run with tampering for a specific new provider info value""" tampering_materials_manager = ProviderInfoChangingCryptoMaterialsManager(materials_manager, new_provider_info) ciphertext_to_decrypt = generation_scenario.encryption_scenario.run(tampering_materials_manager) expected_result = MessageDecryptionTestResult.expect_error( "Incorrect encrypted data key provider info: " + new_provider_info ) return generation_scenario.decryption_test_scenario_pair( ciphertext_writer, ciphertext_to_decrypt, expected_result ) class ProviderInfoChangingCryptoMaterialsManager(CryptoMaterialsManager): """ Custom CMM that modifies the provider info field on EDKS. THIS IS ONLY USED TO CREATE INVALID MESSAGES and should never be used in production! """ wrapped_cmm = attr.ib(validator=attr.validators.instance_of(CryptoMaterialsManager)) new_provider_info = attr.ib(validator=attr.validators.instance_of(six.string_types)) def __init__(self, materials_manager, new_provider_info): """Create a new CMM that wraps a the given CMM.""" self.wrapped_cmm = materials_manager self.new_provider_info = new_provider_info def get_encryption_materials(self, request): """ Request materials from the wrapped CMM, and then change the provider info on each EDK. """ result = self.wrapped_cmm.get_encryption_materials(request) for encrypted_data_key in result.encrypted_data_keys: encrypted_data_key.key_provider.key_info = self.new_provider_info return result def decrypt_materials(self, request): """Thunks to the wrapped CMM""" return self.wrapped_cmm.decrypt_materials(request) BITS_PER_BYTE = 8 class TruncateTamperingMethod(TamperingMethod): """Tampering method that truncates a good message at every byte (except zero).""" # pylint: disable=R0201 def run_scenario_with_tampering(self, ciphertext_writer, generation_scenario, _plaintext_uri): """ Run a given scenario, tampering with the input or the result. return: a list of (ciphertext, result) pairs. """ ciphertext_to_decrypt = generation_scenario.encryption_scenario.run() return [ generation_scenario.decryption_test_scenario_pair( ciphertext_writer, TruncateTamperingMethod.flip_bit(ciphertext_to_decrypt, bit), MessageDecryptionTestResult.expect_error("Bit {} flipped".format(bit)), ) for bit in range(0, len(ciphertext_to_decrypt) * BITS_PER_BYTE) ] @classmethod def flip_bit(cls, ciphertext, bit): """Flip only the given bit in the given ciphertext""" byte_index, bit_index = divmod(bit, BITS_PER_BYTE) result = bytearray(ciphertext) result[byte_index] ^= 1 << (BITS_PER_BYTE - bit_index - 1) return bytes(result) class MutateTamperingMethod(TamperingMethod): """Tampering method that produces a message with a single bit flipped, for every possible bit.""" # pylint: disable=R0201 def run_scenario_with_tampering(self, ciphertext_writer, generation_scenario, _plaintext_uri): """ Run a given scenario, tampering with the input or the result. return: a list of (ciphertext, result) pairs. """ ciphertext_to_decrypt = generation_scenario.encryption_scenario.run() return [ generation_scenario.decryption_test_scenario_pair( ciphertext_writer, ciphertext_to_decrypt[0:length], MessageDecryptionTestResult.expect_error("Truncated at byte {}".format(length)), ) for length in range(1, len(ciphertext_to_decrypt)) ] class HalfSigningTamperingMethod(TamperingMethod): """Tampering method that changes the provider info on all EDKs.""" # pylint: disable=R0201 def run_scenario_with_tampering(self, ciphertext_writer, generation_scenario, _plaintext_uri): """ Run a given scenario, tampering with the input or the result. return: a list of (ciphertext, result) pairs. """ tampering_materials_manager = HalfSigningCryptoMaterialsManager( generation_scenario.encryption_scenario.master_key_provider_fn() ) ciphertext_to_decrypt = generation_scenario.encryption_scenario.run(tampering_materials_manager) expected_result = MessageDecryptionTestResult.expect_error( "Unsigned message using a data key with a public key" ) return [ generation_scenario.decryption_test_scenario_pair(ciphertext_writer, ciphertext_to_decrypt, expected_result) ] class HalfSigningCryptoMaterialsManager(CryptoMaterialsManager): """ Custom CMM that generates materials for an unsigned algorithm suite that includes the "aws-crypto-public-key" encryption context. THIS IS ONLY USED TO CREATE INVALID MESSAGES and should never be used in production! It is imitating what a malicious decryptor without encryption permissions might do, to attempt to forge an unsigned message from a decrypted signed message, and therefore this is an important case for ESDKs to reject. """ wrapped_default_cmm = attr.ib(validator=attr.validators.instance_of(CryptoMaterialsManager)) def __init__(self, master_key_provider): """ Create a new CMM that wraps a new DefaultCryptoMaterialsManager based on the given master key provider. """ self.wrapped_default_cmm = DefaultCryptoMaterialsManager(master_key_provider) def get_encryption_materials(self, request): """ Generate half-signing materials by requesting signing materials from the wrapped default CMM, and then changing the algorithm suite and removing the signing key from teh result. """ if request.algorithm == AlgorithmSuite.AES_256_GCM_HKDF_SHA512_COMMIT_KEY: signing_request = copy(request) signing_request.algorithm = AlgorithmSuite.AES_256_GCM_HKDF_SHA512_COMMIT_KEY_ECDSA_P384 result = self.wrapped_default_cmm.get_encryption_materials(signing_request) result.algorithm = request.algorithm result.signing_key = None return result raise NotImplementedError( "The half-sign tampering method is only supported on the " "AES_256_GCM_HKDF_SHA512_COMMIT_KEY algorithm suite." ) def decrypt_materials(self, request): """Thunks to the wrapped default CMM""" return self.wrapped_default_cmm.decrypt_materials(request) @attr.s class MessageDecryptionTestScenarioGenerator(object): # pylint: disable=too-many-instance-attributes """Data class for a single full message decrypt test scenario. Handles serialization and deserialization to and from manifest specs. :param MessageEncryptionTestScenario encryption_scenario: Encryption parameters :param tampering_method: Optional method used to tamper with the ciphertext :type tampering_method: :class:`TamperingMethod` :param decryption_method: :param decryption_master_key_specs: Iterable of master key specifications :type decryption_master_key_specs: iterable of :class:`MasterKeySpec` :param Callable decryption_master_key_provider_fn: :param result: """ encryption_scenario = attr.ib(validator=attr.validators.instance_of(MessageEncryptionTestScenario)) tampering_method = attr.ib(validator=attr.validators.optional(attr.validators.instance_of(TamperingMethod))) decryption_method = attr.ib(validator=attr.validators.optional(attr.validators.instance_of(DecryptionMethod))) decryption_master_key_specs = attr.ib(validator=iterable_validator(list, MasterKeySpec)) decryption_master_key_provider_fn = attr.ib(validator=attr.validators.is_callable()) result = attr.ib(validator=attr.validators.optional(attr.validators.instance_of(MessageDecryptionTestResult))) @classmethod def from_scenario(cls, scenario, keys, plaintexts): """Load from a scenario specification. :param dict scenario: Scenario specification JSON :param KeysManifest keys: Loaded keys :param dict plaintexts: Mapping of plaintext names to plaintext values :return: Loaded test scenario :rtype: MessageDecryptionTestScenarioGenerator """ encryption_scenario_spec = scenario["encryption-scenario"] encryption_scenario = MessageEncryptionTestScenario.from_scenario(encryption_scenario_spec, keys, plaintexts) tampering = scenario.get("tampering") tampering_method = TamperingMethod.from_tampering_spec(tampering) decryption_method_spec = scenario.get("decryption-method") decryption_method = DecryptionMethod(decryption_method_spec) if decryption_method_spec else None if "decryption-master-keys" in scenario: decryption_master_key_specs = [ MasterKeySpec.from_scenario(spec) for spec in scenario["decryption-master-keys"] ] def decryption_master_key_provider_fn(): return master_key_provider_from_master_key_specs(keys, decryption_master_key_specs) else: decryption_master_key_specs = encryption_scenario.master_key_specs decryption_master_key_provider_fn = encryption_scenario.master_key_provider_fn result_spec = scenario.get("result") result = MessageDecryptionTestResult.from_result_spec(result_spec, None) if result_spec else None return cls( encryption_scenario=encryption_scenario, tampering_method=tampering_method, decryption_method=decryption_method, decryption_master_key_specs=decryption_master_key_specs, decryption_master_key_provider_fn=decryption_master_key_provider_fn, result=result, ) def run(self, ciphertext_writer, plaintext_uri): """Run this scenario, writing the resulting ciphertext with ``ciphertext_writer`` and returning a :class:`MessageDecryptionTestScenario` that describes the matching decrypt scenario. :param callable ciphertext_writer: Callable that will write the requested named ciphertext and return a URI locating the written data :param str plaintext_uri: URI locating the written plaintext data for this scenario :return: Decrypt test scenario that describes the generated scenario :rtype: MessageDecryptionTestScenario """ return dict(self.tampering_method.run_scenario_with_tampering(ciphertext_writer, self, plaintext_uri)) def decryption_test_scenario_pair(self, ciphertext_writer, ciphertext_to_decrypt, expected_result): """Create a new (name, decryption scenario) pair""" ciphertext_name = str(uuid.uuid4()) ciphertext_uri = ciphertext_writer(ciphertext_name, ciphertext_to_decrypt) return ( ciphertext_name, MessageDecryptionTestScenario( ciphertext_uri=ciphertext_uri, ciphertext=ciphertext_to_decrypt, master_key_specs=self.decryption_master_key_specs, master_key_provider_fn=self.decryption_master_key_provider_fn, decryption_method=self.decryption_method, result=expected_result, ), ) @attr.s class MessageDecryptionGenerationManifest(object): """AWS Encryption SDK Decryption Message Generation manifest handler. Described in AWS Crypto Tools Test Vector Framework feature #0006 AWS Encryption SDK Decrypt Message Generation. :param int version: Version of this manifest :param KeysManifest keys: Loaded keys :param dict plaintexts: Mapping of plaintext names to plaintext values :param dict tests: Mapping of test scenario names to :class:`MessageDecryptionGenerationManifest`s """ version = attr.ib(validator=membership_validator(SUPPORTED_VERSIONS)) keys = attr.ib(validator=attr.validators.instance_of(KeysManifest)) plaintexts = attr.ib(validator=dictionary_validator(six.string_types, six.binary_type)) tests = attr.ib(validator=dictionary_validator(six.string_types, MessageDecryptionTestScenarioGenerator)) type_name = "awses-decrypt-generate" @staticmethod def _generate_plaintexts(plaintexts_specs): # type: (PLAINTEXTS_SPEC) -> Dict[str, bytes] """Generate required plaintext values. :param dict plaintexts_specs: Mapping of plaintext name to size in bytes :return: Mapping of plaintext name to randomly generated bytes :rtype: dict """ return {name: os.urandom(size) for name, size in plaintexts_specs.items()} @classmethod def from_file(cls, input_file): # type: (IO) -> MessageDecryptionGenerationManifest """Load from a file containing a full message encrypt manifest. :param file input_file: File object for file containing JSON manifest :return: Loaded manifest :rtype: MessageEncryptionManifest """ raw_manifest = json.load(input_file) validate_manifest_type( type_name=cls.type_name, manifest_version=raw_manifest["manifest"], supported_versions=SUPPORTED_VERSIONS ) parent_dir = os.path.abspath(os.path.dirname(input_file.name)) reader = file_reader(parent_dir) raw_keys_manifest = json.loads(reader(raw_manifest["keys"]).decode(ENCODING)) keys = KeysManifest.from_manifest_spec(raw_keys_manifest) plaintexts = cls._generate_plaintexts(raw_manifest["plaintexts"]) tests = {} for name, scenario in raw_manifest["tests"].items(): try: tests[name] = MessageDecryptionTestScenarioGenerator.from_scenario( scenario=scenario, keys=keys, plaintexts=plaintexts ) except NotImplementedError: continue return cls(version=raw_manifest["manifest"]["version"], keys=keys, plaintexts=plaintexts, tests=tests) def run_and_write_to_dir(self, target_directory, json_indent=None): # type: (str, Optional[int]) -> None """Process all known encrypt test scenarios and write the resulting data and manifests to disk. :param str target_directory: Directory in which to write all output :param int json_indent: Number of spaces to indent JSON files (optional: default is to write minified) """ root_dir = os.path.abspath(target_directory) root_writer = file_writer(root_dir) root_writer("keys.json", json.dumps(self.keys.manifest_spec, indent=json_indent).encode(ENCODING)) plaintext_writer = file_writer(os.path.join(root_dir, "plaintexts")) plaintext_uris = {name: plaintext_writer(name, plaintext) for name, plaintext in self.plaintexts.items()} ciphertext_writer = file_writer(os.path.join(root_dir, "ciphertexts")) test_scenarios = { decrypt_scenario_name: decrypt_scenario for name, scenario in self.tests.items() for decrypt_scenario_name, decrypt_scenario in scenario.run( ciphertext_writer, plaintext_uris[scenario.encryption_scenario.plaintext_name] ).items() } decrypt_manifest = MessageDecryptionManifest( keys_uri="file://keys.json", keys=self.keys, test_scenarios=test_scenarios ) root_writer("manifest.json", json.dumps(decrypt_manifest.manifest_spec, indent=json_indent).encode(ENCODING))

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null

0

null

0

1

0

e40ca767179088e9b2626907b90dc14b9802c60c

10,237

py

Python

atmpro1_vsm2.py

joselynzhao/One-shot-Person-Re-ID-ATM

d039b1a66410f87cfe931774eba54a5f1a1a0260

[ "MIT" ]

3

2020-07-28T03:16:51.000Z

2020-11-23T05:39:54.000Z

atmpro1_vsm2.py

joselynzhao/One-shot-Person-Re-ID-ATM

d039b1a66410f87cfe931774eba54a5f1a1a0260

[ "MIT" ]

null

atmpro1_vsm2.py

joselynzhao/One-shot-Person-Re-ID-ATM

d039b1a66410f87cfe931774eba54a5f1a1a0260

[ "MIT" ]

null

#!/usr/bin/python3.6 # -*- coding: utf-8 -*- # @Time : 2020/9/3 上午11:03 # @Author : Joselynzhao # @Email : zhaojing17@forxmail.com # @File : atmpro1_vsm2.py # @Software: PyCharm # @Desc : #!/usr/bin/python3.6 # -*- coding: utf-8 -*- # @Time : 2020/9/1 下午7:07 # @Author : Joselynzhao # @Email : zhaojing17@forxmail.com # @File : atmpro1_vsm.py # @Software: PyCharm # @Desc : #!/usr/bin/python3.6 # -*- coding: utf-8 -*- # @Time : 2020/8/26 下午8:26 # @Author : Joselynzhao # @Email : zhaojing17@forxmail.com # @File : atmpro1.py # @Software: PyCharm # @Desc : from my_reid.eug import * from my_reid import datasets from my_reid import models import numpy as np import torch import argparse import os import warnings warnings.filterwarnings("ignore") from my_reid.utils.logging import Logger import os.path as osp import sys from torch.backends import cudnn from my_reid.utils.serialization import load_checkpoint from torch import nn import time import pickle import torch.distributed as dist from torch.nn.parallel import DistributedDataParallel as DDP from torch.utils.data.distributed import DistributedSampler from pathlib import Path def resume(savepath): import re pattern = re.compile(r'step_(\d+)\.ckpt') start_step = -1 ckpt_file = "" # find start step files = os.listdir(savepath) files.sort() for filename in files: try: iter_ = int(pattern.search(filename).groups()[0]) print(iter_) if iter_ > start_step: start_step = iter_ ckpt_file = osp.join(savepath, filename) except: continue # if need resume if start_step >= 0: print("continued from iter step", start_step) else: print("resume failed", start_step, files) return start_step, ckpt_file def main(args): father = Path('/mnt/') if father.exists(): # 是在服务器上 data_dir = Path('/mnt/share/datasets/RE-ID/data') # 服务器 logs_dir = Path('/mnt/home/{}'.format(args.log_name)) # 服务器 else: #本地 data_dir = Path('/home/joselyn/workspace/ATM_SERIES/data') # 本地跑用这个 logs_dir = Path('/home/joselyn/workspace/ATM_SERIES/{}'.format(args.log_name)) # 本地跑用这个 cudnn.benchmark = True cudnn.enabled = True save_path = os.path.join(logs_dir, args.dataset, args.exp_name, args.exp_order) # 到编号位置. total_step = 100 // args.EF + 1 sys.stdout = Logger(osp.join(save_path, 'log' + str(args.EF) + time.strftime(".%m_%d_%H:%M:%S") + '.txt')) dataf_file = open(osp.join(save_path, 'dataf.txt'), 'a') # 保存性能数据. #特征空间中的性能问题. data_file = open(osp.join(save_path, 'data.txt'), 'a') # 保存性能数据. #特征空间中的性能问题. kf_file = open(osp.join(save_path,'kf.txt'),'a') # 数据格式为 label_pre_r, select_pre_r,label_pre_t, select_pre_t ,加上了了tagper的数据. tagper_path = osp.join(save_path,'tagper') #tagper存储路径. if not Path(tagper_path).exists(): os.mkdir(tagper_path) '''# 记录配置信息和路径''' print('-'*20+'config_info'+'-'*20) config_file = open(osp.join(save_path, 'config.txt'), 'w') config_info = str(args).split('(')[1].strip(')').split(',') config_info.sort() for one in config_info: key,value=map(str,one.split('=')) config_file.write(key.strip()+'='+value.strip('\'')+'\n') print(key.strip()+'='+value.strip('\'')) config_file.write('save_path='+save_path) print('save_path='+save_path) print('-' * 20 + 'config_info' + '-' * 20) config_file.close() train_time_file = open(osp.join(save_path, 'time.txt'), 'a') # 只记录训练所需要的时间. # 数据格式为 step_time total_time. total_time = 0 # get all the labeled and unlabeled data for training dataset_all = datasets.create(args.dataset, osp.join(data_dir, args.dataset)) num_all_examples = len(dataset_all.train) l_data, u_data = get_init_shot_in_cam1(dataset_all, load_path="./examples/{}_init_{}.pickle".format(dataset_all.name, args.init), init=args.init) resume_step, ckpt_file = -1, '' if args.resume: resume_step, ckpt_file = resume(save_path) # initial the EUG algorithm eug = EUG(batch_size=args.batch_size, num_classes=dataset_all.num_train_ids, dataset=dataset_all, l_data=l_data, u_data=u_data, save_path=save_path, max_frames=args.max_frames, embeding_fea_size=args.fea, momentum=args.momentum, lamda=args.lamda) tagper = EUG(batch_size=args.batch_size, num_classes=dataset_all.num_train_ids, dataset=dataset_all, l_data=l_data, u_data=u_data, save_path=tagper_path, max_frames=args.max_frames, embeding_fea_size=args.fea, momentum=args.momentum, lamda=args.lamda) new_train_data = l_data unselected_data = u_data iter_mode = 2 #迭代模式,确定是否训练tagper for step in range(total_step): # for resume if step < resume_step: continue ratio = (step + 1) * args.EF / 100 ratio_t = (step+1+args.t) * args.EF /100 nums_to_select = int(len(u_data) * ratio) nums_to_select_tagper = int(len(u_data) * ratio_t) if nums_to_select >= len(u_data): break #args.vsm_lambda的衰减 0.5 - 0 vsm_lambda = args.vsm_lambda*step/(1-(total_step/2)) +args.vsm_lambda vsm_lambda +=1 print("Runing: EF={}%, step {}:\t Nums_to_be_select {} \t Ritio \t Logs-dir {}".format( args.EF, step, nums_to_select, ratio, save_path)) # train the model or load ckpt start_time = time.time() print("training reid model") eug.train(new_train_data, unselected_data, step, loss=args.loss, epochs=args.epochs, step_size=args.step_size, init_lr=0.1) if step != resume_step else eug.resume(ckpt_file, step) # 只对eug进行性能评估 # mAP, rank1, rank5, rank10, rank20 = 0, 0, 0, 0, 0 mAP, rank1, rank5, rank10, rank20 = eug.evaluate(dataset_all.query, dataset_all.gallery) # 把数据写到data文件里. data_file.write('{} {:.2%} {:.2%} {:.2%} {:.2%} {:.2%}\n'.format(step, mAP, rank1, rank5, rank10, rank20)) pred_y, pred_score,label_pre,dists= eug.estimate_label_vsm() selected_idx = eug.select_top_data_vsm2(pred_score, dists,args.topk,vsm_lambda,min(nums_to_select_tagper,len(u_data)-50) if iter_mode==2 else min(nums_to_select,len(u_data))) #直接翻两倍取数据. -50个样本,保证unselected_data数量不为0 new_train_data, unselected_data, select_pre= eug.generate_new_train_data(selected_idx, pred_y) raw_label_pre, raw_select_pre = label_pre,select_pre t_label_pre,t_select_pre = 0,0 raw_select_pre_t = 0 # label_pre_t,select_pre_t=0,0 if iter_mode==2: raw_select_pre_t = raw_select_pre print("training tagper model") selected_idx = eug.select_top_data_vsm2(pred_score,dists,args.topk,vsm_lambda, min(nums_to_select, len(u_data))) _, _, raw_select_pre = eug.generate_new_train_data(selected_idx, pred_y) # kf_file.write('{} {:.2%} {:.2%}'.format(step, label_pre, select_pre)) tagper.resume(osp.join(save_path,'step_{}.ckpt'.format(step)),step) tagper.train(new_train_data, unselected_data, step, loss=args.loss, epochs=args.epochs, step_size=args.step_size, init_lr=0.1) pred_y, pred_score, label_pre,dists= tagper.estimate_label_vsm() selected_idx = tagper.select_top_data_vsm2(pred_score,dists,args.topk,vsm_lambda,min(nums_to_select,len(u_data))) # 采样目标数量 new_train_data, unselected_data, select_pre= tagper.generate_new_train_data(selected_idx, pred_y) t_label_pre,t_select_pre = label_pre,select_pre label_pre,select_pre = t_label_pre,t_select_pre if nums_to_select_tagper >=len(u_data): iter_mode=1 #切换模式 print('tagper is stop') else: #mode = 1 # raw_select_pre = raw_select_pre_t # raw_select_pre_t = 0 label_pre,select_pre = raw_label_pre,raw_select_pre end_time = time.time() step_time = end_time - start_time total_time = step_time + total_time train_time_file.write('{} {:.6} {:.6}\n'.format(step, step_time, total_time)) kf_file.write('{} {} {} {:.2%} {:.2%} {:.2%} {:.2%} {:.2%}\n'.format(step,nums_to_select,nums_to_select_tagper,raw_label_pre,raw_select_pre,raw_select_pre_t,t_label_pre,t_select_pre)) dataf_file.write( '{} {:.2%} {:.2%}\n'.format(step, label_pre, select_pre)) dataf_file.close() train_time_file.close() if __name__ == '__main__': parser = argparse.ArgumentParser(description='Progressive Learning for One-Example re-ID') parser.add_argument('-d', '--dataset', type=str, default='mars', choices=datasets.names()) parser.add_argument('-b', '--batch-size', type=int, default=16) parser.add_argument('-f', '--fea', type=int, default=1024) parser.add_argument('--EF', type=int, default=10) parser.add_argument('--t', type=float, default=2) #不再tagper采样的倍率, 而是表示跨多少个step采样. parser.add_argument('--exp_order', type=str, default='0') parser.add_argument('--exp_name', type=str, default='atm') parser.add_argument('--exp_aim', type=str, default='for paper') parser.add_argument('--run_file',type=str,default='train.py') parser.add_argument('--log_name',type=str,default='pl_logs') parser.add_argument('--topk',type=int,default=2) parser.add_argument('--vsm_lambda',type=float,default=0.5) parser.add_argument('--resume', type=str, default='Yes') parser.add_argument('--max_frames', type=int, default=900) parser.add_argument('--loss', type=str, default='ExLoss', choices=['CrossEntropyLoss', 'ExLoss']) parser.add_argument('--init', type=float, default=-1) parser.add_argument('-m', '--momentum', type=float, default=0.5) parser.add_argument('-e', '--epochs', type=int, default=70) parser.add_argument('-s', '--step_size', type=int, default=55) parser.add_argument('--lamda', type=float, default=0.5) main(parser.parse_args())

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225

0.65019

1,460

10,237

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0.197945

0.037303

0.054201

0.01913

0.38578

0.344492

0.295234

0.227164

0.184441

0

0.020705

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false

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null

0

null

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1

0

7c0e42d68dd892a292e20be61de2cca89811eb9b

6,252

py

Python

consumer/tests/test__index_handler.py

eHealthAfrica/aether-elasticsearch-consumer

fc29a1da8cfd7482257b1023b50a1a43372886c5

[ "Apache-2.0" ]

null

consumer/tests/test__index_handler.py

eHealthAfrica/aether-elasticsearch-consumer

fc29a1da8cfd7482257b1023b50a1a43372886c5

[ "Apache-2.0" ]

8

2018-08-02T09:11:22.000Z

2021-09-13T14:12:22.000Z

consumer/tests/test__index_handler.py

eHealthAfrica/aether-elasticsearch-consumer

fc29a1da8cfd7482257b1023b50a1a43372886c5

[ "Apache-2.0" ]

1

2019-10-29T11:29:32.000Z

# Copyright (C) 2019 by eHealth Africa : http://www.eHealthAfrica.org # # See the NOTICE file distributed with this work for additional information # regarding copyright ownership. # # Licensed under the Apache License, Version 2.0 (the 'License'); # you may not use this file except in compliance with # the License. You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, # software distributed under the License is distributed on an # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY # KIND, either express or implied. See the License for the # specific language governing permissions and limitations # under the License. import json import pytest import requests import responses from time import sleep from elasticsearch.exceptions import NotFoundError from aet.logger import get_logger from app import index_handler from . import * # noqa # fixtures LOG = get_logger('TEST-IDX') # convenience function for jsonpath @responses.activate @pytest.mark.unit def test__handle_http(): responses.add( responses.GET, 'http://bad-url', json={'error': 'not found'}, status=404 ) res = requests.get('http://bad-url') with pytest.raises(requests.exceptions.HTTPError): index_handler.handle_http(res) @pytest.mark.unit def test__get_es_index_from_autoconfig(SubscriptionDefinition, ComplexSchema): es_options = SubscriptionDefinition.get('es_options') tenant = 'dev' name = 'a-topic' alias = es_options.get('alias_name') index = index_handler.get_es_index_from_subscription( es_options, name, tenant, ComplexSchema ) LOG.debug(json.dumps(index, indent=2)) assert(first('$.name', index) == f'{tenant}.{name}') geo_name = es_options['geo_point_name'] assert(first( f'$.body.mappings._doc.properties.{geo_name}', index) is not None) @pytest.mark.unit def test__get_index_for_topic(SubscriptionDefinition, ComplexSchema): name = 'Person' es_options = SubscriptionDefinition.get('es_options') geo_name = es_options.get('geo_point_name') auto_ts = es_options.get('auto_timestamp') index = index_handler.get_index_for_topic(name, geo_name, auto_ts, ComplexSchema) index = index.get('mappings', None) assert(len(index) == 1) assert(first('$._doc', index) is not None) assert(first(f'$._doc.properties.{geo_name}.type', index) == 'geo_point') assert(first(f'$._doc._meta.aet_auto_ts', index) == auto_ts) @pytest.mark.unit def test__get_es_types_from_schema(ComplexSchema): res = index_handler.get_es_types_from_schema(ComplexSchema) assert(first('$.beds.type', res) == 'integer') assert(first('$.username.type', res) == 'keyword') assert(first('$._start.type', res) == 'date') assert(first('$.geometry.type', res) == 'object') assert(first('$.meta.type', res) == 'object') assert(first('$.mandatory_date.type', res) == 'date') assert(first('$.mandatory_date.format', res) == 'date') assert(first('$.optional_dt.type', res) == 'date') assert(first('$.optional_dt.format', res) == 'epoch_millis') assert(len(list(res.keys())) == 55) @pytest.mark.unit def test__make_kibana_index(AutoGenSchema): name = 'kibana-index-name' res = index_handler.make_kibana_index(name, AutoGenSchema) assert(res.get('attributes', {}).get('title') == name) @pytest.mark.unit def test___find_timestamp(ComplexSchema): result = index_handler._find_timestamp(ComplexSchema) assert(result == 'timestamp') @pytest.mark.unit def test___format_lookups(ComplexSchema): formatted = index_handler._format_lookups(ComplexSchema) assert( json.dumps( formatted.get( 'operational_status'), sort_keys=True) == json.dumps( SAMPLE_FIELD_LOOKUP.get( 'operational_status'), sort_keys=True) ) @pytest.mark.unit def test___format_single_lookup(ComplexSchema): matching = ComplexSchema.get_node('MySurvey.operational_status') res = index_handler._format_single_lookup(matching) assert( json.dumps(res, sort_keys=True) == json.dumps(SAMPLE_FIELD_LOOKUP.get( 'operational_status'), sort_keys=True) ) @pytest.mark.unit def test__get_alias_from_namespace(): namespace = 'A_Gather_Form_V1' res = index_handler.get_alias_from_namespace(namespace) assert(res == 'A_Gather_Form') @pytest.mark.integration def test__update_es_index(TestElasticsearch, PolySchemaA, PolySchemaB): # register index with mapping es = TestElasticsearch.get_session() doc_id = 'poly-test-doc' doc = { 'id': doc_id, 'poly': '1001' } index_a = index_handler.get_es_index_from_subscription( es_options={}, name='test1', tenant='test-tenant', schema=PolySchemaA ) index_name = index_a.get('name') index_b = index_handler.get_es_index_from_subscription( es_options={}, name='test1', tenant='test-tenant', schema=PolySchemaB ) alias = index_handler.get_alias_from_namespace(PolySchemaA.name) # register schema A index_handler.update_es_index(es, index_a, 'test-tenant', alias) # put doc es.create( index=index_name, id=doc_id, body=doc ) es.indices.refresh(index=index_name) res = es.search(index=index_name, body={ "query": {"term": {"poly": "1001"}} }) assert(res.get('hits').get('max_score') < 1.0) # find imperfect by string res = es.search(index=index_name, body={ "query": {"term": {"poly": 1001}} }) assert(res.get('hits').get('max_score') < 1.0) # find imperfect by string # migrate to schema B index_handler.update_es_index(es, index_b, 'test-tenant', alias) es.indices.refresh(index=index_name) res = es.search(index=index_name, body={ "query": {"term": {"poly": "1001"}} }) assert(res.get('hits').get('max_score') == 1.0) # find by string res = es.search(index=index_name, body={ "query": {"term": {"poly": 1001}} }) assert(res.get('hits').get('max_score') == 1.0) # find by int

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null

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7c0f8b607ed4a4992f5429c04c93d80a3e6a70fc

9,656

py

Python

tests/test_api_transaction.py

preston-wagner/authorizesauce

130ee30f500c8b5bf9a6384296ca4f5d5bb565e7

[ "MIT" ]

null

tests/test_api_transaction.py

preston-wagner/authorizesauce

130ee30f500c8b5bf9a6384296ca4f5d5bb565e7

[ "MIT" ]

null

tests/test_api_transaction.py

preston-wagner/authorizesauce

130ee30f500c8b5bf9a6384296ca4f5d5bb565e7

[ "MIT" ]

1

2020-06-17T15:48:46.000Z

from datetime import date from six import BytesIO, binary_type, u from six.moves.urllib.parse import parse_qsl, urlencode from unittest2 import TestCase import mock from authorizesauce.apis.transaction import PROD_URL, TEST_URL, TransactionAPI from authorizesauce.data import Address, CreditCard from authorizesauce.exceptions import AuthorizeConnectionError, \ AuthorizeResponseError class MockResponse(BytesIO): class Headers(dict): def getparam(self, *args, **kwargs): """Python 2 version""" return None def get_content_charset(self, failobj=None, *args, **kwargs): """Python 3 version""" return failobj def __init__(self, *args, **kwargs): BytesIO.__init__(self, *args, **kwargs) self.headers = self.Headers() SUCCESS = MockResponse( b'1;1;1;This transaction has been approved.;IKRAGJ;Y;2171062816;;;20.00;CC' b';auth_only;;Jeffrey;Schenck;;45 Rose Ave;Venice;CA;90291;USA;;;;;;;;;;;;' b';;;;;375DD9293D7605E20DF0B437EE2A7B92;P;2;;;;;;;;;;;XXXX1111;Visa;;;;;;;' b';;;;;;;;;;Y') PARSED_SUCCESS = { 'cvv_response': 'P', 'authorization_code': 'IKRAGJ', 'response_code': '1', 'amount': '20.00', 'transaction_type': 'auth_only', 'avs_response': 'Y', 'response_reason_code': '1', 'response_reason_text': 'This transaction has been approved.', 'transaction_id': '2171062816', } ERROR = MockResponse( b'2;1;2;This transaction has been declined.;000000;N;2171062816;;;20.00;CC' b';auth_only;;Jeffrey;Schenck;;45 Rose Ave;Venice;CA;90291;USA;;;;;;;;;;;;' b';;;;;375DD9293D7605E20DF0B437EE2A7B92;N;1;;;;;;;;;;;XXXX1111;Visa;;;;;;;' b';;;;;;;;;;Y') PARSED_ERROR = { 'cvv_response': 'N', 'authorization_code': '000000', 'response_code': '2', 'amount': '20.00', 'transaction_type': 'auth_only', 'avs_response': 'N', 'response_reason_code': '2', 'response_reason_text': 'This transaction has been declined.', 'transaction_id': '2171062816', } def _unicode_str(s): if isinstance(s, binary_type): return s.decode('unicode_escape') return s def _are_params_eq(params1, params2): _params1, _params2 = map(_unicode_str, (params1, params2)) return frozenset(parse_qsl(_params1)) == frozenset(parse_qsl(_params2)) class TransactionAPITests(TestCase): def setUp(self): self.api = TransactionAPI('123', '456') self.success = lambda *args, **kwargs: SUCCESS.seek(0) or SUCCESS self.error = lambda *args, **kwargs: ERROR.seek(0) or ERROR self.year = date.today().year + 10 self.credit_card = CreditCard('4111111111111111', self.year, 1, '911') self.address = Address('45 Rose Ave', 'Venice', 'CA', '90291') def test_basic_api(self): api = TransactionAPI('123', '456') self.assertEqual(api.url, TEST_URL) api = TransactionAPI('123', '456', debug=False) self.assertEqual(api.url, PROD_URL) @mock.patch('authorizesauce.apis.transaction.urlopen') def test_make_call(self, urlopen): urlopen.side_effect = self.success params = {'a': '1', 'b': '2'} result = self.api._make_call(params) self.assertEqual(urlopen.call_args[0][0], TEST_URL) self.assertTrue(_are_params_eq( urlopen.call_args[1]['data'], urlencode(params) )) self.assertEqual(result, PARSED_SUCCESS) @mock.patch('authorizesauce.apis.transaction.urlopen') def test_make_call_with_unicode(self, urlopen): urlopen.side_effect = self.success result = self.api._make_call({u('\xe3'): '1', 'b': u('\xe3')}) self.assertEqual(urlopen.call_args[0][0], TEST_URL) self.assertTrue(_are_params_eq( urlopen.call_args[1]['data'], 'b=%C3%A3&%C3%A3=1' )) self.assertEqual(result, PARSED_SUCCESS) @mock.patch('authorizesauce.apis.transaction.urlopen') def test_make_call_connection_error(self, urlopen): urlopen.side_effect = IOError('Borked') self.assertRaises(AuthorizeConnectionError, self.api._make_call, {'a': '1', 'b': '2'}) @mock.patch('authorizesauce.apis.transaction.urlopen') def test_make_call_response_error(self, urlopen): urlopen.side_effect = self.error try: self.api._make_call({'a': '1', 'b': '2'}) except AuthorizeResponseError as e: self.assertTrue(str(e).startswith( 'This transaction has been declined.' )) self.assertEqual(e.full_response, PARSED_ERROR) def test_add_params(self): self.assertEqual(self.api._add_params({}), {}) params = self.api._add_params({}, credit_card=self.credit_card) self.assertEqual(params, { 'x_card_num': '4111111111111111', 'x_exp_date': '01-{0}'.format(self.year), 'x_card_code': '911', }) params = self.api._add_params({}, address=self.address) self.assertEqual(params, { 'x_address': '45 Rose Ave', 'x_city': 'Venice', 'x_state': 'CA', 'x_zip': '90291', 'x_country': 'US', }) params = self.api._add_params( {}, credit_card=self.credit_card, address=self.address ) self.assertEqual(params, { 'x_card_num': '4111111111111111', 'x_exp_date': '01-{0}'.format(self.year), 'x_card_code': '911', 'x_address': '45 Rose Ave', 'x_city': 'Venice', 'x_state': 'CA', 'x_zip': '90291', 'x_country': 'US', }) @mock.patch('authorizesauce.apis.transaction.urlopen') def test_auth(self, urlopen): urlopen.side_effect = self.success result = self.api.auth(20, self.credit_card, self.address) self.assertEqual(urlopen.call_args[0][0], TEST_URL) self.assertTrue(urlopen.call_args[1]['data'], ( 'x_login=123&x_zip=90291&x_card_num=4111111111111111&' 'x_amount=20.00&x_tran_key=456&x_city=Venice&x_country=US&' 'x_version=3.1&x_state=CA&x_delim_char=%3B&' 'x_address=45+Rose+Ave&x_exp_date=01-{0}&x_test_request=FALSE' '&x_card_code=911&x_type=AUTH_ONLY&x_delim_data=TRUE'.format( str(self.year) ) )) self.assertEqual(result, PARSED_SUCCESS) @mock.patch('authorizesauce.apis.transaction.urlopen') def test_capture(self, urlopen): urlopen.side_effect = self.success result = self.api.capture(20, self.credit_card, self.address) self.assertEqual(urlopen.call_args[0][0], TEST_URL) self.assertTrue(urlopen.call_args[1]['data'], ( 'x_login=123&x_zip=90291&x_card_num=4111111111111111&' 'x_amount=20.00&x_tran_key=456&x_city=Venice&x_country=US&' 'x_version=3.1&x_state=CA&x_delim_char=%3B&' 'x_address=45+Rose+Ave&x_exp_date=01-{0}&x_test_request=FALSE' '&x_card_code=911&x_type=AUTH_ONLY&x_delim_data=TRUE'.format( str(self.year) ) )) self.assertEqual(result, PARSED_SUCCESS) @mock.patch('authorizesauce.apis.transaction.urlopen') def test_settle(self, urlopen): urlopen.side_effect = self.success # Test without specified amount result = self.api.settle('123456') self.assertEqual(urlopen.call_args[0][0], TEST_URL) self.assertTrue(urlopen.call_args[1]['data'], ( 'https://test.authorize.net/gateway/transact.dll?x_login=123' '&x_trans_id=123456&x_version=3.1&x_delim_char=%3B' '&x_type=PRIOR_AUTH_CAPTURE&x_delim_data=TRUE&x_tran_key=456' '&x_test_request=FALSE' )) self.assertEqual(result, PARSED_SUCCESS) # Test with specified amount result = self.api.settle('123456', amount=10) self.assertEqual(urlopen.call_args[0][0], TEST_URL) self.assertTrue(urlopen.call_args[1]['data'], ( 'https://test.authorize.net/gateway/transact.dll?x_login=123' '&x_trans_id=123456&x_version=3.1&x_delim_char=%3B' '&x_type=PRIOR_AUTH_CAPTURE&x_amount=10.00&x_delim_data=TRUE' '&x_tran_key=456&x_test_request=FALSE' )) self.assertEqual(result, PARSED_SUCCESS) @mock.patch('authorizesauce.apis.transaction.urlopen') def test_credit(self, urlopen): urlopen.side_effect = self.success # Test with transaction_id, amount result = self.api.credit('1111', '123456', 10) self.assertEqual(urlopen.call_args[0][0], TEST_URL) self.assertTrue(urlopen.call_args[1]['data'], ( 'https://test.authorize.net/gateway/transact.dll?x_login=123' '&x_trans_id=123456&x_version=3.1&x_amount=10.00' '&x_delim_char=%3B&x_type=CREDIT&x_card_num=1111' '&x_delim_data=TRUE&x_tran_key=456&x_test_request=FALSE' )) self.assertEqual(result, PARSED_SUCCESS) @mock.patch('authorizesauce.apis.transaction.urlopen') def test_void(self, urlopen): urlopen.side_effect = self.success result = self.api.void('123456') self.assertEqual(urlopen.call_args[0][0], TEST_URL) self.assertTrue(urlopen.call_args[1]['data'], ( 'https://test.authorize.net/gateway/transact.dll?x_login=123' '&x_trans_id=123456&x_version=3.1&x_delim_char=%3B&x_type=VOID' '&x_delim_data=TRUE&x_tran_key=456&x_test_request=FALSE' )) self.assertEqual(result, PARSED_SUCCESS)

40.06639

79

0.629453

1,225

9,656

4.730612

0.149388

0.059534

0.041415

0.041933

0.69698

0.662985

0.630026

0.586885

0.556514

0.541329

0

0.065495

0.222038

9,656

240

80

40.233333

0.705937

0.012842

0

0.487923

0

0.009662

0.310117

0.185629

0

0.15942

1

0.082126

false

0

0.038647

0

0.15942

0

null

0

null

0

1

0

7c11512944aa360a8ca2b2179d573b01222bea5e

2,621

py

Python

build_json.py

sungpyocho/covid19-aichi-tools

5170bf405f67b14179fe10838701ec5baa9d6cc1

[ "MIT" ]

null

build_json.py

sungpyocho/covid19-aichi-tools

5170bf405f67b14179fe10838701ec5baa9d6cc1

[ "MIT" ]

null

build_json.py

sungpyocho/covid19-aichi-tools

5170bf405f67b14179fe10838701ec5baa9d6cc1

[ "MIT" ]

null

import csv import io import json import pandas as pd import sys from dateutil import tz from datetime import datetime, date, time, timedelta # Japan Standard Time (UTC + 09:00) JST = tz.gettz('Asia/Tokyo') JST_current_time = datetime.now(tz=JST).strftime('%Y/%m/%d %H:%M') patients_list = [] patients_summary_dic = {} # 引数を取得異常系処理はしてないので注意 args = sys.argv with open('data/patients.csv', 'r', encoding="utf-8") as csvfile: reader = csv.DictReader(csvfile) for row in reader: patients_list.append(row) patients_summary_dic.setdefault(row['date'], 0) patients_summary_dic[row['date']] += 1 # 日付のリストを生成 strdt = datetime.strptime("2020-01-26", '%Y-%m-%d') # 開始日 enddt = datetime.strptime(args[1], '%Y-%m-%d') # 終了日 # 日付差の日数を算出（リストに最終日も含めたいので、＋１しています） days_num = (enddt - strdt).days + 1 datelist = [] for i in range(days_num): datelist.append(strdt + timedelta(days = i)) patients_summary_list = [] # 日付の新しい順に辿って小計が 0 でない日から開始する foundZero = True for date in reversed(datelist): if (not (date.strftime('%Y-%m-%d') in patients_summary_dic)) and foundZero: continue else: foundZero = False patients_summary_dic.setdefault(date.strftime('%Y-%m-%d'), 0) patients_summary_list.append({ "日付": date.strftime('%Y-%m-%d'), "小計": patients_summary_dic[date.strftime('%Y-%m-%d')] }) patients_summary_list = patients_summary_list[::-1] # 日付の昇順に並び替え # main_summary_history.csvをPandasのDataframeに変換 main_summary_history_df = pd.read_csv('data/main_summary_history.csv', keep_default_na=False) # 検査件数の読み込み inspections_summary_list = [] with open('data/inspections_summary.csv', 'r', encoding="utf-8") as csvfile: reader = csv.DictReader(csvfile) for row in reader: inspections_summary_list.append({ "日付": datetime.strptime(row['検査日'], '%Y/%m/%d').strftime('%Y-%m-%d'), "小計": int(row['検査件数（件）']), "合算": row['合算'] }) data = { "lastUpdate": JST_current_time, "patients": { "date": JST_current_time, "data": patients_list }, "patients_summary" : { "date": JST_current_time, "data": patients_summary_list }, "inspections_summary" : { "date": JST_current_time, "data": inspections_summary_list }, "main_summary_history": { "date": JST_current_time, "data": json.loads(main_summary_history_df.to_json(orient='records', force_ascii=False)) } } sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8') print(json.dumps(data, indent=4, ensure_ascii=False))

28.48913

96

0.655857

343

2,621

4.819242

0.341108

0.108893

0.016334

0.039927

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0.136721

0.078645

0

0.011342

0.192675

2,621

91

97

28.802198

0.769849

0.076307

0

0.147059

0

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0.023651

0

1

0

false

0

0.102941

0

0.102941

0.014706

0

null

0

null

0

1

0

7c138f84c229bf0a17e877706fc36f489907d8bf

23,732

py

Python

scipy/optimize/_numdiff.py

jeremiedbb/scipy

2bea64c334b18fd445a7945b350d7ace2dc22913

[ "BSD-3-Clause" ]

1

2019-12-19T16:51:27.000Z

scipy/optimize/_numdiff.py

jeremiedbb/scipy

2bea64c334b18fd445a7945b350d7ace2dc22913

[ "BSD-3-Clause" ]

null

scipy/optimize/_numdiff.py

jeremiedbb/scipy

2bea64c334b18fd445a7945b350d7ace2dc22913

[ "BSD-3-Clause" ]

null

"""Routines for numerical differentiation.""" from __future__ import division import numpy as np from numpy.linalg import norm from scipy.sparse.linalg import LinearOperator from ..sparse import issparse, csc_matrix, csr_matrix, coo_matrix, find from ._group_columns import group_dense, group_sparse EPS = np.finfo(np.float64).eps def _adjust_scheme_to_bounds(x0, h, num_steps, scheme, lb, ub): """Adjust final difference scheme to the presence of bounds. Parameters ---------- x0 : ndarray, shape (n,) Point at which we wish to estimate derivative. h : ndarray, shape (n,) Desired finite difference steps. num_steps : int Number of `h` steps in one direction required to implement finite difference scheme. For example, 2 means that we need to evaluate f(x0 + 2 * h) or f(x0 - 2 * h) scheme : {'1-sided', '2-sided'} Whether steps in one or both directions are required. In other words '1-sided' applies to forward and backward schemes, '2-sided' applies to center schemes. lb : ndarray, shape (n,) Lower bounds on independent variables. ub : ndarray, shape (n,) Upper bounds on independent variables. Returns ------- h_adjusted : ndarray, shape (n,) Adjusted step sizes. Step size decreases only if a sign flip or switching to one-sided scheme doesn't allow to take a full step. use_one_sided : ndarray of bool, shape (n,) Whether to switch to one-sided scheme. Informative only for ``scheme='2-sided'``. """ if scheme == '1-sided': use_one_sided = np.ones_like(h, dtype=bool) elif scheme == '2-sided': h = np.abs(h) use_one_sided = np.zeros_like(h, dtype=bool) else: raise ValueError("`scheme` must be '1-sided' or '2-sided'.") if np.all((lb == -np.inf) & (ub == np.inf)): return h, use_one_sided h_total = h * num_steps h_adjusted = h.copy() lower_dist = x0 - lb upper_dist = ub - x0 if scheme == '1-sided': x = x0 + h_total violated = (x < lb) | (x > ub) fitting = np.abs(h_total) <= np.maximum(lower_dist, upper_dist) h_adjusted[violated & fitting] *= -1 forward = (upper_dist >= lower_dist) & ~fitting h_adjusted[forward] = upper_dist[forward] / num_steps backward = (upper_dist < lower_dist) & ~fitting h_adjusted[backward] = -lower_dist[backward] / num_steps elif scheme == '2-sided': central = (lower_dist >= h_total) & (upper_dist >= h_total) forward = (upper_dist >= lower_dist) & ~central h_adjusted[forward] = np.minimum( h[forward], 0.5 * upper_dist[forward] / num_steps) use_one_sided[forward] = True backward = (upper_dist < lower_dist) & ~central h_adjusted[backward] = -np.minimum( h[backward], 0.5 * lower_dist[backward] / num_steps) use_one_sided[backward] = True min_dist = np.minimum(upper_dist, lower_dist) / num_steps adjusted_central = (~central & (np.abs(h_adjusted) <= min_dist)) h_adjusted[adjusted_central] = min_dist[adjusted_central] use_one_sided[adjusted_central] = False return h_adjusted, use_one_sided relative_step = {"2-point": EPS**0.5, "3-point": EPS**(1/3), "cs": EPS**0.5} def _compute_absolute_step(rel_step, x0, method): if rel_step is None: rel_step = relative_step[method] sign_x0 = (x0 >= 0).astype(float) * 2 - 1 return rel_step * sign_x0 * np.maximum(1.0, np.abs(x0)) def _prepare_bounds(bounds, x0): lb, ub = [np.asarray(b, dtype=float) for b in bounds] if lb.ndim == 0: lb = np.resize(lb, x0.shape) if ub.ndim == 0: ub = np.resize(ub, x0.shape) return lb, ub def group_columns(A, order=0): """Group columns of a 2-D matrix for sparse finite differencing [1]_. Two columns are in the same group if in each row at least one of them has zero. A greedy sequential algorithm is used to construct groups. Parameters ---------- A : array_like or sparse matrix, shape (m, n) Matrix of which to group columns. order : int, iterable of int with shape (n,) or None Permutation array which defines the order of columns enumeration. If int or None, a random permutation is used with `order` used as a random seed. Default is 0, that is use a random permutation but guarantee repeatability. Returns ------- groups : ndarray of int, shape (n,) Contains values from 0 to n_groups-1, where n_groups is the number of found groups. Each value ``groups[i]`` is an index of a group to which ith column assigned. The procedure was helpful only if n_groups is significantly less than n. References ---------- .. [1] A. Curtis, M. J. D. Powell, and J. Reid, "On the estimation of sparse Jacobian matrices", Journal of the Institute of Mathematics and its Applications, 13 (1974), pp. 117-120. """ if issparse(A): A = csc_matrix(A) else: A = np.atleast_2d(A) A = (A != 0).astype(np.int32) if A.ndim != 2: raise ValueError("`A` must be 2-dimensional.") m, n = A.shape if order is None or np.isscalar(order): rng = np.random.RandomState(order) order = rng.permutation(n) else: order = np.asarray(order) if order.shape != (n,): raise ValueError("`order` has incorrect shape.") A = A[:, order] if issparse(A): groups = group_sparse(m, n, A.indices, A.indptr) else: groups = group_dense(m, n, A) groups[order] = groups.copy() return groups def approx_derivative(fun, x0, method='3-point', rel_step=None, f0=None, bounds=(-np.inf, np.inf), sparsity=None, as_linear_operator=False, args=(), kwargs={}): """Compute finite difference approximation of the derivatives of a vector-valued function. If a function maps from R^n to R^m, its derivatives form m-by-n matrix called the Jacobian, where an element (i, j) is a partial derivative of f[i] with respect to x[j]. Parameters ---------- fun : callable Function of which to estimate the derivatives. The argument x passed to this function is ndarray of shape (n,) (never a scalar even if n=1). It must return 1-D array_like of shape (m,) or a scalar. x0 : array_like of shape (n,) or float Point at which to estimate the derivatives. Float will be converted to a 1-D array. method : {'3-point', '2-point', 'cs'}, optional Finite difference method to use: - '2-point' - use the first order accuracy forward or backward difference. - '3-point' - use central difference in interior points and the second order accuracy forward or backward difference near the boundary. - 'cs' - use a complex-step finite difference scheme. This assumes that the user function is real-valued and can be analytically continued to the complex plane. Otherwise, produces bogus results. rel_step : None or array_like, optional Relative step size to use. The absolute step size is computed as ``h = rel_step * sign(x0) * max(1, abs(x0))``, possibly adjusted to fit into the bounds. For ``method='3-point'`` the sign of `h` is ignored. If None (default) then step is selected automatically, see Notes. f0 : None or array_like, optional If not None it is assumed to be equal to ``fun(x0)``, in this case the ``fun(x0)`` is not called. Default is None. bounds : tuple of array_like, optional Lower and upper bounds on independent variables. Defaults to no bounds. Each bound must match the size of `x0` or be a scalar, in the latter case the bound will be the same for all variables. Use it to limit the range of function evaluation. Bounds checking is not implemented when `as_linear_operator` is True. sparsity : {None, array_like, sparse matrix, 2-tuple}, optional Defines a sparsity structure of the Jacobian matrix. If the Jacobian matrix is known to have only few non-zero elements in each row, then it's possible to estimate its several columns by a single function evaluation [3]_. To perform such economic computations two ingredients are required: * structure : array_like or sparse matrix of shape (m, n). A zero element means that a corresponding element of the Jacobian identically equals to zero. * groups : array_like of shape (n,). A column grouping for a given sparsity structure, use `group_columns` to obtain it. A single array or a sparse matrix is interpreted as a sparsity structure, and groups are computed inside the function. A tuple is interpreted as (structure, groups). If None (default), a standard dense differencing will be used. Note, that sparse differencing makes sense only for large Jacobian matrices where each row contains few non-zero elements. as_linear_operator : bool, optional When True the function returns an `scipy.sparse.linalg.LinearOperator`. Otherwise it returns a dense array or a sparse matrix depending on `sparsity`. The linear operator provides an efficient way of computing ``J.dot(p)`` for any vector ``p`` of shape (n,), but does not allow direct access to individual elements of the matrix. By default `as_linear_operator` is False. args, kwargs : tuple and dict, optional Additional arguments passed to `fun`. Both empty by default. The calling signature is ``fun(x, *args, **kwargs)``. Returns ------- J : {ndarray, sparse matrix, LinearOperator} Finite difference approximation of the Jacobian matrix. If `as_linear_operator` is True returns a LinearOperator with shape (m, n). Otherwise it returns a dense array or sparse matrix depending on how `sparsity` is defined. If `sparsity` is None then a ndarray with shape (m, n) is returned. If `sparsity` is not None returns a csr_matrix with shape (m, n). For sparse matrices and linear operators it is always returned as a 2-D structure, for ndarrays, if m=1 it is returned as a 1-D gradient array with shape (n,). See Also -------- check_derivative : Check correctness of a function computing derivatives. Notes ----- If `rel_step` is not provided, it assigned to ``EPS**(1/s)``, where EPS is machine epsilon for float64 numbers, s=2 for '2-point' method and s=3 for '3-point' method. Such relative step approximately minimizes a sum of truncation and round-off errors, see [1]_. A finite difference scheme for '3-point' method is selected automatically. The well-known central difference scheme is used for points sufficiently far from the boundary, and 3-point forward or backward scheme is used for points near the boundary. Both schemes have the second-order accuracy in terms of Taylor expansion. Refer to [2]_ for the formulas of 3-point forward and backward difference schemes. For dense differencing when m=1 Jacobian is returned with a shape (n,), on the other hand when n=1 Jacobian is returned with a shape (m, 1). Our motivation is the following: a) It handles a case of gradient computation (m=1) in a conventional way. b) It clearly separates these two different cases. b) In all cases np.atleast_2d can be called to get 2-D Jacobian with correct dimensions. References ---------- .. [1] W. H. Press et. al. "Numerical Recipes. The Art of Scientific Computing. 3rd edition", sec. 5.7. .. [2] A. Curtis, M. J. D. Powell, and J. Reid, "On the estimation of sparse Jacobian matrices", Journal of the Institute of Mathematics and its Applications, 13 (1974), pp. 117-120. .. [3] B. Fornberg, "Generation of Finite Difference Formulas on Arbitrarily Spaced Grids", Mathematics of Computation 51, 1988. Examples -------- >>> import numpy as np >>> from scipy.optimize import approx_derivative >>> >>> def f(x, c1, c2): ... return np.array([x[0] * np.sin(c1 * x[1]), ... x[0] * np.cos(c2 * x[1])]) ... >>> x0 = np.array([1.0, 0.5 * np.pi]) >>> approx_derivative(f, x0, args=(1, 2)) array([[ 1., 0.], [-1., 0.]]) Bounds can be used to limit the region of function evaluation. In the example below we compute left and right derivative at point 1.0. >>> def g(x): ... return x**2 if x >= 1 else x ... >>> x0 = 1.0 >>> approx_derivative(g, x0, bounds=(-np.inf, 1.0)) array([ 1.]) >>> approx_derivative(g, x0, bounds=(1.0, np.inf)) array([ 2.]) """ if method not in ['2-point', '3-point', 'cs']: raise ValueError("Unknown method '%s'. " % method) x0 = np.atleast_1d(x0) if x0.ndim > 1: raise ValueError("`x0` must have at most 1 dimension.") lb, ub = _prepare_bounds(bounds, x0) if lb.shape != x0.shape or ub.shape != x0.shape: raise ValueError("Inconsistent shapes between bounds and `x0`.") if as_linear_operator and not (np.all(np.isinf(lb)) and np.all(np.isinf(ub))): raise ValueError("Bounds not supported when " "`as_linear_operator` is True.") def fun_wrapped(x): f = np.atleast_1d(fun(x, *args, **kwargs)) if f.ndim > 1: raise RuntimeError("`fun` return value has " "more than 1 dimension.") return f if f0 is None: f0 = fun_wrapped(x0) else: f0 = np.atleast_1d(f0) if f0.ndim > 1: raise ValueError("`f0` passed has more than 1 dimension.") if np.any((x0 < lb) | (x0 > ub)): raise ValueError("`x0` violates bound constraints.") if as_linear_operator: if rel_step is None: rel_step = relative_step[method] return _linear_operator_difference(fun_wrapped, x0, f0, rel_step, method) else: h = _compute_absolute_step(rel_step, x0, method) if method == '2-point': h, use_one_sided = _adjust_scheme_to_bounds( x0, h, 1, '1-sided', lb, ub) elif method == '3-point': h, use_one_sided = _adjust_scheme_to_bounds( x0, h, 1, '2-sided', lb, ub) elif method == 'cs': use_one_sided = False if sparsity is None: return _dense_difference(fun_wrapped, x0, f0, h, use_one_sided, method) else: if not issparse(sparsity) and len(sparsity) == 2: structure, groups = sparsity else: structure = sparsity groups = group_columns(sparsity) if issparse(structure): structure = csc_matrix(structure) else: structure = np.atleast_2d(structure) groups = np.atleast_1d(groups) return _sparse_difference(fun_wrapped, x0, f0, h, use_one_sided, structure, groups, method) def _linear_operator_difference(fun, x0, f0, h, method): m = f0.size n = x0.size if method == '2-point': def matvec(p): if np.array_equal(p, np.zeros_like(p)): return np.zeros(m) dx = h / norm(p) x = x0 + dx*p df = fun(x) - f0 return df / dx elif method == '3-point': def matvec(p): if np.array_equal(p, np.zeros_like(p)): return np.zeros(m) dx = 2*h / norm(p) x1 = x0 - (dx/2)*p x2 = x0 + (dx/2)*p f1 = fun(x1) f2 = fun(x2) df = f2 - f1 return df / dx elif method == 'cs': def matvec(p): if np.array_equal(p, np.zeros_like(p)): return np.zeros(m) dx = h / norm(p) x = x0 + dx*p*1.j f1 = fun(x) df = f1.imag return df / dx else: raise RuntimeError("Never be here.") return LinearOperator((m, n), matvec) def _dense_difference(fun, x0, f0, h, use_one_sided, method): m = f0.size n = x0.size J_transposed = np.empty((n, m)) h_vecs = np.diag(h) for i in range(h.size): if method == '2-point': x = x0 + h_vecs[i] dx = x[i] - x0[i] # Recompute dx as exactly representable number. df = fun(x) - f0 elif method == '3-point' and use_one_sided[i]: x1 = x0 + h_vecs[i] x2 = x0 + 2 * h_vecs[i] dx = x2[i] - x0[i] f1 = fun(x1) f2 = fun(x2) df = -3.0 * f0 + 4 * f1 - f2 elif method == '3-point' and not use_one_sided[i]: x1 = x0 - h_vecs[i] x2 = x0 + h_vecs[i] dx = x2[i] - x1[i] f1 = fun(x1) f2 = fun(x2) df = f2 - f1 elif method == 'cs': f1 = fun(x0 + h_vecs[i]*1.j) df = f1.imag dx = h_vecs[i, i] else: raise RuntimeError("Never be here.") J_transposed[i] = df / dx if m == 1: J_transposed = np.ravel(J_transposed) return J_transposed.T def _sparse_difference(fun, x0, f0, h, use_one_sided, structure, groups, method): m = f0.size n = x0.size row_indices = [] col_indices = [] fractions = [] n_groups = np.max(groups) + 1 for group in range(n_groups): # Perturb variables which are in the same group simultaneously. e = np.equal(group, groups) h_vec = h * e if method == '2-point': x = x0 + h_vec dx = x - x0 df = fun(x) - f0 # The result is written to columns which correspond to perturbed # variables. cols, = np.nonzero(e) # Find all non-zero elements in selected columns of Jacobian. i, j, _ = find(structure[:, cols]) # Restore column indices in the full array. j = cols[j] elif method == '3-point': # Here we do conceptually the same but separate one-sided # and two-sided schemes. x1 = x0.copy() x2 = x0.copy() mask_1 = use_one_sided & e x1[mask_1] += h_vec[mask_1] x2[mask_1] += 2 * h_vec[mask_1] mask_2 = ~use_one_sided & e x1[mask_2] -= h_vec[mask_2] x2[mask_2] += h_vec[mask_2] dx = np.zeros(n) dx[mask_1] = x2[mask_1] - x0[mask_1] dx[mask_2] = x2[mask_2] - x1[mask_2] f1 = fun(x1) f2 = fun(x2) cols, = np.nonzero(e) i, j, _ = find(structure[:, cols]) j = cols[j] mask = use_one_sided[j] df = np.empty(m) rows = i[mask] df[rows] = -3 * f0[rows] + 4 * f1[rows] - f2[rows] rows = i[~mask] df[rows] = f2[rows] - f1[rows] elif method == 'cs': f1 = fun(x0 + h_vec*1.j) df = f1.imag dx = h_vec cols, = np.nonzero(e) i, j, _ = find(structure[:, cols]) j = cols[j] else: raise ValueError("Never be here.") # All that's left is to compute the fraction. We store i, j and # fractions as separate arrays and later construct coo_matrix. row_indices.append(i) col_indices.append(j) fractions.append(df[i] / dx[j]) row_indices = np.hstack(row_indices) col_indices = np.hstack(col_indices) fractions = np.hstack(fractions) J = coo_matrix((fractions, (row_indices, col_indices)), shape=(m, n)) return csr_matrix(J) def check_derivative(fun, jac, x0, bounds=(-np.inf, np.inf), args=(), kwargs={}): """Check correctness of a function computing derivatives (Jacobian or gradient) by comparison with a finite difference approximation. Parameters ---------- fun : callable Function of which to estimate the derivatives. The argument x passed to this function is ndarray of shape (n,) (never a scalar even if n=1). It must return 1-D array_like of shape (m,) or a scalar. jac : callable Function which computes Jacobian matrix of `fun`. It must work with argument x the same way as `fun`. The return value must be array_like or sparse matrix with an appropriate shape. x0 : array_like of shape (n,) or float Point at which to estimate the derivatives. Float will be converted to 1-D array. bounds : 2-tuple of array_like, optional Lower and upper bounds on independent variables. Defaults to no bounds. Each bound must match the size of `x0` or be a scalar, in the latter case the bound will be the same for all variables. Use it to limit the range of function evaluation. args, kwargs : tuple and dict, optional Additional arguments passed to `fun` and `jac`. Both empty by default. The calling signature is ``fun(x, *args, **kwargs)`` and the same for `jac`. Returns ------- accuracy : float The maximum among all relative errors for elements with absolute values higher than 1 and absolute errors for elements with absolute values less or equal than 1. If `accuracy` is on the order of 1e-6 or lower, then it is likely that your `jac` implementation is correct. See Also -------- approx_derivative : Compute finite difference approximation of derivative. Examples -------- >>> import numpy as np >>> from scipy.optimize import check_derivative >>> >>> >>> def f(x, c1, c2): ... return np.array([x[0] * np.sin(c1 * x[1]), ... x[0] * np.cos(c2 * x[1])]) ... >>> def jac(x, c1, c2): ... return np.array([ ... [np.sin(c1 * x[1]), c1 * x[0] * np.cos(c1 * x[1])], ... [np.cos(c2 * x[1]), -c2 * x[0] * np.sin(c2 * x[1])] ... ]) ... >>> >>> x0 = np.array([1.0, 0.5 * np.pi]) >>> check_derivative(f, jac, x0, args=(1, 2)) 2.4492935982947064e-16 """ J_to_test = jac(x0, *args, **kwargs) if issparse(J_to_test): J_diff = approx_derivative(fun, x0, bounds=bounds, sparsity=J_to_test, args=args, kwargs=kwargs) J_to_test = csr_matrix(J_to_test) abs_err = J_to_test - J_diff i, j, abs_err_data = find(abs_err) J_diff_data = np.asarray(J_diff[i, j]).ravel() return np.max(np.abs(abs_err_data) / np.maximum(1, np.abs(J_diff_data))) else: J_diff = approx_derivative(fun, x0, bounds=bounds, args=args, kwargs=kwargs) abs_err = np.abs(J_to_test - J_diff) return np.max(abs_err / np.maximum(1, np.abs(J_diff)))

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7c147e3dd10a5e110c033ad9ba1df174aabe3c39

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Python

tests/models/test_hparams.py

abhinavg97/pytorch-lightning

0d54cf25a2dba33e4640ac52768a83406e7a0a94

[ "Apache-2.0" ]

1

2020-10-26T09:02:08.000Z

tests/models/test_hparams.py

vivektalwar13071999/pytorch-lightning

7c4f80a1afe3d7b0f1e9ee834aacaf8439195cdf

[ "Apache-2.0" ]

null

tests/models/test_hparams.py

vivektalwar13071999/pytorch-lightning

7c4f80a1afe3d7b0f1e9ee834aacaf8439195cdf

[ "Apache-2.0" ]

null

# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import pickle from argparse import Namespace import cloudpickle import pytest import torch from fsspec.implementations.local import LocalFileSystem from omegaconf import OmegaConf, Container from torch.nn import functional as F from torch.utils.data import DataLoader from pytorch_lightning import Trainer, LightningModule from pytorch_lightning.core.saving import save_hparams_to_yaml, load_hparams_from_yaml from pytorch_lightning.utilities import AttributeDict, is_picklable from tests.base import EvalModelTemplate, TrialMNIST, BoringModel class SaveHparamsModel(EvalModelTemplate): """ Tests that a model can take an object """ def __init__(self, hparams): super().__init__() self.save_hyperparameters(hparams) class AssignHparamsModel(EvalModelTemplate): """ Tests that a model can take an object with explicit setter """ def __init__(self, hparams): super().__init__() self.hparams = hparams # ------------------------- # STANDARD TESTS # ------------------------- def _run_standard_hparams_test(tmpdir, model, cls, try_overwrite=False): """ Tests for the existence of an arg 'test_arg=14' """ hparam_type = type(model.hparams) # test proper property assignments assert model.hparams.test_arg == 14 # verify we can train trainer = Trainer(default_root_dir=tmpdir, max_epochs=1, overfit_batches=2) trainer.fit(model) # make sure the raw checkpoint saved the properties raw_checkpoint_path = _raw_checkpoint_path(trainer) raw_checkpoint = torch.load(raw_checkpoint_path) assert LightningModule.CHECKPOINT_HYPER_PARAMS_KEY in raw_checkpoint assert raw_checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]['test_arg'] == 14 # verify that model loads correctly model2 = cls.load_from_checkpoint(raw_checkpoint_path) assert model2.hparams.test_arg == 14 assert isinstance(model2.hparams, hparam_type) if try_overwrite: # verify that we can overwrite the property model3 = cls.load_from_checkpoint(raw_checkpoint_path, test_arg=78) assert model3.hparams.test_arg == 78 return raw_checkpoint_path @pytest.mark.parametrize("cls", [SaveHparamsModel, AssignHparamsModel]) def test_namespace_hparams(tmpdir, cls): # init model model = cls(hparams=Namespace(test_arg=14)) # run standard test suite _run_standard_hparams_test(tmpdir, model, cls) @pytest.mark.parametrize("cls", [SaveHparamsModel, AssignHparamsModel]) def test_dict_hparams(tmpdir, cls): # init model model = cls(hparams={'test_arg': 14}) # run standard test suite _run_standard_hparams_test(tmpdir, model, cls) @pytest.mark.parametrize("cls", [SaveHparamsModel, AssignHparamsModel]) def test_omega_conf_hparams(tmpdir, cls): # init model conf = OmegaConf.create(dict(test_arg=14, mylist=[15.4, dict(a=1, b=2)])) model = cls(hparams=conf) assert isinstance(model.hparams, Container) # run standard test suite raw_checkpoint_path = _run_standard_hparams_test(tmpdir, model, cls) model2 = cls.load_from_checkpoint(raw_checkpoint_path) assert isinstance(model2.hparams, Container) # config specific tests assert model2.hparams.test_arg == 14 assert model2.hparams.mylist[0] == 15.4 def test_explicit_args_hparams(tmpdir): """ Tests that a model can take implicit args and assign """ # define model class LocalModel(EvalModelTemplate): def __init__(self, test_arg, test_arg2): super().__init__() self.save_hyperparameters('test_arg', 'test_arg2') model = LocalModel(test_arg=14, test_arg2=90) # run standard test suite raw_checkpoint_path = _run_standard_hparams_test(tmpdir, model, LocalModel) model = LocalModel.load_from_checkpoint(raw_checkpoint_path, test_arg2=120) # config specific tests assert model.hparams.test_arg2 == 120 def test_implicit_args_hparams(tmpdir): """ Tests that a model can take regular args and assign """ # define model class LocalModel(EvalModelTemplate): def __init__(self, test_arg, test_arg2): super().__init__() self.save_hyperparameters() model = LocalModel(test_arg=14, test_arg2=90) # run standard test suite raw_checkpoint_path = _run_standard_hparams_test(tmpdir, model, LocalModel) model = LocalModel.load_from_checkpoint(raw_checkpoint_path, test_arg2=120) # config specific tests assert model.hparams.test_arg2 == 120 def test_explicit_missing_args_hparams(tmpdir): """ Tests that a model can take regular args and assign """ # define model class LocalModel(EvalModelTemplate): def __init__(self, test_arg, test_arg2): super().__init__() self.save_hyperparameters('test_arg') model = LocalModel(test_arg=14, test_arg2=90) # test proper property assignments assert model.hparams.test_arg == 14 # verify we can train trainer = Trainer(default_root_dir=tmpdir, max_epochs=2, overfit_batches=0.5) trainer.fit(model) # make sure the raw checkpoint saved the properties raw_checkpoint_path = _raw_checkpoint_path(trainer) raw_checkpoint = torch.load(raw_checkpoint_path) assert LightningModule.CHECKPOINT_HYPER_PARAMS_KEY in raw_checkpoint assert raw_checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]['test_arg'] == 14 # verify that model loads correctly model = LocalModel.load_from_checkpoint(raw_checkpoint_path, test_arg2=123) assert model.hparams.test_arg == 14 assert 'test_arg2' not in model.hparams # test_arg2 is not registered in class init return raw_checkpoint_path # ------------------------- # SPECIFIC TESTS # ------------------------- def test_class_nesting(): class MyModule(LightningModule): def forward(self): ... # make sure PL modules are always nn.Module a = MyModule() assert isinstance(a, torch.nn.Module) def test_outside(): a = MyModule() _ = a.hparams class A: def test(self): a = MyModule() _ = a.hparams def test2(self): test_outside() test_outside() A().test2() A().test() class SubClassEvalModel(EvalModelTemplate): any_other_loss = torch.nn.CrossEntropyLoss() def __init__(self, *args, subclass_arg=1200, **kwargs): super().__init__(*args, **kwargs) self.save_hyperparameters() class SubSubClassEvalModel(SubClassEvalModel): pass class AggSubClassEvalModel(SubClassEvalModel): def __init__(self, *args, my_loss=torch.nn.CrossEntropyLoss(), **kwargs): super().__init__(*args, **kwargs) self.save_hyperparameters() class UnconventionalArgsEvalModel(EvalModelTemplate): """ A model that has unconventional names for "self", "*args" and "**kwargs". """ def __init__(obj, *more_args, other_arg=300, **more_kwargs): # intentionally named obj super().__init__(*more_args, **more_kwargs) obj.save_hyperparameters() class DictConfSubClassEvalModel(SubClassEvalModel): def __init__(self, *args, dict_conf=OmegaConf.create(dict(my_param='something')), **kwargs): super().__init__(*args, **kwargs) self.save_hyperparameters() @pytest.mark.parametrize("cls", [ EvalModelTemplate, SubClassEvalModel, SubSubClassEvalModel, AggSubClassEvalModel, UnconventionalArgsEvalModel, DictConfSubClassEvalModel, ]) def test_collect_init_arguments(tmpdir, cls): """ Test that the model automatically saves the arguments passed into the constructor """ extra_args = {} if cls is AggSubClassEvalModel: extra_args.update(my_loss=torch.nn.CosineEmbeddingLoss()) elif cls is DictConfSubClassEvalModel: extra_args.update(dict_conf=OmegaConf.create(dict(my_param='anything'))) model = cls(**extra_args) assert model.hparams.batch_size == 32 model = cls(batch_size=179, **extra_args) assert model.hparams.batch_size == 179 if isinstance(model, SubClassEvalModel): assert model.hparams.subclass_arg == 1200 if isinstance(model, AggSubClassEvalModel): assert isinstance(model.hparams.my_loss, torch.nn.CosineEmbeddingLoss) # verify that the checkpoint saved the correct values trainer = Trainer(default_root_dir=tmpdir, max_epochs=2, overfit_batches=0.5) trainer.fit(model) raw_checkpoint_path = _raw_checkpoint_path(trainer) raw_checkpoint = torch.load(raw_checkpoint_path) assert LightningModule.CHECKPOINT_HYPER_PARAMS_KEY in raw_checkpoint assert raw_checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY]['batch_size'] == 179 # verify that model loads correctly model = cls.load_from_checkpoint(raw_checkpoint_path) assert model.hparams.batch_size == 179 if isinstance(model, AggSubClassEvalModel): assert isinstance(model.hparams.my_loss, torch.nn.CosineEmbeddingLoss) if isinstance(model, DictConfSubClassEvalModel): assert isinstance(model.hparams.dict_conf, Container) assert model.hparams.dict_conf['my_param'] == 'anything' # verify that we can overwrite whatever we want model = cls.load_from_checkpoint(raw_checkpoint_path, batch_size=99) assert model.hparams.batch_size == 99 def _raw_checkpoint_path(trainer) -> str: raw_checkpoint_paths = os.listdir(trainer.checkpoint_callback.dirpath) raw_checkpoint_paths = [x for x in raw_checkpoint_paths if '.ckpt' in x] assert raw_checkpoint_paths raw_checkpoint_path = raw_checkpoint_paths[0] raw_checkpoint_path = os.path.join(trainer.checkpoint_callback.dirpath, raw_checkpoint_path) return raw_checkpoint_path class LocalVariableModelSuperLast(EvalModelTemplate): """ This model has the super().__init__() call at the end. """ def __init__(self, arg1, arg2, *args, **kwargs): self.argument1 = arg1 # arg2 intentionally not set arg1 = 'overwritten' local_var = 1234 super().__init__(*args, **kwargs) # this is intentionally here at the end class LocalVariableModelSuperFirst(EvalModelTemplate): """ This model has the _auto_collect_arguments() call at the end. """ def __init__(self, arg1, arg2, *args, **kwargs): super().__init__(*args, **kwargs) self.argument1 = arg1 # arg2 intentionally not set arg1 = 'overwritten' local_var = 1234 self.save_hyperparameters() # this is intentionally here at the end @pytest.mark.parametrize("cls", [ LocalVariableModelSuperFirst, # LocalVariableModelSuperLast, ]) def test_collect_init_arguments_with_local_vars(cls): """ Tests that only the arguments are collected and not local variables. """ model = cls(arg1=1, arg2=2) assert 'local_var' not in model.hparams assert model.hparams['arg1'] == 'overwritten' assert model.hparams['arg2'] == 2 # @pytest.mark.parametrize("cls,config", [ # (SaveHparamsModel, Namespace(my_arg=42)), # (SaveHparamsModel, dict(my_arg=42)), # (SaveHparamsModel, OmegaConf.create(dict(my_arg=42))), # (AssignHparamsModel, Namespace(my_arg=42)), # (AssignHparamsModel, dict(my_arg=42)), # (AssignHparamsModel, OmegaConf.create(dict(my_arg=42))), # ]) # def test_single_config_models(tmpdir, cls, config): # """ Test that the model automatically saves the arguments passed into the constructor """ # model = cls(config) # # # no matter how you do it, it should be assigned # assert model.hparams.my_arg == 42 # # # verify that the checkpoint saved the correct values # trainer = Trainer(default_root_dir=tmpdir, max_epochs=2, overfit_batches=0.5) # trainer.fit(model) # # # verify that model loads correctly # raw_checkpoint_path = _raw_checkpoint_path(trainer) # model = cls.load_from_checkpoint(raw_checkpoint_path) # assert model.hparams.my_arg == 42 class AnotherArgModel(EvalModelTemplate): def __init__(self, arg1): super().__init__() self.save_hyperparameters(arg1) class OtherArgsModel(EvalModelTemplate): def __init__(self, arg1, arg2): super().__init__() self.save_hyperparameters(arg1, arg2) @pytest.mark.parametrize("cls,config", [ (AnotherArgModel, dict(arg1=42)), (OtherArgsModel, dict(arg1=3.14, arg2='abc')), ]) def test_single_config_models_fail(tmpdir, cls, config): """ Test fail on passing unsupported config type. """ with pytest.raises(ValueError): _ = cls(**config) @pytest.mark.parametrize("past_key", ['module_arguments']) def test_load_past_checkpoint(tmpdir, past_key): model = EvalModelTemplate() # verify we can train trainer = Trainer(default_root_dir=tmpdir, max_epochs=1) trainer.fit(model) # make sure the raw checkpoint saved the properties raw_checkpoint_path = _raw_checkpoint_path(trainer) raw_checkpoint = torch.load(raw_checkpoint_path) raw_checkpoint[past_key] = raw_checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY] raw_checkpoint['hparams_type'] = 'Namespace' raw_checkpoint[past_key]['batch_size'] = -17 del raw_checkpoint[LightningModule.CHECKPOINT_HYPER_PARAMS_KEY] # save back the checkpoint torch.save(raw_checkpoint, raw_checkpoint_path) # verify that model loads correctly model2 = EvalModelTemplate.load_from_checkpoint(raw_checkpoint_path) assert model2.hparams.batch_size == -17 def test_hparams_pickle(tmpdir): ad = AttributeDict({'key1': 1, 'key2': 'abc'}) pkl = pickle.dumps(ad) assert ad == pickle.loads(pkl) pkl = cloudpickle.dumps(ad) assert ad == pickle.loads(pkl) class UnpickleableArgsEvalModel(EvalModelTemplate): """ A model that has an attribute that cannot be pickled. """ def __init__(self, foo='bar', pickle_me=(lambda x: x + 1), **kwargs): super().__init__(**kwargs) assert not is_picklable(pickle_me) self.save_hyperparameters() def test_hparams_pickle_warning(tmpdir): model = UnpickleableArgsEvalModel() trainer = Trainer(default_root_dir=tmpdir, max_steps=1) with pytest.warns(UserWarning, match="attribute 'pickle_me' removed from hparams because it cannot be pickled"): trainer.fit(model) assert 'pickle_me' not in model.hparams def test_hparams_save_yaml(tmpdir): hparams = dict(batch_size=32, learning_rate=0.001, data_root='./any/path/here', nasted=dict(any_num=123, anystr='abcd')) path_yaml = os.path.join(tmpdir, 'testing-hparams.yaml') save_hparams_to_yaml(path_yaml, hparams) assert load_hparams_from_yaml(path_yaml) == hparams save_hparams_to_yaml(path_yaml, Namespace(**hparams)) assert load_hparams_from_yaml(path_yaml) == hparams save_hparams_to_yaml(path_yaml, AttributeDict(hparams)) assert load_hparams_from_yaml(path_yaml) == hparams save_hparams_to_yaml(path_yaml, OmegaConf.create(hparams)) assert load_hparams_from_yaml(path_yaml) == hparams class NoArgsSubClassEvalModel(EvalModelTemplate): def __init__(self): super().__init__() class SimpleNoArgsModel(LightningModule): def __init__(self): super().__init__() self.l1 = torch.nn.Linear(28 * 28, 10) def forward(self, x): return torch.relu(self.l1(x.view(x.size(0), -1))) def training_step(self, batch, batch_nb): x, y = batch loss = F.cross_entropy(self(x), y) return {'loss': loss, 'log': {'train_loss': loss}} def test_step(self, batch, batch_nb): x, y = batch loss = F.cross_entropy(self(x), y) return {'loss': loss, 'log': {'train_loss': loss}} def configure_optimizers(self): return torch.optim.Adam(self.parameters(), lr=0.02) @pytest.mark.parametrize("cls", [ SimpleNoArgsModel, NoArgsSubClassEvalModel, ]) def test_model_nohparams_train_test(tmpdir, cls): """Test models that do not tae any argument in init.""" model = cls() trainer = Trainer( max_epochs=1, default_root_dir=tmpdir, ) train_loader = DataLoader(TrialMNIST(os.getcwd(), train=True, download=True), batch_size=32) trainer.fit(model, train_loader) test_loader = DataLoader(TrialMNIST(os.getcwd(), train=False, download=True), batch_size=32) trainer.test(test_dataloaders=test_loader) def test_model_ignores_non_exist_kwargument(tmpdir): """Test that the model takes only valid class arguments.""" class LocalModel(EvalModelTemplate): def __init__(self, batch_size=15): super().__init__(batch_size=batch_size) self.save_hyperparameters() model = LocalModel() assert model.hparams.batch_size == 15 # verify that the checkpoint saved the correct values trainer = Trainer(default_root_dir=tmpdir, max_epochs=1) trainer.fit(model) # verify that we can overwrite whatever we want raw_checkpoint_path = _raw_checkpoint_path(trainer) model = LocalModel.load_from_checkpoint(raw_checkpoint_path, non_exist_kwarg=99) assert 'non_exist_kwarg' not in model.hparams class SuperClassPositionalArgs(EvalModelTemplate): def __init__(self, hparams): super().__init__() self._hparams = None # pretend EvalModelTemplate did not call self.save_hyperparameters() self.hparams = hparams class SubClassVarArgs(SuperClassPositionalArgs): """ Loading this model should accept hparams and init in the super class """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) def test_args(tmpdir): """ Test for inheritance: super class takes positional arg, subclass takes varargs. """ hparams = dict(test=1) model = SubClassVarArgs(hparams) trainer = Trainer(default_root_dir=tmpdir, max_epochs=1) trainer.fit(model) raw_checkpoint_path = _raw_checkpoint_path(trainer) with pytest.raises(TypeError, match="__init__ got an unexpected keyword argument 'test'"): SubClassVarArgs.load_from_checkpoint(raw_checkpoint_path) class RuntimeParamChangeModelSaving(BoringModel): def __init__(self, **kwargs): super().__init__() self.save_hyperparameters() class RuntimeParamChangeModelAssign(BoringModel): def __init__(self, **kwargs): super().__init__() self.hparams = kwargs @pytest.mark.parametrize("cls", [RuntimeParamChangeModelSaving, RuntimeParamChangeModelAssign]) def test_init_arg_with_runtime_change(tmpdir, cls): """Test that we save/export only the initial hparams, no other runtime change allowed""" model = cls(running_arg=123) assert model.hparams.running_arg == 123 model.hparams.running_arg = -1 assert model.hparams.running_arg == -1 model.hparams = Namespace(abc=42) assert model.hparams.abc == 42 trainer = Trainer( default_root_dir=tmpdir, limit_train_batches=2, limit_val_batches=2, limit_test_batches=2, max_epochs=1, ) trainer.fit(model) path_yaml = os.path.join(trainer.logger.log_dir, trainer.logger.NAME_HPARAMS_FILE) hparams = load_hparams_from_yaml(path_yaml) assert hparams.get('running_arg') == 123 class UnsafeParamModel(BoringModel): def __init__(self, my_path, any_param=123): super().__init__() self.save_hyperparameters() def test_model_with_fsspec_as_parameter(tmpdir): model = UnsafeParamModel(LocalFileSystem(tmpdir)) trainer = Trainer( default_root_dir=tmpdir, limit_train_batches=2, limit_val_batches=2, limit_test_batches=2, max_epochs=1, ) trainer.fit(model) trainer.test()

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Python

tests/space_test.py

hadrianmontes/jax-md

cea1cc6b22db6044a502eeeab4bddde35ac15d94

[ "ECL-2.0", "Apache-2.0" ]

713

2019-05-14T19:02:00.000Z

2022-03-31T17:42:23.000Z

tests/space_test.py

hadrianmontes/jax-md

cea1cc6b22db6044a502eeeab4bddde35ac15d94

[ "ECL-2.0", "Apache-2.0" ]

109

2019-05-15T13:27:09.000Z

2022-03-17T16:15:59.000Z

tests/space_test.py

hadrianmontes/jax-md

cea1cc6b22db6044a502eeeab4bddde35ac15d94

[ "ECL-2.0", "Apache-2.0" ]

117

2019-05-17T13:23:37.000Z

2022-03-18T10:32:29.000Z

# Copyright 2019 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # https://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for jax_md.space.""" from absl.testing import absltest from absl.testing import parameterized from jax.config import config as jax_config from jax import random import jax.numpy as jnp from jax import grad, jit, jacfwd from jax import test_util as jtu from jax_md import space, test_util, quantity, energy from jax_md.util import * from functools import partial from unittest import SkipTest test_util.update_test_tolerance(5e-5, 5e-13) jax_config.parse_flags_with_absl() jax_config.enable_omnistaging() FLAGS = jax_config.FLAGS PARTICLE_COUNT = 10 STOCHASTIC_SAMPLES = 10 SHIFT_STEPS = 10 SPATIAL_DIMENSION = [2, 3] BOX_FORMATS = ['scalar', 'vector', 'matrix'] if FLAGS.jax_enable_x64: POSITION_DTYPE = [f32, f64] else: POSITION_DTYPE = [f32] def make_periodic_general_test_system(N, dim, dtype, box_format): assert box_format in BOX_FORMATS box_size = quantity.box_size_at_number_density(N, 1.0, dim) box = dtype(box_size) if box_format == 'vector': box = jnp.array(jnp.ones(dim) * box_size, dtype) elif box_format == 'matrix': box = jnp.array(jnp.eye(dim) * box_size, dtype) d, s = space.periodic(jnp.diag(box) if box_format == 'matrix' else box) d_gf, s_gf = space.periodic_general(box) d_g, s_g = space.periodic_general(box, fractional_coordinates=False) key = random.PRNGKey(0) R_f = random.uniform(key, (N, dim), dtype=dtype) R = space.transform(box, R_f) E = jit(energy.soft_sphere_pair(d)) E_gf = jit(energy.soft_sphere_pair(d_gf)) E_g = jit(energy.soft_sphere_pair(d_g)) return R_f, R, box, (s, E), (s_gf, E_gf), (s_g, E_g) # pylint: disable=invalid-name class SpaceTest(jtu.JaxTestCase): # pylint: disable=g-complex-comprehension @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': '_dim={}_dtype={}'.format(dim, dtype.__name__), 'spatial_dimension': dim, 'dtype': dtype } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE)) def test_transform(self, spatial_dimension, dtype): key = random.PRNGKey(0) for _ in range(STOCHASTIC_SAMPLES): key, split1, split2 = random.split(key, 3) R = random.normal( split1, (PARTICLE_COUNT, spatial_dimension), dtype=dtype) T = random.normal( split2, (spatial_dimension, spatial_dimension), dtype=dtype) R_prime_exact = jnp.array(jnp.einsum('ij,kj->ki', T, R), dtype=dtype) R_prime = space.transform(T, R) self.assertAllClose(R_prime_exact, R_prime) @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': '_dim={}'.format(dim), 'spatial_dimension': dim } for dim in SPATIAL_DIMENSION)) def test_transform_grad(self, spatial_dimension): key = random.PRNGKey(0) for _ in range(STOCHASTIC_SAMPLES): key, split1, split2 = random.split(key, 3) R = random.normal(split1, (PARTICLE_COUNT, spatial_dimension)) T = random.normal(split2, (spatial_dimension, spatial_dimension)) R_prime = space.transform(T, R) energy_direct = lambda R: jnp.sum(R ** 2) energy_indirect = lambda T, R: jnp.sum(space.transform(T, R) ** 2) grad_direct = grad(energy_direct)(R_prime) grad_indirect = grad(energy_indirect, 1)(T, R) self.assertAllClose(grad_direct, grad_indirect) @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': '_dim={}_dtype={}'.format(dim, dtype.__name__), 'spatial_dimension': dim, 'dtype': dtype } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE)) def test_transform_inverse(self, spatial_dimension, dtype): key = random.PRNGKey(0) tol = 1e-13 if dtype is f32: tol = 1e-5 for _ in range(STOCHASTIC_SAMPLES): key, split1, split2 = random.split(key, 3) R = random.normal( split1, (PARTICLE_COUNT, spatial_dimension), dtype=dtype) T = random.normal( split2, (spatial_dimension, spatial_dimension), dtype=dtype) T_inv = space.inverse(T) R_test = space.transform(T_inv, space.transform(T, R)) self.assertAllClose(R, R_test) @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': '_dim={}_dtype={}'.format(dim, dtype.__name__), 'spatial_dimension': dim, 'dtype': dtype } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE)) def test_canonicalize_displacement_or_metric(self, spatial_dimension, dtype): key = random.PRNGKey(0) displacement, _ = space.periodic_general(jnp.eye(spatial_dimension)) metric = space.metric(displacement) test_metric = space.canonicalize_displacement_or_metric(displacement) metric = space.map_product(metric) test_metric = space.map_product(test_metric) for _ in range(STOCHASTIC_SAMPLES): key, split1, split2 = random.split(key, 3) R = random.normal( split1, (PARTICLE_COUNT, spatial_dimension), dtype=dtype) self.assertAllClose(metric(R, R), test_metric(R, R)) @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': '_dim={}_dtype={}'.format(dim, dtype.__name__), 'spatial_dimension': dim, 'dtype': dtype } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE)) def test_periodic_displacement(self, spatial_dimension, dtype): key = random.PRNGKey(0) for _ in range(STOCHASTIC_SAMPLES): key, split = random.split(key) R = random.uniform( split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype) dR = space.map_product(space.pairwise_displacement)(R, R) dR_wrapped = space.periodic_displacement(f32(1.0), dR) dR_direct = dR dr_direct = space.distance(dR) dr_direct = jnp.reshape(dr_direct, dr_direct.shape + (1,)) if spatial_dimension == 2: for i in range(-1, 2): for j in range(-1, 2): dR_shifted = dR + jnp.array([i, j], dtype=R.dtype) dr_shifted = space.distance(dR_shifted) dr_shifted = jnp.reshape(dr_shifted, dr_shifted.shape + (1,)) dR_direct = jnp.where(dr_shifted < dr_direct, dR_shifted, dR_direct) dr_direct = jnp.where(dr_shifted < dr_direct, dr_shifted, dr_direct) elif spatial_dimension == 3: for i in range(-1, 2): for j in range(-1, 2): for k in range(-1, 2): dR_shifted = dR + jnp.array([i, j, k], dtype=R.dtype) dr_shifted = space.distance(dR_shifted) dr_shifted = jnp.reshape(dr_shifted, dr_shifted.shape + (1,)) dR_direct = jnp.where( dr_shifted < dr_direct, dR_shifted, dR_direct) dr_direct = jnp.where( dr_shifted < dr_direct, dr_shifted, dr_direct) dR_direct = jnp.array(dR_direct, dtype=dR.dtype) assert dR_wrapped.dtype == dtype self.assertAllClose(dR_wrapped, dR_direct) @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': '_dim={}_dtype={}'.format(dim, dtype.__name__), 'spatial_dimension': dim, 'dtype': dtype } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE)) def test_periodic_shift(self, spatial_dimension, dtype): key = random.PRNGKey(0) for _ in range(STOCHASTIC_SAMPLES): key, split1, split2 = random.split(key, 3) R = random.uniform( split1, (PARTICLE_COUNT, spatial_dimension), dtype=dtype) dR = jnp.sqrt(f32(0.1)) * random.normal( split2, (PARTICLE_COUNT, spatial_dimension), dtype=dtype) dR = jnp.where(dR > 0.49, f32(0.49), dR) dR = jnp.where(dR < -0.49, f32(-0.49), dR) R_shift = space.periodic_shift(f32(1.0), R, dR) assert R_shift.dtype == R.dtype assert jnp.all(R_shift < 1.0) assert jnp.all(R_shift > 0.0) dR_after = space.periodic_displacement(f32(1.0), R_shift - R) assert dR_after.dtype == R.dtype self.assertAllClose(dR_after, dR) @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': '_dim={}_dtype={}'.format(dim, dtype.__name__), 'spatial_dimension': dim, 'dtype': dtype } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE)) def test_periodic_against_periodic_general(self, spatial_dimension, dtype): key = random.PRNGKey(0) tol = 1e-13 if dtype is f32: tol = 1e-5 for _ in range(STOCHASTIC_SAMPLES): key, split1, split2, split3 = random.split(key, 4) max_box_size = f32(10.0) box_size = max_box_size * random.uniform( split1, (spatial_dimension,), dtype=dtype) transform = jnp.diag(box_size) R = random.uniform( split2, (PARTICLE_COUNT, spatial_dimension), dtype=dtype) R_scaled = R * box_size dR = random.normal( split3, (PARTICLE_COUNT, spatial_dimension), dtype=dtype) disp_fn, shift_fn = space.periodic(box_size) general_disp_fn, general_shift_fn = space.periodic_general(transform) disp_fn = space.map_product(disp_fn) general_disp_fn = space.map_product(general_disp_fn) self.assertAllClose(disp_fn(R_scaled, R_scaled), general_disp_fn(R, R)) assert disp_fn(R_scaled, R_scaled).dtype == dtype self.assertAllClose( shift_fn(R_scaled, dR), general_shift_fn(R, dR) * box_size) assert shift_fn(R_scaled, dR).dtype == dtype @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': '_dim={}_dtype={}'.format(dim, dtype.__name__), 'spatial_dimension': dim, 'dtype': dtype } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE)) def test_periodic_against_periodic_general_grad(self, spatial_dimension, dtype): key = random.PRNGKey(0) tol = 1e-13 if dtype is f32: tol = 1e-5 for _ in range(STOCHASTIC_SAMPLES): key, split1, split2, split3 = random.split(key, 4) max_box_size = f32(10.0) box_size = max_box_size * random.uniform( split1, (spatial_dimension,), dtype=dtype) transform = jnp.diag(box_size) R = random.uniform( split2, (PARTICLE_COUNT, spatial_dimension), dtype=dtype) R_scaled = R * box_size dR = random.normal( split3, (PARTICLE_COUNT, spatial_dimension), dtype=dtype) disp_fn, shift_fn = space.periodic(box_size) general_disp_fn, general_shift_fn = space.periodic_general(transform) disp_fn = space.map_product(disp_fn) general_disp_fn = space.map_product(general_disp_fn) grad_fn = grad(lambda R: jnp.sum(disp_fn(R, R) ** 2)) general_grad_fn = grad(lambda R: jnp.sum(general_disp_fn(R, R) ** 2)) self.assertAllClose(grad_fn(R_scaled), general_grad_fn(R)) assert general_grad_fn(R).dtype == dtype @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': '_dim={}_dtype={}'.format(dim, dtype.__name__), 'spatial_dimension': dim, 'dtype': dtype, } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE)) def test_periodic_general_dynamic(self, spatial_dimension, dtype): key = random.PRNGKey(0) eye = jnp.eye(spatial_dimension) for _ in range(STOCHASTIC_SAMPLES): key, split_T0_scale, split_T0_dT = random.split(key, 3) key, split_T1_scale, split_T1_dT = random.split(key, 3) key, split_t, split_R, split_dR = random.split(key, 4) size_0 = 10.0 * random.uniform(split_T0_scale, ()) dtransform_0 = 0.5 * random.normal( split_T0_dT, (spatial_dimension, spatial_dimension)) T_0 = jnp.array(size_0 * (eye + dtransform_0), dtype=dtype) size_1 = 10.0 * random.uniform(split_T1_scale, (), dtype=dtype) dtransform_1 = 0.5 * random.normal( split_T1_dT, (spatial_dimension, spatial_dimension), dtype=dtype) T_1 = jnp.array(size_1 * (eye + dtransform_1), dtype=dtype) disp_fn, shift_fn = space.periodic_general(T_0) true_disp_fn, true_shift_fn = space.periodic_general(T_1) disp_fn = partial(disp_fn, box=T_1) disp_fn = space.map_product(disp_fn) true_disp_fn = space.map_product(true_disp_fn) R = random.uniform( split_R, (PARTICLE_COUNT, spatial_dimension), dtype=dtype) dR = random.normal( split_dR, (PARTICLE_COUNT, spatial_dimension), dtype=dtype) self.assertAllClose( disp_fn(R, R), jnp.array(true_disp_fn(R, R), dtype=dtype)) self.assertAllClose( shift_fn(R, dR, box=T_1), jnp.array(true_shift_fn(R, dR), dtype=dtype)) @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': '_dim={}_dtype={}'.format(dim, dtype.__name__), 'spatial_dimension': dim, 'dtype': dtype, } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE)) def test_periodic_general_wrapped_vs_unwrapped( self, spatial_dimension, dtype): key = random.PRNGKey(0) eye = jnp.eye(spatial_dimension, dtype=dtype) tol = 1e-13 if dtype is f32: tol = 2e-5 for _ in range(STOCHASTIC_SAMPLES): key, split_R, split_T = random.split(key, 3) dT = random.normal( split_T, (spatial_dimension, spatial_dimension), dtype=dtype) T = eye + dT + jnp.transpose(dT) R = random.uniform( split_R, (PARTICLE_COUNT, spatial_dimension), dtype=dtype) R0 = R unwrapped_R = R displacement, shift = space.periodic_general(T) _, unwrapped_shift = space.periodic_general(T, wrapped=False) displacement = space.map_product(displacement) for _ in range(SHIFT_STEPS): key, split = random.split(key) dR = random.normal( split, (PARTICLE_COUNT, spatial_dimension), dtype=dtype) R = shift(R, dR) unwrapped_R = unwrapped_shift(unwrapped_R, dR) self.assertAllClose( displacement(R, R0), displacement(unwrapped_R, R0)) assert not (jnp.all(unwrapped_R > 0) and jnp.all(unwrapped_R < 1)) @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': f'_dim={dim}_dtype={dtype.__name__}_box_format={box_format}', 'spatial_dimension': dim, 'dtype': dtype, 'box_format': box_format } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE for box_format in BOX_FORMATS)) def test_periodic_general_energy(self, spatial_dimension, dtype, box_format): N = 16 R_f, R, box, (s, E), (s_gf, E_gf), (s_g, E_g) = \ make_periodic_general_test_system(N, spatial_dimension, dtype, box_format) self.assertAllClose(E(R), E_gf(R_f)) self.assertAllClose(E(R), E_g(R)) @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': f'_dim={dim}_dtype={dtype.__name__}_box_format={box_format}', 'spatial_dimension': dim, 'dtype': dtype, 'box_format': box_format } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE for box_format in BOX_FORMATS)) def test_periodic_general_force(self, spatial_dimension, dtype, box_format): N = 16 R_f, R, box, (s, E), (s_gf, E_gf), (s_g, E_g) = \ make_periodic_general_test_system(N, spatial_dimension, dtype, box_format) self.assertAllClose(grad(E)(R), grad(E_gf)(R_f)) self.assertAllClose(grad(E)(R), grad(E_g)(R)) @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': f'_dim={dim}_dtype={dtype.__name__}_box_format={box_format}', 'spatial_dimension': dim, 'dtype': dtype, 'box_format': box_format } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE for box_format in BOX_FORMATS)) def test_periodic_general_shift(self, spatial_dimension, dtype, box_format): N = 16 R_f, R, box, (s, E), (s_gf, E_gf), (s_g, E_g) = \ make_periodic_general_test_system(N, spatial_dimension, dtype, box_format) R_new = s(R, grad(E)(R)) R_gf_new = s_gf(R_f, grad(E_gf)(R_f)) R_g_new = s_g(R, grad(E_g)(R)) self.assertAllClose(R_new, space.transform(box, R_gf_new)) self.assertAllClose(R_new, R_g_new) @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': f'_dim={dim}_dtype={dtype.__name__}_box_format={box_format}', 'spatial_dimension': dim, 'dtype': dtype, 'box_format': box_format } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE for box_format in BOX_FORMATS)) def test_periodic_general_deform(self, spatial_dimension, dtype, box_format): N = 16 R_f, R, box, (s, E), (s_gf, E_gf), (s_g, E_g) = \ make_periodic_general_test_system(N, spatial_dimension, dtype, box_format) deformed_box = box * 0.9 self.assertAllClose(E_gf(R_f, box=deformed_box), E_g(R, new_box=deformed_box)) @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': f'_dim={dim}_dtype={dtype.__name__}_box_format={box_format}', 'spatial_dimension': dim, 'dtype': dtype, 'box_format': box_format } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE for box_format in BOX_FORMATS)) def test_periodic_general_deform_grad(self, spatial_dimension, dtype, box_format): N = 16 R_f, R, box, (s, E), (s_gf, E_gf), (s_g, E_g) = \ make_periodic_general_test_system(N, spatial_dimension, dtype, box_format) deformed_box = box * 0.9 self.assertAllClose(grad(E_gf)(R_f, box=deformed_box), grad(E_g)(R, new_box=deformed_box)) self.assertAllClose(jacfwd(E_gf)(R_f, box=deformed_box), jacfwd(E_g)(R, new_box=deformed_box)) @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': f'_dim={dim}_dtype={dtype.__name__}_box_format={box_format}', 'spatial_dimension': dim, 'dtype': dtype, 'box_format': box_format } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE for box_format in BOX_FORMATS)) def test_periodic_general_deform_shift(self, spatial_dimension, dtype, box_format): N = 16 R_f, R, box, (s, E), (s_gf, E_gf), (s_g, E_g) = \ make_periodic_general_test_system(N, spatial_dimension, dtype, box_format) deformed_box = box * 0.9 R_new = s_g(R, grad(E_g)(R), new_box=deformed_box) R_gf_new = space.transform(deformed_box, s_gf(R_f, grad(E_gf)(R_f))) self.assertAllClose(R_new, R_gf_new) @parameterized.named_parameters(jtu.cases_from_list( { 'testcase_name': f'_dim={dim}_dtype={dtype.__name__}_box_format={box_format}', 'spatial_dimension': dim, 'dtype': dtype, 'box_format': box_format } for dim in SPATIAL_DIMENSION for dtype in POSITION_DTYPE for box_format in BOX_FORMATS)) def test_periodic_general_grad_box(self, spatial_dimension, dtype, box_format): if box_format == 'scalar': raise SkipTest('Scalar case fails due to JAX Issue #5849.') N = 16 R_f, R, box, (s, E), (s_gf, E_gf), (s_g, E_g) = \ make_periodic_general_test_system(N, spatial_dimension, dtype, box_format) @grad def box_energy_g_fn(box): return E_g(R, new_box=box) @grad def box_energy_gf_fn(box): return E_gf(R_f, box=box) self.assertAllClose(box_energy_g_fn(box), box_energy_gf_fn(box)) if __name__ == '__main__': absltest.main()

35.52381

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0.081019

0

null

0

null

0

1

0

7c14cbf83bd9f7d5d27ebfe3490cc6f31c415451

246

py

Python

functions/batch-custom-action/status-api/lambda.py

TrollPursePublishing/trollpurse-trollops

27e54cfd1ba1eed27097e2e3038dfab56691cf49

[ "Xnet", "Linux-OpenIB", "X11" ]

2

2020-11-18T06:04:27.000Z

2021-04-22T12:38:15.000Z

functions/batch-custom-action/status-api/lambda.py

TrollPursePublishing/trollpurse-ops

27e54cfd1ba1eed27097e2e3038dfab56691cf49

[ "Xnet", "Linux-OpenIB", "X11" ]

null

functions/batch-custom-action/status-api/lambda.py

TrollPursePublishing/trollpurse-ops

27e54cfd1ba1eed27097e2e3038dfab56691cf49

[ "Xnet", "Linux-OpenIB", "X11" ]

null

import boto3 batch_client = boto3.client('batch') def lambda_handler(event, context): describe_response = batch_client.describe_jobs( jobs=[ event.get('jobId', '')] ) return describe_response.get('jobs', [{}])[0].get('status', '')

22.363636

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0.678862

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0.136646

0

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0.138211

246

10

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0

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1

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0

0.428571

0

null

0

null

0

1

0

7c170adc77db7c06c4c5968ae2d5e3df343748b4

776

py

Python

python97/chapter05/list_gen.py

youaresherlock/PythonPractice

2e22d3fdcb26353cb0d8215c150e84d11bc9a022

[ "Apache-2.0" ]

null

python97/chapter05/list_gen.py

youaresherlock/PythonPractice

2e22d3fdcb26353cb0d8215c150e84d11bc9a022

[ "Apache-2.0" ]

null

python97/chapter05/list_gen.py

youaresherlock/PythonPractice

2e22d3fdcb26353cb0d8215c150e84d11bc9a022

[ "Apache-2.0" ]

1

2019-11-05T01:10:15.000Z

#!usr/bin/python # -*- coding:utf8 -*- # 列表生成式(列表推导式) # 1. 提取出1-20之间的奇数 # odd_list = [] # for i in range(21): # if i % 2 == 1: # odd_list.append(i) # odd_list = [i for i in range(21) if i % 2 == 1] # print(odd_list) # 2. 逻辑复杂的情况如果是奇数将结果平方 # 列表生成式性能高于列表操作 def handle_item(item): return item * item odd_list = [handle_item(i) for i in range(21) if i % 2 == 1] print(odd_list) # 生成器表达式 odd_gen = (i for i in range(21) if i % 2 == 1) print(type(odd_gen)) for item in odd_gen: print(item) # 字典推导式 my_dict = {"bobby1": 22, "bobby2": 23, "imooc.com": 5} reversed_dict = {value:key for key, value in my_dict.items()} print(reversed_dict) # 集合推导式 my_set = set(my_dict.keys()) my_set = {key for key, value in my_dict.items()} print(type(my_set))

15.836735

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137

776

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0.350365

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776

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false

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0.357143

0

null

0

null

0

1

0

7c17743faf77b54c0516f30699a3b1dc9b050a25

11,409

py

Python

src/streamlink/plugin/plugin.py

isqad/streamlink

f6708f1d38d056177ac3d614ebbb740d956d46f0

[ "BSD-2-Clause" ]

1

2017-11-26T18:48:29.000Z

src/streamlink/plugin/plugin.py

isqad/streamlink

f6708f1d38d056177ac3d614ebbb740d956d46f0

[ "BSD-2-Clause" ]

null

src/streamlink/plugin/plugin.py

isqad/streamlink

f6708f1d38d056177ac3d614ebbb740d956d46f0

[ "BSD-2-Clause" ]

1

2021-06-03T23:08:48.000Z

import ast import operator import re from collections import OrderedDict from functools import partial from ..cache import Cache from ..exceptions import PluginError, NoStreamsError from ..options import Options # FIXME: This is a crude attempt at making a bitrate's # weight end up similar to the weight of a resolution. # Someone who knows math, please fix. BIT_RATE_WEIGHT_RATIO = 2.8 ALT_WEIGHT_MOD = 0.01 QUALITY_WEIGTHS_EXTRA = { "other": { "live": 1080, }, "tv": { "hd": 1080, "sd": 576, }, "quality": { "ehq": 720, "hq": 576, "sq": 360, }, } FILTER_OPERATORS = { "<": operator.lt, "<=": operator.le, ">": operator.gt, ">=": operator.ge, } PARAMS_REGEX = r"(\w+)=({.+?}|\[.+?\]|$.+?$|'(?:[^'\\]|\\')*'|\"(?:[^\"\\]|\\\")*\"|\S+)" HIGH_PRIORITY = 30 NORMAL_PRIORITY = 20 LOW_PRIORITY = 10 NO_PRIORITY = 0 def stream_weight(stream): for group, weights in QUALITY_WEIGTHS_EXTRA.items(): if stream in weights: return weights[stream], group match = re.match(r"^(\d+)(k|p)?(\d+)?(\+)?(?:_(\d+)k)?(?:_(alt)(\d)?)?$", stream) if match: weight = 0 if match.group(6): if match.group(7): weight -= ALT_WEIGHT_MOD * int(match.group(7)) else: weight -= ALT_WEIGHT_MOD name_type = match.group(2) if name_type == "k": # bit rate bitrate = int(match.group(1)) weight += bitrate / BIT_RATE_WEIGHT_RATIO return weight, "bitrate" elif name_type == "p": # resolution weight += int(match.group(1)) if match.group(3): # fps eg. 60p or 50p weight += int(match.group(3)) if match.group(4) == "+": weight += 1 if match.group(5): # bit rate classifier for resolution weight += int(match.group(5)) / BIT_RATE_WEIGHT_RATIO return weight, "pixels" return 0, "none" def iterate_streams(streams): for name, stream in streams: if isinstance(stream, list): for sub_stream in stream: yield (name, sub_stream) else: yield (name, stream) def stream_type_priority(stream_types, stream): stream_type = type(stream[1]).shortname() try: prio = stream_types.index(stream_type) except ValueError: try: prio = stream_types.index("*") except ValueError: prio = 99 return prio def stream_sorting_filter(expr, stream_weight): match = re.match(r"(?P<op><=|>=|<|>)?(?P<value>[\w+]+)", expr) if not match: raise PluginError("Invalid filter expression: {0}".format(expr)) op, value = match.group("op", "value") op = FILTER_OPERATORS.get(op, operator.eq) filter_weight, filter_group = stream_weight(value) def func(quality): weight, group = stream_weight(quality) if group == filter_group: return not op(weight, filter_weight) return True return func def parse_url_params(url): split = url.split(" ", 1) url = split[0] params = split[1] if len(split) > 1 else '' return url, parse_params(params) def parse_params(params): rval = {} matches = re.findall(PARAMS_REGEX, params) for key, value in matches: try: value = ast.literal_eval(value) except Exception: pass rval[key] = value return rval class Plugin(object): """A plugin can retrieve stream information from the URL specified. :param url: URL that the plugin will operate on """ cache = None logger = None module = "unknown" options = Options() session = None @classmethod def bind(cls, session, module): cls.cache = Cache(filename="plugin-cache.json", key_prefix=module) cls.logger = session.logger.new_module("plugin." + module) cls.module = module cls.session = session def __init__(self, url): self.url = url @classmethod def can_handle_url(cls, url): raise NotImplementedError @classmethod def set_option(cls, key, value): cls.options.set(key, value) @classmethod def get_option(cls, key): return cls.options.get(key) @classmethod def stream_weight(cls, stream): return stream_weight(stream) @classmethod def default_stream_types(cls, streams): stream_types = ["rtmp", "hls", "hds", "http"] for name, stream in iterate_streams(streams): stream_type = type(stream).shortname() if stream_type not in stream_types: stream_types.append(stream_type) return stream_types @classmethod def broken(cls, issue=None): def func(*args, **kwargs): msg = ( "This plugin has been marked as broken. This is likely due to " "changes to the service preventing a working implementation. " ) if issue: msg += "More info: https://github.com/streamlink/streamlink/issues/{0}".format(issue) raise PluginError(msg) def decorator(*args, **kwargs): return func return decorator @classmethod def priority(cls, url): """ Return the plugin priority for a given URL, by default it returns NORMAL priority. :return: priority level """ return NORMAL_PRIORITY def streams(self, stream_types=None, sorting_excludes=None): """Attempts to extract available streams. Returns a :class:`dict` containing the streams, where the key is the name of the stream, most commonly the quality and the value is a :class:`Stream` object. The result can contain the synonyms **best** and **worst** which points to the streams which are likely to be of highest and lowest quality respectively. If multiple streams with the same name are found, the order of streams specified in *stream_types* will determine which stream gets to keep the name while the rest will be renamed to "<name>_<stream type>". The synonyms can be fine tuned with the *sorting_excludes* parameter. This can be either of these types: - A list of filter expressions in the format *[operator]<value>*. For example the filter ">480p" will exclude streams ranked higher than "480p" from the list used in the synonyms ranking. Valid operators are >, >=, < and <=. If no operator is specified then equality will be tested. - A function that is passed to filter() with a list of stream names as input. :param stream_types: A list of stream types to return. :param sorting_excludes: Specify which streams to exclude from the best/worst synonyms. .. versionchanged:: 1.4.2 Added *priority* parameter. .. versionchanged:: 1.5.0 Renamed *priority* to *stream_types* and changed behaviour slightly. .. versionchanged:: 1.5.0 Added *sorting_excludes* parameter. .. versionchanged:: 1.6.0 *sorting_excludes* can now be a list of filter expressions or a function that is passed to filter(). """ try: ostreams = self._get_streams() if isinstance(ostreams, dict): ostreams = ostreams.items() # Flatten the iterator to a list so we can reuse it. if ostreams: ostreams = list(ostreams) except NoStreamsError: return {} except (IOError, OSError, ValueError) as err: raise PluginError(err) if not ostreams: return {} if stream_types is None: stream_types = self.default_stream_types(ostreams) # Add streams depending on stream type and priorities sorted_streams = sorted(iterate_streams(ostreams), key=partial(stream_type_priority, stream_types)) streams = {} for name, stream in sorted_streams: stream_type = type(stream).shortname() # Use * as wildcard to match other stream types if "*" not in stream_types and stream_type not in stream_types: continue # drop _alt from any stream names if name.endswith("_alt"): name = name[:-len("_alt")] existing = streams.get(name) if existing: existing_stream_type = type(existing).shortname() if existing_stream_type != stream_type: name = "{0}_{1}".format(name, stream_type) if name in streams: name = "{0}_alt".format(name) num_alts = len(list(filter(lambda n: n.startswith(name), streams.keys()))) # We shouldn't need more than 2 alt streams if num_alts >= 2: continue elif num_alts > 0: name = "{0}{1}".format(name, num_alts + 1) # Validate stream name and discard the stream if it's bad. match = re.match("([A-z0-9_+]+)", name) if match: name = match.group(1) else: self.logger.debug("The stream '{0}' has been ignored " "since it is badly named.", name) continue # Force lowercase name and replace space with underscore. streams[name.lower()] = stream # Create the best/worst synonmys def stream_weight_only(s): return (self.stream_weight(s)[0] or (len(streams) == 1 and 1)) stream_names = filter(stream_weight_only, streams.keys()) sorted_streams = sorted(stream_names, key=stream_weight_only) if isinstance(sorting_excludes, list): for expr in sorting_excludes: filter_func = stream_sorting_filter(expr, self.stream_weight) sorted_streams = list(filter(filter_func, sorted_streams)) elif callable(sorting_excludes): sorted_streams = list(filter(sorting_excludes, sorted_streams)) final_sorted_streams = OrderedDict() for stream_name in sorted(streams, key=stream_weight_only): final_sorted_streams[stream_name] = streams[stream_name] if len(sorted_streams) > 0: best = sorted_streams[-1] worst = sorted_streams[0] final_sorted_streams["worst"] = streams[worst] final_sorted_streams["best"] = streams[best] return final_sorted_streams def get_streams(self, *args, **kwargs): """Deprecated since version 1.9.0. Has been renamed to :func:`Plugin.streams`, this is an alias for backwards compatibility. """ return self.streams(*args, **kwargs) def _get_streams(self): raise NotImplementedError __all__ = ["Plugin"]

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0.25

0

null

0

null

0

1

0

7c1898e479d14fbe657ed1376514f87c04d2b942

2,971

py

Python

swav/vissl/vissl/data/ssl_transforms/img_patches_tensor.py

lhoestq/DeDLOC

36f5a6d043c3d727f9d098a35fba94aa351a5cd4

[ "Apache-2.0" ]

null

swav/vissl/vissl/data/ssl_transforms/img_patches_tensor.py

lhoestq/DeDLOC

36f5a6d043c3d727f9d098a35fba94aa351a5cd4

[ "Apache-2.0" ]

null

swav/vissl/vissl/data/ssl_transforms/img_patches_tensor.py

lhoestq/DeDLOC

36f5a6d043c3d727f9d098a35fba94aa351a5cd4

[ "Apache-2.0" ]

null

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved import logging import math from typing import Any, Dict import numpy as np from classy_vision.dataset.transforms import register_transform from classy_vision.dataset.transforms.classy_transform import ClassyTransform @register_transform("ImgPatchesFromTensor") class ImgPatchesFromTensor(ClassyTransform): """ Create image patches from a torch Tensor or numpy array. This transform was proposed in Jigsaw - https://arxiv.org/abs/1603.09246 Args: num_patches (int): how many image patches to create patch_jitter (int): space to leave between patches """ def __init__(self, num_patches=9, patch_jitter=21): self.num_patches = num_patches self.patch_jitter = patch_jitter assert self.patch_jitter > 0, "Negative jitter not supported" self.grid_side_len = int(math.sqrt(self.num_patches)) # usually = 3 logging.info( f"ImgPatchesFromTensor: num_patches: {num_patches} " f"patch_jitter: {patch_jitter}" ) def __call__(self, image): """ Input image which is a torch.Tensor object of shape 3 x H x W """ data = [] grid_size = int(image.shape[1] / self.grid_side_len) patch_size = grid_size - self.patch_jitter jitter = np.random.randint( 0, self.patch_jitter, (2, self.grid_side_len, self.grid_side_len) ) for i in range(self.grid_side_len): for j in range(self.grid_side_len): x_offset = i * grid_size y_offset = j * grid_size grid_cell = image[ :, y_offset : y_offset + grid_size, x_offset : x_offset + grid_size ] patch = grid_cell[ :, jitter[1, i, j] : jitter[1, i, j] + patch_size, jitter[0, i, j] : jitter[0, i, j] + patch_size, ] assert patch.shape[1] == patch_size, "Image not cropped properly" assert patch.shape[2] == patch_size, "Image not cropped properly" # copy patch data so that all patches are different in underlying memory data.append(np.copy(patch)) return data @classmethod def from_config(cls, config: Dict[str, Any]) -> "ImgPatchesFromTensor": """ Instantiates ImgPatchesFromTensor from configuration. Args: config (Dict): arguments for for the transform Returns: ImgPatchesFromTensor instance. """ num_patches = config.get("num_patches", 9) patch_jitter = config.get("patch_jitter", 21) logging.info(f"ImgPatchesFromTensor | Using num_patches: {num_patches}") logging.info(f"ImgPatchesFromTensor | Using patch_jitter: {patch_jitter}") return cls(num_patches=num_patches, patch_jitter=patch_jitter)

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null

0

null

0

1

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7c1a65d75547f91601127884078028e187b93021

588

py

Python

prodapt_solutions/config/cliargs.py

DineshDevaraj/interview_answers

8d3d631dc96dc97ebef80604d6455c2c57c8823d

[ "MIT" ]

null

prodapt_solutions/config/cliargs.py

DineshDevaraj/interview_answers

8d3d631dc96dc97ebef80604d6455c2c57c8823d

[ "MIT" ]

null

prodapt_solutions/config/cliargs.py

DineshDevaraj/interview_answers

8d3d631dc96dc97ebef80604d6455c2c57c8823d

[ "MIT" ]

null

import argparse from helper.metaclasses_definition import Singleton class CliArgs(metaclass=Singleton): LogLevel = None BankName = None InputFilepath = None @staticmethod def init(): my_parser = argparse.ArgumentParser() my_parser.add_argument('--bank-name', required=True) my_parser.add_argument('--input-filepath') my_parser.add_argument('--log-level') args = my_parser.parse_args() CliArgs.BankName = args.bank_name CliArgs.InputFilepath = args.input_filepath CliArgs.LogLevel = args.log_level

24.5

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0.685374

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null

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null

0

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0

7c1c295aedd09d62a7ca4222595cff9f7fd4e5fc

1,237

py

Python

plugins/flytekit-papermill/setup.py

TeoZosa/flytekit

c4f33c6deaf36a3feaf397cfc6de3bd62e986733

[ "Apache-2.0" ]

null

plugins/flytekit-papermill/setup.py

TeoZosa/flytekit

c4f33c6deaf36a3feaf397cfc6de3bd62e986733

[ "Apache-2.0" ]

null

plugins/flytekit-papermill/setup.py

TeoZosa/flytekit

c4f33c6deaf36a3feaf397cfc6de3bd62e986733

[ "Apache-2.0" ]

null

from setuptools import setup PLUGIN_NAME = "papermill" microlib_name = f"flytekitplugins-{PLUGIN_NAME}" plugin_requires = [ "flytekit>=0.16.0b0,<1.0.0", "flytekitplugins-spark>=0.16.0b0,<1.0.0,!=0.24.0b0", "papermill>=1.2.0", "nbconvert>=6.0.7", "ipykernel>=5.0.0", ] __version__ = "0.0.0+develop" setup( name=microlib_name, version=__version__, author="flyteorg", author_email="admin@flyte.org", description="This is the flytekit papermill plugin", namespace_packages=["flytekitplugins"], packages=[f"flytekitplugins.{PLUGIN_NAME}"], install_requires=plugin_requires, license="apache2", python_requires=">=3.7", classifiers=[ "Intended Audience :: Science/Research", "Intended Audience :: Developers", "License :: OSI Approved :: Apache Software License", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Software Development", "Topic :: Software Development :: Libraries", "Topic :: Software Development :: Libraries :: Python Modules", ], )

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null

0

null

0

1

0

7c1e9749d62da31f126224b5dcf3c15abd4025bd

10,568

py

Python

base/frontends/views.py

danielecook/upvote.pub

fdda3c0895427ddc76f4680d0d63f2d4bac59da0

[ "MIT" ]

1

2020-09-13T09:16:44.000Z

base/frontends/views.py

danielecook/upvote.pub

fdda3c0895427ddc76f4680d0d63f2d4bac59da0

[ "MIT" ]

null

base/frontends/views.py

danielecook/upvote.pub

fdda3c0895427ddc76f4680d0d63f2d4bac59da0

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ """ import os import markdown2 from flask import (Blueprint, request, render_template, flash, g, session, redirect, url_for, abort, Markup) from werkzeug import check_password_hash, generate_password_hash from logzero import logger from base import db, app from base import search as search_module # don't override function name from base.users.forms import RegisterForm, LoginForm from base.users.models import User from base.threads.models import Thread, Publication from base.subreddits.models import Subreddit from base.users.decorators import requires_login from base.utils.user_utils import get_school from base.subreddits.forms import subreddit_subs, sub_form from base.utils.email import send_email from base.utils.misc import random_string, validate_sort_type mod = Blueprint('frontends', __name__, url_prefix='') @mod.before_request def before_request(): g.user = None if session.get('user_id'): g.user = User.query.get(session['user_id']) def home_subreddit(): logger.info(g.user) if g.get('user'): subreddit_subs = g.user.subreddit_subs.get('subs') subs = Thread.query.order_by(db.desc(Thread.hotness), db.desc(Thread.hotness)) \ .filter(Subreddit.name.in_(subreddit_subs)) else: subs = Thread.query.order_by(db.desc(Thread.hotness), db.desc(Thread.hotness)) return subs def get_subreddits(): """ Fetch user subreddits otherwise fetch a list of defaults """ if g.get('user'): subreddit_subs = g.user.subreddit_subs.get('subs') subreddits = Subreddit.query.filter(Subreddit.name.in_(subreddit_subs)) else: # Default set of subreddits subreddits = Subreddit.query.all() return subreddits def process_thread_paginator(trending=False, rs=None, subreddit=None, sort_type='hot'): """ abstracted because many sources pull from a thread listing source (subreddit permalink, homepage, etc) """ threads_per_page = 15 cur_page = request.args.get('page') or 1 cur_page = int(cur_page) thread_paginator = None # if we are passing in a resultset, that means we are just looking to # quickly paginate some arbitrary data, no sorting if rs: thread_paginator = rs.paginate(cur_page, per_page=threads_per_page, error_out=True) return thread_paginator # sexy line of code :) base_query = subreddit.threads if subreddit else Thread.query # Filter by user subs logger.info(g.user) if g.user: subreddit_subs = g.user.subreddit_subs.get('subs') base_query = base_query.join(Subreddit).filter(Subreddit.name.in_(subreddit_subs)) # Sorting if sort_type == 'hot': base_query = base_query.order_by(db.desc(Thread.hotness)) elif sort_type == 'top': base_query = base_query.order_by(db.desc(Thread.votes)) elif sort_type == 'comments': base_query = base_query.order_by(db.desc(Thread.n_comments)) elif sort_type == 'new': base_query = base_query.order_by(db.desc(Thread.created_on)) elif sort_type == 'publication_date': base_query = base_query.join(Publication).order_by(db.desc(Publication.pub_date)) thread_paginator = base_query.paginate(cur_page, per_page=threads_per_page, error_out=True) return thread_paginator @mod.route('/') def home(sort_type='hot'): """ If not trending we order by creation date """ atom_url = url_for('subreddits.atom_feed', subreddit_name='frontpage', _external=True) trending = True if request.path.endswith('trending') else False page_title = "Trending" if trending else "Frontpage" thread_paginator = process_thread_paginator(trending=trending) return render_template('home.html', atom_url=atom_url, page_title=page_title, cur_subreddit=home_subreddit(), thread_paginator=thread_paginator) @mod.route('/.atom') @mod.route('/.xml') @mod.route('/.rss') def atom_redirect(): return redirect(url_for("subreddits.atom_feed", subreddit_name="frontpage")) @mod.route('/h/<string:page>') def render_markdown(page): page_md = f"base/markdown/{page}.md" if not os.path.exists(page_md): abort(404) with open(page_md, 'r') as f: content = f.read() md = markdown2.markdown(content, extras = ['fenced-code-blocks', 'nofollow', 'target-blank-links', 'toc', 'tables', 'footnotes', 'metadata', 'markdown-in-html']) return render_template('markdown.html', page=md, **md.metadata) @mod.route('/search/', methods=['GET']) def search(): """ Allows users to search threads and comments """ query = request.args.get('query') page_title=f"Search results for '{query}'" rs = search_module.search(query, orderby='creation', search_title=True, search_text=True) thread_paginator = process_thread_paginator(rs=rs) #rs = rs.all() num_searches = rs.count() subreddits = get_subreddits() return render_template('home.html', page_title=page_title, cur_subreddit=home_subreddit(), thread_paginator=thread_paginator, num_searches=num_searches) @mod.route('/login/', methods=['GET', 'POST']) def login(): """ We had to do some extra work to route the user back to his or her original place before logging in """ if g.user: return redirect(url_for('frontends.home')) next = '' if request.method == 'GET': if 'next' in request.args: next = request.args['next'] form = LoginForm(request.form) # make sure data is valid, but doesn't validate password is right if form.validate_on_submit(): # continue where we left off if so user = User.query.filter_by(email=form.email.data).first() # we use werzeug to validate user's password if user and check_password_hash(user.password, form.password.data): # the session can't be modified as it's signed, # it's a safe place to store the user id session['user_id'] = user.id if 'next' in request.form and request.form['next']: return redirect(request.form['next']) return redirect(url_for('frontends.home')) flash('Wrong email or password', 'danger') return render_template("login.html", form=form, next=next) @mod.route('/logout/', methods=['GET', 'POST']) @requires_login def logout(): session.pop('user_id', None) return redirect(url_for('frontends.home')) @mod.route('/confirm-email/<string:token>') def confirm_email(token): """ Confirm user email """ user = User.query.filter_by(email_token=token).first() if user.email_token == token: user.email_verified = True db.session.commit() flash("Thank you for confirming your email! You can now submit and comment.", 'success') return redirect(url_for('frontends.home')) @mod.route('/register/', methods=['GET', 'POST']) def register(): """ Registration page """ if g.user: # If the user is logged in send them home return redirect(url_for('frontends.home')) next = '' if request.method == 'GET': if 'next' in request.args: next = request.args['next'] form = RegisterForm(request.form) if form.validate_on_submit(): # create an user instance not yet stored in the database user = User(username=form.username.data, email=form.email.data, \ password=generate_password_hash(form.password.data), university=get_school(form.email.data), email_token=random_string()) # Insert the record in our database and commit it db.session.add(user) email_confirm_link = url_for('frontends.confirm_email', token = user.email_token) email_response = send_email("Confirm upvote.pub email", """Please visit the link below to confirm your email:\n\n{}{}""".format(request.url_root.strip("/"), email_confirm_link), user.email) # Log the user in, as he now has an id db.session.commit() session['user_id'] = user.id flash('Thanks for signing up! Please confirm your email by following the link sent in the confirmation email.', 'success') if 'next' in request.form and request.form['next']: return redirect(request.form['next']) return redirect(url_for('frontends.home')) return render_template("register.html", form=form, next=next) @mod.route('/subs/', methods=['GET', 'POST']) def view_all(): """ """ subreddit_list = Subreddit.query.all() form = None if g.user: if request.form: form = subreddit_subs(request.form) if form.validate_on_submit(): form_subs = form.data.get('subs') form_subs = list(set([x['sub_name'] for x in form_subs if x['value']])) g.user.subreddit_subs = {'subs': form_subs} flash("Updated Subs", 'success') db.session.commit() else: form = subreddit_subs() for subreddit in subreddit_list: sform = sub_form() sform.sub_name = subreddit.name sform.sub_group = subreddit.group if g.user: sform.value=subreddit.name in g.user.subreddit_subs['subs'] form.subs.append_entry(sform) return render_template('subreddits/subs.html', cur_subreddit=None, page_title='subs', form=form, subreddit_list=subreddit_list)

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false

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0.009662

0

null

0

null

0

1

0

7c1ed9a736672c0c84e29905bebe37cc7b644280

2,949

py

Python

Jarvis.py

vijayeshmt/Securitylock

5877663a170a22ab8b5931dcef07c74f149cf9b8

[ "CC0-1.0" ]

1

2021-05-27T09:05:00.000Z

Jarvis.py

vijayeshmt/Securitylock

5877663a170a22ab8b5931dcef07c74f149cf9b8

[ "CC0-1.0" ]

null

Jarvis.py

vijayeshmt/Securitylock

5877663a170a22ab8b5931dcef07c74f149cf9b8

[ "CC0-1.0" ]

null

import pyttsx3 import datetime import speech_recognition as sr import wikipedia import webbrowser import os import smtplib engine = pyttsx3.init('sapi5') voices = engine.getProperty('voices') engine.setProperty('voice', voices[0].id) # To change the voice to female change 0 to 1. def speak(audio): engine.say(audio) engine.runAndWait() pass def take_command(): """ It takes microphone input from the user and returns a string :return: """ r = sr.Recognizer() with sr.Microphone() as source: print("Listening...") r.pause_threshold = 1.5 # It will wait 1.5 seconds to complete a sentence audio = r.listen(source) #Do read details try: print("Recognizing") query = r.recognize_google(audio,language='en-in') print(f'user said : {query}\n') except Exception as e: #print(e) print("Say that again please") return "None" return query def sendEmail(to,content): server =smtplib.SMTP('smtp.gmail.com',28) # server.connect("smtp.gmail.com",465) # server.ehlo() server.login('jayeshvijayesh@gmail.com','########') server.sendmail('jayeshvijayesh@gmail.com',to,content) server.close() def wish_me(): hour = int(datetime.datetime.now().hour) if hour >= 0 and hour < 12: speak("Good morning") elif hour >= 12 and hour < 18: speak("Good afternoon") else: speak("Good night") speak("I am JARVIS how can i help you") if __name__ == '__main__': wish_me() while True: query =take_command().lower() if 'wikipedia' in query: speak("Searching wikipedia") query = query.replace('wikipedia','') results = wikipedia.summary(query,sentences=2)#To read more increase sentence to decrease sentence decreease sentence speak("According to wikipedia") #print(results) speak(results) elif 'open youtube' in query: # webbrowser.Chrome.open_new("youtube.com") webbrowser.open("youtube.com") elif "open google" in query: webbrowser.open("google.com") elif "play music" in query: music_dir = "D:\\vijayesh\\music" songs = os.listdir(music_dir) print(songs) os.startfile(os.path.join(music_dir,songs[1])) elif "the time" in query: strtime = datetime.datetime.now().strftime("%H:%M:%S") speak(f"The time is {strtime}") elif " open pycharm" in query: pycharmpath ="C:\\Program Files\\JetBrains\\PyCharm Community Edition 2021" os.startfile(pycharmpath) #elif "open command" in query: # filelocation = "path of the particular file like above" # os.startfile(filelocation) elif " email to vijayesh" or "email to vijesh" in query: try: speak("What should i say")#error present content = take_command() to = "jayeshvijayesh@gmail.com" sendEmail(to,content) speak("Email has been sent") exit() except Exception as e: print(e) speak("Sorry,I am not able to send this email") exit()

26.097345

121

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401

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4.837905

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0

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0.206172

2,949

112

122

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0.816745

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0

1

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false

0.012658

0.088608

0

0.164557

0.075949

0

null

0

null

0

1

0

7c1ff3b3368700c34adbc70fc88801c1bc52b535

2,838

py

Python

utils/data_loader.py

dilum1995/DAugmentor

6cc86dccf826415a88b8226265e16ae96b5cc05b

[ "MIT" ]

1

2020-08-02T13:06:03.000Z

utils/data_loader.py

dilum1995/DAugmentor

6cc86dccf826415a88b8226265e16ae96b5cc05b

[ "MIT" ]

null

utils/data_loader.py

dilum1995/DAugmentor

6cc86dccf826415a88b8226265e16ae96b5cc05b

[ "MIT" ]

null

import pandas as pd import os import numpy as np import cv2 from utils import constants as const import matplotlib.pyplot as plt class DataLoader: def load_data(): ''' This function is handling the data loading and pre-processing :return: (xtrain, ytrain), (xtest, ytest) ''' print('**** Read data into DAugmentor ****') x_train = [] y_train = [] x_test = [] y_test = [] # setting the path to metadata path = const.PATH metadata_csv_path = os.path.join(path, const.FILE_METADATA) test_img_dir_path = os.path.join(path, const.DIR_TEST) train_img_dir_path = os.path.join(path, const.DIR_TRAIN) print(metadata_csv_path) # setting the path to train data x_train_path = os.path.join(path, const.DIR_TRAIN) print(x_train_path) # setting the path to train data x_test_path = os.path.join(path, const.DIR_TEST) # reading meta data file as dataframe df = pd.read_csv(metadata_csv_path, delimiter=',') # dataset format: # image_name # label # data_type data_type_row = df["data_type"].tolist() image_row = df["image_name"].tolist() label_row = df["label"].tolist() data_rows = len(data_type_row) for row in range(data_rows): if (data_type_row[row] == "TRAIN"): # setting the path of the current image img_path = os.path.join(train_img_dir_path, image_row[row]) # reading image image = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE) # downscaling image to 28x28 image = cv2.resize(image, (128, 128)) x_train.append(image) print("Loaded: " + img_path) # extracting labels y_train.append(label_row[row]) if (data_type_row[row] == "TEST"): # setting the path of the current image img_path = os.path.join(test_img_dir_path, image_row[row]) # reading image image = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE) # downscaling image to 28x28 image = cv2.resize(image, (128, 128)) x_test.append(image) print("Loaded: " + img_path) # extracting labels y_test.append(label_row[row]) xtrain = np.asarray(x_train) ytrain = np.asarray(y_train) xtest = np.asarray(x_test) ytest = np.asarray(y_test) print(x_train[0].shape) print(x_train[0].shape) print(xtrain[0].shape) print(x_test[0].shape) #(X_train, y_train), (X_test, y_test) return (xtrain, ytrain), (xtest, ytest)

31.88764

75

0.565891

361

2,838

4.232687

0.235457

0.031414

0.045812

0.064136

0.541885

0.484293

0.443717

0.353403

0.221204

0

0.016411

0.33439

2,838

89

76

31.88764

0.792483

0.180056

0

0.16

0

0.037281

0

1

0.02

false

0

0.12

0

0.18

0

null

0

null

0

1

0

7c2027c5e127752f77dcae4527133dc870a9894e

288

py

Python

CompilerPython/LexerPython/main.py

valternunez/Compiler

879cecbbeb1c21d9d19021664ace62442273d3ba

[ "MIT" ]

null

CompilerPython/LexerPython/main.py

valternunez/Compiler

879cecbbeb1c21d9d19021664ace62442273d3ba

[ "MIT" ]

null

CompilerPython/LexerPython/main.py

valternunez/Compiler

879cecbbeb1c21d9d19021664ace62442273d3ba

[ "MIT" ]

null

from lexer import * import sys if len(sys.argv) != 2: print("usage: main.py file") else: lex = Lexer(sys.argv[1]) with open(sys.argv[1]) as f: while True: c = f.read(1) if not c: break print(lex.scan().toString())

19.2

40

0.496528

42

288

3.404762

0.666667

0.146853

0.111888

0

0.021858

0.364583

288

14

41

20.571429

0.759563

0

0.065972

0

1

0

false

0

0.166667

0

0.166667

0

null

0

null

0

1

0

7c20c3110a71ede08c1358d9822f7b43bb07338f

4,903

py

Python

3D/Train_Module_3D.py

geometatqueens/RCNN

2e1e67264969f05a2f554595577dfb1025938245

[ "Unlicense" ]

1

2020-04-30T21:31:59.000Z

3D/Train_Module_3D.py

geometatqueens/RCNN

2e1e67264969f05a2f554595577dfb1025938245

[ "Unlicense" ]

null

3D/Train_Module_3D.py

geometatqueens/RCNN

2e1e67264969f05a2f554595577dfb1025938245

[ "Unlicense" ]

null

"""The present code is the Version 1.0 of the RCNN approach to perform MPS in 3D for categorical variables. It has been developed by S. Avalos and J. Ortiz in the Geometallurygical Group at Queen's University as part of a PhD program. The code is not free of bugs but running end-to-end. Any comments and further improvements are well recevied to: 17saa6@queensu.ca April 16, 2019. Geomet Group - Queen's University - Canada""" # Do not display the AVX message about using GPU import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' #from tensorflow.python.client import device_lib #print(device_lib.list_local_devices()) #os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # see issue #152 #os.environ["CUDA_VISIBLE_DEVICES"]="0" ## ######################### import numpy as np import tensorflow as tf import time import External_Functions_3D as fns_nested import gc for ind0 in range(1): start_time_AllTrain = time.time() HyperPar = [] HyperPar.append(50) # SGsizex - Num 0 HyperPar.append(50) # SGsizey - Num 1 HyperPar.append(50) # SGsizez - Num 2 HyperPar.append(int(7)) # Search_x - Num 3 HyperPar.append(int(7)) # Search_y - Num 4 HyperPar.append(int(7)) # Search_z - Num 5 HyperPar.append(int(7)) # IPsizex - Num 6 HyperPar.append(int(7)) # IPsizey - Num 7 HyperPar.append(int(7)) # IPsizez - Num 8 HyperPar.append(50) # Percentage of Data Conditioning - Num 9 .. divided by 3 so 1% is 10 represents 1% HyperPar.append(1) # MinDC - Num 10 HyperPar.append(1500) # Num Fully Connected - Num 11 HyperPar.append(3) # wdnh - Num 12 HyperPar.append(16) # convdepth - Num 13 HyperPar.append(2) # num of categories - Num 14 print("SG: ", int(HyperPar[3]),"x",int(HyperPar[4]),"x",int(HyperPar[5]), "IP: ", int(HyperPar[6]),"x",int(HyperPar[7]),"x",int(HyperPar[8])) Ncicles = 500 Nepoch = 1 #Nbatch = 250 Nsamples = 512 TrainingImage = "TI_Collaboration_1of4_50x50x50_newRepresentation.dat" LocModel = 'Models/3D_NewRepresentation/Allperc/%sx%sx%s_%sx%sx%s_4ConvNets_4HL_BN_3FC%s_ws%sx%sx%s_%sconvdepth/FeatMaps'%(int(HyperPar[3]),int(HyperPar[4]),int(HyperPar[5]), int(HyperPar[6]),int(HyperPar[7]),int(HyperPar[8]), int(HyperPar[11]), int(HyperPar[12]),int(HyperPar[12]),int(HyperPar[12]), int(HyperPar[13])) #LocModel = 'Models/3D_NewRepresentation/New%sperc/%sx%sx%s_%sx%sx%s_4ConvNets_4HL_BN_3FC%s_ws%sx%sx%s_%sconvdepth/FeatMaps'%(int(HyperPar[9]), int(HyperPar[3]),int(HyperPar[4]),int(HyperPar[5]), int(HyperPar[6]),int(HyperPar[7]),int(HyperPar[8]), int(HyperPar[11]), int(HyperPar[12]),int(HyperPar[12]),int(HyperPar[12]), int(HyperPar[13])) LocFile = 'Models/3D_NewRepresentation/Allperc/%sx%sx%s_%sx%sx%s_4ConvNets_4HL_BN_3FC%s_ws%sx%sx%s_%sconvdepth'%(int(HyperPar[3]),int(HyperPar[4]),int(HyperPar[5]), int(HyperPar[6]),int(HyperPar[7]),int(HyperPar[8]), int(HyperPar[11]), int(HyperPar[12]),int(HyperPar[12]),int(HyperPar[12]), int(HyperPar[13])) #LocFile = 'Models/3D_NewRepresentation/New%sperc/%sx%sx%s_%sx%sx%s_4ConvNets_4HL_BN_3FC%s_ws%sx%sx%s_%sconvdepth'%(int(HyperPar[9]), int(HyperPar[3]),int(HyperPar[4]),int(HyperPar[5]), int(HyperPar[6]),int(HyperPar[7]),int(HyperPar[8]), int(HyperPar[11]), int(HyperPar[12]),int(HyperPar[12]),int(HyperPar[12]), int(HyperPar[13])) print("[Graph]") #fns_nested.CreateGraph_4ConvNets_4HL_NFeaConv_wdnhxwdnh_BN_3D_NoBN(HyperPar=HyperPar, LocModel=LocModel) fns_nested.CreateGraph_4ConvNets_4HL_NFeaConv_wdnhxwdnh_BN_3D(HyperPar=HyperPar, LocModel=LocModel) # To save the TI TempSimGrid = fns_nested.Grid(HyperPar=HyperPar, DBname=TrainingImage, Lvl=3,Training=False, Padding=True) TempSimGrid.SavePlot(name=LocModel+'_TI.png', Level=1) MaxLR, MinLR = 0.01, 0.001 StepLR = 10 PointStart = 1 for indTrain in range(Ncicles): #HyperPar[9] = np.random.randint(41)+10 cuos = indTrain%(2*StepLR) if cuos < StepLR: LearningRate = np.around(((MaxLR - MinLR)/StepLR)*cuos + MinLR, decimals=7) else: LearningRate = np.around(((MaxLR - MinLR)/StepLR)*(StepLR - cuos) + MaxLR, decimals=7) start_time_1 = time.time() print ("Cicle: {}".format(indTrain+PointStart), "Learning Rate: ", LearningRate) TempSimGrid = fns_nested.Grid(HyperPar=HyperPar, DBname=TrainingImage, Lvl=5, Training=True, Padding=True) print("[Sim]") TempSimGrid.Simulate_4ConvNets_BN_3D(LocModel=LocModel, Cicle=(indTrain+PointStart), Plot=True) print("[Saving Grid]") TempSimGrid.SaveGrid(file="{}/TrainReas_{}.txt".format(LocFile, indTrain+PointStart)) print("[Train]") TempSimGrid.Train_4ConvNets_BN_3D(Epochs=Nepoch, Num_samples=Nsamples, LocModel=LocModel, LR=LearningRate) print("--%s seconds of whole training process-" % (np.around((time.time() - start_time_1), decimals=2))) gc.collect() print(" ") print("--%s minutes of ALL training-" % ((time.time() - start_time_AllTrain)/60))

53.879121

343

0.713237

743

4,903

4.585464

0.312248

0.16789

0.017611

0.056355

0.377458

0.356325

0.335192

0.267684

0

0.048894

0.124006

4,903

91

344

53.879121

0.744354

0.373241

0

0.172414

0

0.034483

0.151505

0.088577

0

1

0

false

0

0.103448

0

0.103448

0.155172

0

null

0

null

0

1

0

7c21319778186a2abea07c3db5dcc502d14e209f

1,306

py

Python

feature_flags_project/feature_flags/providers.py

steuke/django_feature_flags_example

00e33378999d6d567c37593c17289405fc7b5847

[ "MIT" ]

null

feature_flags_project/feature_flags/providers.py

steuke/django_feature_flags_example

00e33378999d6d567c37593c17289405fc7b5847

[ "MIT" ]

3

2021-09-22T18:56:38.000Z

2021-11-29T16:11:59.000Z

feature_flags_project/feature_flags/providers.py

steuke/django_feature_flags_example

00e33378999d6d567c37593c17289405fc7b5847

[ "MIT" ]

null

import logging from typing import Dict from django.http import HttpRequest logger = logging.getLogger(__name__) class FeatureFlagProvider: def is_feature_enabled(self, feature_name: str, user_id: str = None, attributes: Dict = None): raise NotImplementedError("You must override FeatureFlagProvider.is_feature_enabled()") def _attributes_from_request(request: HttpRequest) -> Dict: if not request: return dict() attributes = dict() try: attributes["is_staff"] = request.user.is_staff return attributes except Exception: logger.exception( "Unexpected exception while trying to parse http-request for feature-attributes." ) return dict() def is_feature_enabled(feature_name: str, request: HttpRequest) -> bool: from django.conf import settings is_enabled = False attributes = _attributes_from_request(request) try: is_enabled = settings.FEATURE_FLAG_PROVIDER.is_feature_enabled( feature_name=feature_name, user_id="dontcare", attributes=attributes ) logger.info(f"Feature '{feature_name}' is enabled={is_enabled}") except Exception: logger.exception(f"Exception while trying to check feature-flag state for '{feature_name}'") return is_enabled

32.65

100

0.712098

153

1,306

5.856209

0.346405

0.073661

0.071429

0.042411

0.060268

0

0.20827

1,306

39

101

33.487179

0.866538

0

0.193548

0

0.20827

0.030628

0

1

0.096774

false

0

0.129032

0

0.387097

0

null

0

null

0

1

0

7c23d8601d0a15002cc4ed3c1cea741aa47089e1

34,227

py

Python

src/plottoolbox/functions/kde.py

timcera/plottoolbox

b5f4b634d366eb5ba244e2f1fd33a7ef0eba7298

[ "BSD-3-Clause" ]

null

src/plottoolbox/functions/kde.py

timcera/plottoolbox

b5f4b634d366eb5ba244e2f1fd33a7ef0eba7298

[ "BSD-3-Clause" ]

6

2021-09-06T21:26:12.000Z

2022-03-30T11:55:56.000Z

src/plottoolbox/functions/kde.py

timcera/plottoolbox

b5f4b634d366eb5ba244e2f1fd33a7ef0eba7298

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- """Collection of functions for the manipulation of time series.""" from __future__ import absolute_import, division, print_function import itertools import os import warnings import mando import numpy as np import pandas as pd from mando.rst_text_formatter import RSTHelpFormatter from tstoolbox import tsutils from .. import plotutils warnings.filterwarnings("ignore") @mando.command("kde", formatter_class=RSTHelpFormatter, doctype="numpy") @tsutils.doc(plotutils.ldocstrings) def kde_cli( input_ts="-", columns=None, start_date=None, end_date=None, clean=False, skiprows=None, index_type="datetime", names=None, ofilename="plot.png", xtitle="", ytitle="", title="", figsize="10,6.0", legend=None, legend_names=None, subplots=False, sharex=True, sharey=False, colors="auto", linestyles="auto", markerstyles=" ", bar_hatchstyles="auto", style="auto", logx=False, logy=False, xaxis="arithmetic", yaxis="arithmetic", xlim=None, ylim=None, secondary_y=False, mark_right=True, scatter_matrix_diagonal="kde", bootstrap_size=50, bootstrap_samples=500, norm_xaxis=False, norm_yaxis=False, lognorm_xaxis=False, lognorm_yaxis=False, xy_match_line="", grid=False, label_rotation=None, label_skip=1, force_freq=None, drawstyle="default", por=False, invert_xaxis=False, invert_yaxis=False, round_index=None, plotting_position="weibull", prob_plot_sort_values="descending", source_units=None, target_units=None, lag_plot_lag=1, plot_styles="bright", hlines_y=None, hlines_xmin=None, hlines_xmax=None, hlines_colors=None, hlines_linestyles="-", vlines_x=None, vlines_ymin=None, vlines_ymax=None, vlines_colors=None, vlines_linestyles="-", ): r"""Kernel density estimation of probability density function. "kde" will create a plot of estimation of the probability density function based on the data called kernel density estimation (KDE). {ydata} Parameters ---------- {input_ts} ofilename : str [optional, defaults to 'plot.png'] Output filename for the plot. Extension defines the type, for example 'filename.png' will create a PNG file. If used within Python, and `ofilename` is None will return the Matplotlib figure that can then be changed or added to as needed. lag_plot_lag [optional, default to 1] The lag used if ``type`` "lag_plot" is chosen. xtitle : str [optional, default depends on ``type``] Title of x-axis. ytitle : str [optional, default depends on ``type``] Title of y-axis. title : str [optional, defaults to ''] Title of chart. figsize : str [optional, defaults to '10,6.5'] The 'width,height' of plot in inches. legend [optional, defaults to True] Whether to display the legend. legend_names : str [optional, defaults to None] Legend would normally use the time-series names associated with the input data. The 'legend_names' option allows you to override the names in the data set. You must supply a comma separated list of strings for each time-series in the data set. subplots [optional, defaults to False] Make separate subplots for each time series. sharex [optional, default to True] In case subplots=True, share x axis. sharey [optional, default to False] In case subplots=True, share y axis. colors [optional, default is 'auto'] The default 'auto' will cycle through matplotlib colors in the chosen style. At the command line supply a comma separated matplotlib color codes, or within Python a list of color code strings. Can identify colors in four different ways. 1. Use 'CN' where N is a number from 0 to 9 that gets the Nth color from the current style. 2. Single character code from the table below. +------+---------+ | Code | Color | +======+=========+ | b | blue | +------+---------+ | g | green | +------+---------+ | r | red | +------+---------+ | c | cyan | +------+---------+ | m | magenta | +------+---------+ | y | yellow | +------+---------+ | k | black | +------+---------+ 3. Number between 0 and 1 that represents the level of gray, where 0 is white an 1 is black. 4. Any of the HTML color names. +------------------+ | HTML Color Names | +==================+ | red | +------------------+ | burlywood | +------------------+ | chartreuse | +------------------+ | ...etc. | +------------------+ Color reference: http://matplotlib.org/api/colors_api.html linestyles [optional, default to 'auto'] If 'auto' will iterate through the available matplotlib line types. Otherwise on the command line a comma separated list, or a list of strings if using the Python API. To not display lines use a space (' ') as the linestyle code. Separated 'colors', 'linestyles', and 'markerstyles' instead of using the 'style' keyword. +---------+--------------+ | Code | Lines | +=========+==============+ | ``-`` | solid | +---------+--------------+ | -- | dashed | +---------+--------------+ | -. | dash_dot | +---------+--------------+ | : | dotted | +---------+--------------+ | None | draw nothing | +---------+--------------+ | ' ' | draw nothing | +---------+--------------+ | '' | draw nothing | +---------+--------------+ Line reference: http://matplotlib.org/api/artist_api.html markerstyles [optional, default to ' '] The default ' ' will not plot a marker. If 'auto' will iterate through the available matplotlib marker types. Otherwise on the command line a comma separated list, or a list of strings if using the Python API. Separated 'colors', 'linestyles', and 'markerstyles' instead of using the 'style' keyword. +-------+----------------+ | Code | Markers | +=======+================+ | . | point | +-------+----------------+ | o | circle | +-------+----------------+ | v | triangle down | +-------+----------------+ | ^ | triangle up | +-------+----------------+ | < | triangle left | +-------+----------------+ | > | triangle right | +-------+----------------+ | 1 | tri_down | +-------+----------------+ | 2 | tri_up | +-------+----------------+ | 3 | tri_left | +-------+----------------+ | 4 | tri_right | +-------+----------------+ | 8 | octagon | +-------+----------------+ | s | square | +-------+----------------+ | p | pentagon | +-------+----------------+ | ``*`` | star | +-------+----------------+ | h | hexagon1 | +-------+----------------+ | H | hexagon2 | +-------+----------------+ | ``+`` | plus | +-------+----------------+ | x | x | +-------+----------------+ | D | diamond | +-------+----------------+ | d | thin diamond | +-------+----------------+ | _ | hlines_y | +-------+----------------+ | None | nothing | +-------+----------------+ | ' ' | nothing | +-------+----------------+ | '' | nothing | +-------+----------------+ Marker reference: http://matplotlib.org/api/markers_api.html style [optional, default is None] Still available, but if None is replaced by 'colors', 'linestyles', and 'markerstyles' options. Currently the 'style' option will override the others. Comma separated matplotlib style strings per time-series. Just combine codes in 'ColorMarkerLine' order, for example 'r*--' is a red dashed line with star marker. bar_hatchstyles [optional, default to "auto", only used if type equal to "bar", "barh", "bar_stacked", and "barh_stacked"] If 'auto' will iterate through the available matplotlib hatch types. Otherwise on the command line a comma separated list, or a list of strings if using the Python API. +-----------------+-------------------+ | bar_hatchstyles | Description | +=================+===================+ | / | diagonal hatching | +-----------------+-------------------+ | ``\`` | back diagonal | +-----------------+-------------------+ | ``|`` | vertical | +-----------------+-------------------+ | - | horizontal | +-----------------+-------------------+ | + | crossed | +-----------------+-------------------+ | x | crossed diagonal | +-----------------+-------------------+ | o | small circle | +-----------------+-------------------+ | O | large circle | +-----------------+-------------------+ | . | dots | +-----------------+-------------------+ | * | stars | +-----------------+-------------------+ logx DEPRECATED: use '--xaxis="log"' instead. logy DEPRECATED: use '--yaxis="log"' instead. xlim [optional, default is based on range of x values] Comma separated lower and upper limits for the x-axis of the plot. For example, '--xlim 1,1000' would limit the plot from 1 to 1000, where '--xlim ,1000' would base the lower limit on the data and set the upper limit to 1000. ylim [optional, default is based on range of y values] Comma separated lower and upper limits for the y-axis of the plot. See `xlim` for examples. xaxis : str [optional, default is 'arithmetic'] Defines the type of the xaxis. One of 'arithmetic', 'log'. yaxis : str [optional, default is 'arithmetic'] Defines the type of the yaxis. One of 'arithmetic', 'log'. secondary_y [optional, default is False] Whether to plot on the secondary y-axis. If a list/tuple, which time-series to plot on secondary y-axis. mark_right [optional, default is True] When using a secondary_y axis, should the legend label the axis of the various time-series automatically. scatter_matrix_diagonal : str [optional, defaults to 'kde'] If plot type is 'scatter_matrix', this specifies the plot along the diagonal. One of 'kde' for Kernel Density Estimation or 'hist' for a histogram. bootstrap_size : int [optional, defaults to 50] The size of the random subset for 'bootstrap' plot. bootstrap_samples [optional, defaults to 500] The number of random subsets of 'bootstrap_size'. norm_xaxis DEPRECATED: use '--type="norm_xaxis"' instead. norm_yaxis DEPRECATED: use '--type="norm_yaxis"' instead. lognorm_xaxis DEPRECATED: use '--type="lognorm_xaxis"' instead. lognorm_yaxis DEPRECATED: use '--type="lognorm_yaxis"' instead. xy_match_line : str [optional, defaults is ''] Will add a match line where x == y. Set to a line style code. grid [optional, default is False] Whether to plot grid lines on the major ticks. label_rotation : int [optional] Rotation for major labels for bar plots. label_skip : int [optional] Skip for major labels for bar plots. drawstyle : str [optional, default is 'default'] 'default' connects the points with lines. The steps variants produce step-plots. 'steps' is equivalent to 'steps-pre' and is maintained for backward-compatibility. ACCEPTS:: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post'] por [optional] Plot from first good value to last good value. Strips NANs from beginning and end. {force_freq} invert_xaxis [optional, default is False] Invert the x-axis. invert_yaxis [optional, default is False] Invert the y-axis. plotting_position : str [optional, default is 'weibull'] {plotting_position_table} Only used for norm_xaxis, norm_yaxis, lognorm_xaxis, lognorm_yaxis, weibull_xaxis, and weibull_yaxis. prob_plot_sort_values : str [optional, default is 'descending'] How to sort the values for the probability plots. Only used for norm_xaxis, norm_yaxis, lognorm_xaxis, lognorm_yaxis, weibull_xaxis, and weibull_yaxis. {columns} {start_date} {end_date} {clean} {skiprows} {index_type} {names} {source_units} {target_units} {round_index} plot_styles: str [optional, default is "default"] Set the style of the plot. One or more of Matplotlib styles "classic", "Solarize_Light2", "bmh", "dark_background", "fast", "fivethirtyeight", "ggplot", "grayscale", "seaborn", "seaborn-bright", "seaborn-colorblind", "seaborn-dark", "seaborn-dark-palette", "seaborn-darkgrid", "seaborn-deep", "seaborn-muted", "seaborn-notebook", "seaborn-paper", "seaborn-pastel", "seaborn-poster", "seaborn-talk", "seaborn-ticks", "seaborn-white", "seaborn-whitegrid", "tableau-colorblind10", and SciencePlots styles "science", "grid", "ieee", "scatter", "notebook", "high-vis", "bright", "vibrant", "muted", and "retro". If multiple styles then each over rides some or all of the characteristics of the previous. Color Blind Appropriate Styles The styles "seaborn-colorblind", "tableau-colorblind10", "bright", "vibrant", and "muted" are all styles that are setup to be able to be distinguished by someone with color blindness. Black, White, and Gray Styles The "ieee" style is appropriate for black, white, and gray, however the "ieee" also will change the chart size to fit in a column of the "IEEE" journal. The "grayscale" is another style useful for photo-copyable black, white, nd gray. Matplotlib styles: https://matplotlib.org/3.3.1/gallery/style_sheets/style_sheets_reference.html SciencePlots styles: https://github.com/garrettj403/SciencePlots hlines_y: [optional, defaults to None] Number or list of y values where to place a horizontal line. hlines_xmin: [optional, defaults to None] List of minimum x values to start the horizontal line. If a list must be same length as `hlines_y`. If a single number will be used as the minimum x values for all horizontal lines. A missing value or None will start at the minimum x value for the entire plot. hlines_xmax: [optional, defaults to None] List of maximum x values to end each horizontal line. If a list must be same length as `hlines_y`. If a single number will be the maximum x value for all horizontal lines. A missing value or None will end at the maximum x value for the entire plot. hlines_colors: [optional, defaults to None] List of colors for the horizontal lines. If a single color then will be used as the color for all horizontal lines. If a list must be same length as `hlines_y`. If None will take from the color pallette in the current plot style. hlines_linestyles: [optional, defaults to None] List of linestyles for the horizontal lines. If a single linestyle then will be used as the linestyle for all horizontal lines. If a list must be same length as `hlines_y`. If None will take for the standard linestyles list. vlines_x: [optional, defaults to None] List of x values where to place a vertical line. vlines_ymin: [optional, defaults to None] List of minimum y values to start the vertical line. If a list must be same length as `vlines_x`. If a single number will be used as the minimum x values for all vertical lines. A missing value or None will start at the minimum x value for the entire plot. vlines_ymax: [optional, defaults to None] List of maximum x values to end each vertical line. If a list must be same length as `vlines_x`. If a single number will be the maximum x value for all vertical lines. A missing value or None will end at the maximum x value for the entire plot. vlines_colors: [optional, defaults to None] List of colors for the vertical lines. If a single color then will be used as the color for all vertical lines. If a list must be same length as `vlines_x`. If None will take from the color pallette in the current plot style. vlines_linestyles: [optional, defaults to None] List of linestyles for the vertical lines. If a single linestyle then will be used as the linestyle for all vertical lines. If a list must be same length as `vlines_x`. If None will take for the standard linestyles list. """ plt = kde( input_ts=input_ts, columns=columns, start_date=start_date, end_date=end_date, clean=clean, skiprows=skiprows, index_type=index_type, names=names, ofilename=ofilename, xtitle=xtitle, ytitle=ytitle, title=title, figsize=figsize, legend=legend, legend_names=legend_names, subplots=subplots, sharex=sharex, sharey=sharey, colors=colors, linestyles=linestyles, markerstyles=markerstyles, bar_hatchstyles=bar_hatchstyles, style=style, logx=logx, logy=logy, xaxis=xaxis, yaxis=yaxis, xlim=xlim, ylim=ylim, secondary_y=secondary_y, mark_right=mark_right, scatter_matrix_diagonal=scatter_matrix_diagonal, bootstrap_size=bootstrap_size, bootstrap_samples=bootstrap_samples, norm_xaxis=norm_xaxis, norm_yaxis=norm_yaxis, lognorm_xaxis=lognorm_xaxis, lognorm_yaxis=lognorm_yaxis, xy_match_line=xy_match_line, grid=grid, label_rotation=label_rotation, label_skip=label_skip, force_freq=force_freq, drawstyle=drawstyle, por=por, invert_xaxis=invert_xaxis, invert_yaxis=invert_yaxis, round_index=round_index, plotting_position=plotting_position, prob_plot_sort_values=prob_plot_sort_values, source_units=source_units, target_units=target_units, lag_plot_lag=lag_plot_lag, plot_styles=plot_styles, hlines_y=hlines_y, hlines_xmin=hlines_xmin, hlines_xmax=hlines_xmax, hlines_colors=hlines_colors, hlines_linestyles=hlines_linestyles, vlines_x=vlines_x, vlines_ymin=vlines_ymin, vlines_ymax=vlines_ymax, vlines_colors=vlines_colors, vlines_linestyles=vlines_linestyles, ) # @tsutils.validator( # ofilename=[str, ["pass", []], 1], # type=[str, ["domain", ["kde",],], 1,], # lag_plot_lag=[int, ["range", [1, None]], 1], # xtitle=[str, ["pass", []], 1], # ytitle=[str, ["pass", []], 1], # title=[str, ["pass", []], 1], # figsize=[float, ["range", [0, None]], 2], # legend=[bool, ["domain", [True, False]], 1], # legend_names=[str, ["pass", []], 1], # subplots=[bool, ["domain", [True, False]], 1], # sharex=[bool, ["domain", [True, False]], 1], # sharey=[bool, ["domain", [True, False]], 1], # colors=[str, ["pass", []], None], # linestyles=[str, ["domain", ["auto", None, "", " ", " "] + plotutils.LINE_LIST], None], # markerstyles=[str, ["domain", ["auto", None, "", " ", " "] + plotutils.MARKER_LIST], None], # bar_hatchstyles=[str, ["domain", ["auto", None, "", " ", " "] + plotutils.HATCH_LIST], None], # style=[str, ["pass", []], None], # xlim=[float, ["pass", []], 2], # ylim=[float, ["pass", []], 2], # xaxis=[str, ["domain", ["arithmetic", "log"]], 1], # yaxis=[str, ["domain", ["arithmetic", "log"]], 1], # secondary_y=[bool, ["domain", [True, False]], 1], # mark_right=[bool, ["domain", [True, False]], 1], # scatter_matrix_diagonal=[str, ["domain", ["kde", "hist"]], 1], # bootstrap_size=[int, ["range", [0, None]], 1], # xy_match_line=[str, ["pass", []], 1], # grid=[bool, ["domain", [True, False]], 1], # label_rotation=[float, ["pass", []], 1], # label_skip=[int, ["range", [1, None]], 1], # drawstyle=[str, ["pass", []], 1], # por=[bool, ["domain", [True, False]], 1], # invert_xaxis=[bool, ["domain", [True, False]], 1], # invert_yaxis=[bool, ["domain", [True, False]], 1], # plotting_position=[ # str, # [ # "domain", # ["weibull", "benard", "tukey", "gumbel", "hazen", "cunnane", "california"], # ], # 1, # ], # prob_plot_sort_values=[str, ["domain", ["ascending", "descending"]], 1], # plot_styles=[ # str, # [ # "domain", # [ # "classic", # "Solarize_Light2", # "bmh", # "dark_background", # "fast", # "fivethirtyeight", # "ggplot", # "grayscale", # "seaborn", # "seaborn-bright", # "seaborn-colorblind", # "seaborn-dark", # "seaborn-dark-palette", # "seaborn-darkgrid", # "seaborn-deep", # "seaborn-muted", # "seaborn-notebook", # "seaborn-paper", # "seaborn-pastel", # "seaborn-poster", # "seaborn-talk", # "seaborn-ticks", # "seaborn-white", # "seaborn-whitegrid", # "tableau-colorblind10", # "science", # "grid", # "ieee", # "scatter", # "notebook", # "high-vis", # "bright", # "vibrant", # "muted", # "retro", # ], # ], # None, # ], # hlines_y=[float, ["pass", []], None], # hlines_xmin=[float, ["pass", []], None], # hlines_xmax=[float, ["pass", []], None], # hlines_colors=[str, ["pass", []], None], # hlines_linestyles=[ # str, # ["domain", ["auto", None, "", " ", " "] + plotutils.LINE_LIST], # None, # ], # vlines_x=[float, ["pass", []], None], # vlines_ymin=[float, ["pass", []], None], # vlines_ymax=[float, ["pass", []], None], # vlines_colors=[str, ["pass", []], None], # vlines_linestyles=[ # str, # ["domain", ["auto", None, "", " ", " "] + plotutils.LINE_LIST], # None, # ], # ) def kde( input_ts="-", columns=None, start_date=None, end_date=None, clean=False, skiprows=None, index_type="datetime", names=None, ofilename="plot.png", xtitle="", ytitle="", title="", figsize="10,6.0", legend=None, legend_names=None, subplots=False, sharex=True, sharey=False, colors="auto", linestyles="auto", markerstyles=" ", bar_hatchstyles="auto", style="auto", logx=False, logy=False, xaxis="arithmetic", yaxis="arithmetic", xlim=None, ylim=None, secondary_y=False, mark_right=True, scatter_matrix_diagonal="kde", bootstrap_size=50, bootstrap_samples=500, norm_xaxis=False, norm_yaxis=False, lognorm_xaxis=False, lognorm_yaxis=False, xy_match_line="", grid=False, label_rotation=None, label_skip=1, force_freq=None, drawstyle="default", por=False, invert_xaxis=False, invert_yaxis=False, round_index=None, plotting_position="weibull", prob_plot_sort_values="descending", source_units=None, target_units=None, lag_plot_lag=1, plot_styles="bright", hlines_y=None, hlines_xmin=None, hlines_xmax=None, hlines_colors=None, hlines_linestyles="-", vlines_x=None, vlines_ymin=None, vlines_ymax=None, vlines_colors=None, vlines_linestyles="-", **kwds, ): r"""Plot data.""" # Need to work around some old option defaults with the implementation of # mando legend = bool(legend == "" or legend == "True" or legend is None) type = "kde" import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt from matplotlib.ticker import FixedLocator tsd = tsutils.common_kwds( input_ts, skiprows=skiprows, names=names, index_type=index_type, start_date=start_date, end_date=end_date, pick=columns, round_index=round_index, dropna="all", source_units=source_units, target_units=target_units, clean=clean, por=por, ) tsd, lnames = plotutils.check(type, tsd, legend_names) # This is to help pretty print the frequency try: try: pltfreq = str(tsd.index.freq, "utf-8").lower() except TypeError: pltfreq = str(tsd.index.freq).lower() if pltfreq.split(" ")[0][1:] == "1": beginstr = 3 else: beginstr = 1 if pltfreq == "none": short_freq = "" else: # short freq string (day) OR (2 day) short_freq = "({})".format(pltfreq[beginstr:-1]) except AttributeError: short_freq = "" if colors == "auto": colors = None else: colors = tsutils.make_list(colors) if linestyles == "auto": linestyles = plotutils.LINE_LIST else: linestyles = tsutils.make_list(linestyles) if bar_hatchstyles == "auto": bar_hatchstyles = plotutils.HATCH_LIST else: bar_hatchstyles = tsutils.make_list(bar_hatchstyles) if markerstyles == "auto": markerstyles = plotutils.MARKER_LIST else: markerstyles = tsutils.make_list(markerstyles) if markerstyles is None: markerstyles = " " if style != "auto": nstyle = tsutils.make_list(style) if len(nstyle) != len(tsd.columns): raise ValueError( tsutils.error_wrapper( """ You have to have the same number of style strings as time-series to plot. You supplied '{}' for style which has {} style strings, but you have {} time-series. """.format( style, len(nstyle), len(tsd.columns) ) ) ) colors = [] markerstyles = [] linestyles = [] for st in nstyle: colors.append(st[0]) if len(st) == 1: markerstyles.append(" ") linestyles.append("-") continue if st[1] in plotutils.MARKER_LIST: markerstyles.append(st[1]) try: linestyles.append(st[2:]) except IndexError: linestyles.append(" ") else: markerstyles.append(" ") linestyles.append(st[1:]) if linestyles is None: linestyles = [" "] else: linestyles = [" " if i in [" ", None] else i for i in linestyles] markerstyles = [" " if i is None else i for i in markerstyles] if colors is not None: icolors = itertools.cycle(colors) else: icolors = None imarkerstyles = itertools.cycle(markerstyles) ilinestyles = itertools.cycle(linestyles) # Only for bar, barh, bar_stacked, and barh_stacked. ibar_hatchstyles = itertools.cycle(bar_hatchstyles) if ( logx is True or logy is True or norm_xaxis is True or norm_yaxis is True or lognorm_xaxis is True or lognorm_yaxis is True ): warnings.warn( """ * * The --logx, --logy, --norm_xaxis, --norm_yaxis, --lognorm_xaxis, and * --lognorm_yaxis options are deprecated. * * For --logx use --xaxis="log" * For --logy use --yaxis="log" * For --norm_xaxis use --type="norm_xaxis" * For --norm_yaxis use --type="norm_yaxis" * For --lognorm_xaxis use --type="lognorm_xaxis" * For --lognorm_yaxis use --type="lognorm_yaxis" * """ ) if xaxis == "log": logx = True if yaxis == "log": logy = True xlim = plotutils.know_your_limits(xlim, axis=xaxis) ylim = plotutils.know_your_limits(ylim, axis=yaxis) plot_styles = tsutils.make_list(plot_styles) + ["no-latex"] style_loc = os.path.join( os.path.dirname(__file__), os.pardir, "SciencePlots_styles" ) plot_styles = [ os.path.join(style_loc, i + ".mplstyle") if os.path.exists(os.path.join(style_loc, i + ".mplstyle")) else i for i in plot_styles ] plt.style.use(plot_styles) figsize = tsutils.make_list(figsize, n=2) _, ax = plt.subplots(figsize=figsize) if type in ["kde", "probability_density"]: ax = tsd.plot.kde( legend=legend, subplots=subplots, sharex=sharex, sharey=sharey, style=None, logx=logx, logy=logy, xlim=xlim, ylim=ylim, secondary_y=secondary_y, figsize=figsize, ) for index, line in enumerate(ax.lines): if icolors is not None: c = next(icolors) else: c = None if imarkerstyles is not None: m = next(imarkerstyles) else: m = None if ilinestyles is not None: l = next(ilinestyles) else: l = None if c is not None: plt.setp(line, color=c) plt.setp(line, marker=m) plt.setp(line, linestyle=l) ytitle = ytitle or "Density" if legend is True: plt.legend(loc="best") if hlines_y is not None: hlines_y = tsutils.make_list(hlines_y) hlines_xmin = tsutils.make_list(hlines_xmin) hlines_xmax = tsutils.make_list(hlines_xmax) hlines_colors = tsutils.make_list(hlines_colors) hlines_linestyles = tsutils.make_list(hlines_linestyles) nxlim = ax.get_xlim() if hlines_xmin is None: hlines_xmin = nxlim[0] if hlines_xmax is None: hlines_xmax = nxlim[1] if vlines_x is not None: vlines_x = tsutils.make_list(vlines_x) vlines_ymin = tsutils.make_list(vlines_ymin) vlines_ymax = tsutils.make_list(vlines_ymax) vlines_colors = tsutils.make_list(vlines_colors) vlines_linestyles = tsutils.make_list(vlines_linestyles) nylim = ax.get_ylim() if vlines_ymin is None: vlines_ymin = nylim[0] if vlines_ymax is None: vlines_ymax = nylim[1] if type in [ "time", "xy", "bar", "bar_stacked", "histogram", "norm_xaxis", "lognorm_xaxis", "weibull_xaxis", "norm_yaxis", "lognorm_yaxis", "weibull_yaxis", ]: if hlines_y is not None: if type in ["norm_yaxis", "lognorm_yaxis", "weibull_yaxis"]: hlines_y = ppf(tsutils.make_list(hlines_y)) plt.hlines( hlines_y, hlines_xmin, hlines_xmax, colors=hlines_colors, linestyles=hlines_linestyles, ) if vlines_x is not None: if type in ["norm_xaxis", "lognorm_xaxis", "weibull_xaxis"]: vlines_x = ppf(tsutils.make_list(vlines_x)) plt.vlines( vlines_x, vlines_ymin, vlines_ymax, colors=vlines_colors, linestyles=vlines_linestyles, ) plt.xlabel(xtitle) plt.ylabel(ytitle) if invert_xaxis is True: plt.gca().invert_xaxis() if invert_yaxis is True: plt.gca().invert_yaxis() plt.grid(grid) plt.title(title) plt.tight_layout() if ofilename is not None: plt.savefig(ofilename) return plt kde.__doc__ = kde_cli.__doc__

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null

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null

0

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0

7c241e9ea6651f1832b530bacf0b946a3f610e8c

2,255

py

Python

src/models/GNN.py

3verlyn/DL-abstract-argumentation

885e442077f5f8e576092c6648077e00ceb79dff

[ "MIT" ]

6

2020-05-01T10:04:16.000Z

2021-12-12T06:35:00.000Z

src/models/GNN.py

3verlyn/DL-abstract-argumentation

885e442077f5f8e576092c6648077e00ceb79dff

[ "MIT" ]

3

2020-05-01T09:58:16.000Z

2021-12-05T09:24:42.000Z

src/models/GNN.py

3verlyn/DL-abstract-argumentation

885e442077f5f8e576092c6648077e00ceb79dff

[ "MIT" ]

3

2021-12-01T12:09:40.000Z

2022-03-08T07:35:10.000Z

from collections import OrderedDict import torch import torch.nn as nn from torch_geometric.data.batch import Batch class GNN(nn.Module): def __init__(self, mp_steps, **config): super().__init__() self.mp_steps = mp_steps self.update_fns = self.assign_update_fns() self.readout_fns = self.assign_readout_fns() def assign_update_fns(self) -> OrderedDict: raise NotImplementedError def assign_readout_fns(self) -> dict: raise NotImplementedError def forward(self, batch: Batch, output_all_steps=True): edge_index = batch.edge_index sections = ( torch.bincount(batch.batch).tolist() if hasattr(batch, "batch") else None ) hiddens = self.initialize(batch) del batch # update attributes with update and aggregation step outputs = {element: [] for element in self.readout_fns.keys()} for step in range(self.mp_steps): hiddens = self.step(edge_index=edge_index, sections=sections, **hiddens) if not output_all_steps and (step + 1) != self.mp_steps: continue for element, readout_fn in self.readout_fns.items(): outputs[element].append(readout_fn(**hiddens)) return outputs def initialize(self, batch): hiddens = {} # initialize attributes trough embeddings and intialize lstm states to None for element in self.embeddings.keys(): embedding = self.embeddings[element](batch[f"{element}_input"]) hiddens.update( { f"{element}_input": embedding, f"{element}_embedding": embedding.clone(), f"{element}_lstm": None, } ) return hiddens def step(self, edge_index, sections, **hiddens): """ Perform a message passing step by propagating information and updating each element """ for element, update_fn in self.update_fns.items(): hiddens[f"{element}_embedding"], hiddens[f"{element}_lstm"] = update_fn( edge_index=edge_index, sections=sections, element=element, **hiddens ) return hiddens

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false

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0

0.297872

0

null

0

null

0

1

0

7c247e4df77036ee1f8b8a7c4396fc03bed606ad

977

py

Python

configs/baselines/DACN/GNN/GCN_res_layer.py

vivek-r-2000/BoundaryNet

fce8d51a516646c1001116d03872dbba9e4c5082

[ "MIT" ]

17

2021-06-07T12:30:23.000Z

2022-03-07T06:32:25.000Z

configs/baselines/DACN/GNN/GCN_res_layer.py

vivek-r-2000/BoundaryNet

fce8d51a516646c1001116d03872dbba9e4c5082

[ "MIT" ]

2

2021-07-13T13:24:14.000Z

2022-03-08T07:21:09.000Z

configs/baselines/DACN/GNN/GCN_res_layer.py

vivek-r-2000/BoundaryNet

fce8d51a516646c1001116d03872dbba9e4c5082

[ "MIT" ]

4

2021-06-26T15:12:44.000Z

2021-11-08T16:36:52.000Z

import math import torch import torch.nn as nn from torch.nn.modules.module import Module from GNN.GCN_layer import GraphConvolution class GraphResConvolution(Module): """ Simple GCN layer, similar to https://arxiv.org/abs/1609.02907 """ def __init__(self, state_dim, name=''): super(GraphResConvolution, self).__init__() self.state_dim = state_dim self.gcn_1 = GraphConvolution(state_dim, '%s_1' % name) self.gcn_2 = GraphConvolution(state_dim, '%s_2' % name) self.relu1 = nn.ReLU() self.relu2 = nn.ReLU() self.name = name def forward(self, input, adj): output_1 = self.gcn_1(input, adj) output_1_relu = self.relu1(output_1) output_2 = self.gcn_2(output_1_relu, adj) output_2_res = output_2 + input output = self.relu2(output_2_res) return output def __repr__(self): return self.__class__.__name__ + ' (' + self.name + ')'

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false

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0.478261

0

null

0

null

0

1

0

7c24dd7d64e797088cd127f5acf19696ee37ca0f

28,569

py

Python

mtools/util/logfile.py

lukasvosyka/mtools

b94620cef48a9eb71b6a7fa93ad88f70cd36982f

[ "Apache-2.0" ]

null

mtools/util/logfile.py

lukasvosyka/mtools

b94620cef48a9eb71b6a7fa93ad88f70cd36982f

[ "Apache-2.0" ]

null

mtools/util/logfile.py

lukasvosyka/mtools

b94620cef48a9eb71b6a7fa93ad88f70cd36982f

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 from __future__ import print_function import os import re import sys from datetime import datetime from math import ceil from mtools.util.input_source import InputSource from mtools.util.logevent import LogEvent class LogFile(InputSource): """Log file wrapper class. Handles open file streams or stdin.""" def __init__(self, filehandle): """Provide logfile as open file stream or stdin.""" self.filehandle = filehandle self.name = filehandle.name self.from_stdin = filehandle.name == "<stdin>" self._bounds_calculated = False self._start = None self._end = None self._filesize = None self._num_lines = None self._restarts = None self._binary = None self._timezone = None self._hostname = None self._port = None self._rs_state = None self._repl_set = None self._repl_set_members = None self._repl_set_version = None self._repl_set_protocol = None self._storage_engine = None self._datetime_format = None self._year_rollover = None self._shards = None self._csrs = None self._chunks_moved_from = None self._chunks_moved_to = None self._chunk_splits = None # Track previous file position for loop detection in _find_curr_line() self.prev_pos = None self._has_level = None # make sure bounds are calculated before starting to iterate, # including potential year rollovers self._calculate_bounds() @property def start(self): """ Lazy evaluation of start and end of logfile. Returns None for stdin input currently. """ if not self._start: self._calculate_bounds() return self._start @property def end(self): """ Lazy evaluation of start and end of logfile. Returns None for stdin input currently. """ if not self._end: self._calculate_bounds() return self._end @property def timezone(self): """Lazy evaluation of timezone of logfile.""" if not self._timezone: self._calculate_bounds() return self._timezone @property def filesize(self): """ Lazy evaluation of start and end of logfile. Returns None for stdin input currently. """ if self.from_stdin: return None if not self._filesize: self._calculate_bounds() return self._filesize @property def datetime_format(self): """Lazy evaluation of the datetime format.""" if not self._datetime_format: self._calculate_bounds() return self._datetime_format @property def has_level(self): """Lazy evaluation of the whether the logfile has any level lines.""" if self._has_level is None: self._iterate_lines() return self._has_level @property def year_rollover(self): """Lazy evaluation of the datetime format.""" if self._year_rollover is None: self._calculate_bounds() return self._year_rollover @property def num_lines(self): """ Lazy evaluation of the number of lines. Returns None for stdin input currently. """ if self.from_stdin: return None if not self._num_lines: self._iterate_lines() return self._num_lines @property def restarts(self): """Lazy evaluation of all restarts.""" if not self._num_lines: self._iterate_lines() return self._restarts @property def rs_state(self): """Lazy evaluation of all restarts.""" if not self._num_lines: self._iterate_lines() return self._rs_state @property def binary(self): """Lazy evaluation of the binary name.""" if not self._num_lines: self._iterate_lines() return self._binary @property def hostname(self): """Lazy evaluation of the binary name.""" if not self._num_lines: self._iterate_lines() return self._hostname @property def port(self): """Lazy evaluation of the binary name.""" if not self._num_lines: self._iterate_lines() return self._port @property def versions(self): """Return all version changes.""" versions = [] for v, _ in self.restarts: if len(versions) == 0 or v != versions[-1]: versions.append(v) return versions @property def repl_set(self): """Return the replSet (if available).""" if not self._num_lines: self._iterate_lines() return self._repl_set @property def repl_set_members(self): """Return the replSet (if available).""" if not self._num_lines: self._iterate_lines() return self._repl_set_members @property def repl_set_version(self): """Return the replSet (if available).""" if not self._num_lines: self._iterate_lines() return self._repl_set_version @property def repl_set_protocol(self): """Return the replSet protocolVersion (if available).""" if not self._num_lines: self._iterate_lines() return self._repl_set_protocol @property def storage_engine(self): """Return storage engine if available.""" if not self._num_lines: self._iterate_lines() return self._storage_engine @property def shards(self): """Lazily return the shards (if available)""" if not self._shards: self._find_sharding_info() return self._shards @property def csrs(self): """Lazily return the CSRS (if available)""" if not self._csrs: self._find_sharding_info() return self._csrs @property def chunks_moved_to(self): """Lazily return the chunks moved to this shard (if available)""" if not self._chunks_moved_to: self._find_sharding_info() return self._chunks_moved_to @property def chunks_moved_from(self): """Lazily return the chunks moved from this shard (if available)""" if not self._chunks_moved_from: self._find_sharding_info() return self._chunks_moved_from @property def chunk_splits(self): """Lazily return the chunks split in this shard (if available)""" if not self._chunk_splits: self._find_sharding_info() return self._chunk_splits def next(self): """Get next line, adjust for year rollover and hint datetime format.""" # use readline here because next() iterator uses internal readahead # buffer so seek position is wrong line = self.filehandle.readline() if isinstance(line, bytes): line = line.decode('utf-8', 'replace') if line == '': raise StopIteration line = line.rstrip('\n') le = LogEvent(line) # hint format and nextpos from previous line if self._datetime_format and self._datetime_nextpos is not None: ret = le.set_datetime_hint(self._datetime_format, self._datetime_nextpos, self.year_rollover) if not ret: # logevent indicates timestamp format has changed, # invalidate hint info self._datetime_format = None self._datetime_nextpos = None elif le.datetime: # gather new hint info from another logevent self._datetime_format = le.datetime_format self._datetime_nextpos = le._datetime_nextpos return le def __iter__(self): """ Iterate over LogFile object. Return a LogEvent object for each line (generator). """ le = None while True: try: le = self.next() except StopIteration as e: # end of log file, get end date if not self.end and self.from_stdin: if le and le.datetime: self._end = le.datetime # future iterations start from the beginning if not self.from_stdin: self.filehandle.seek(0) # return (instead of raising StopIteration exception) per PEP 479 return # get start date for stdin input if not self.start and self.from_stdin: if le and le.datetime: self._start = le.datetime try: yield le except StopIteration: return states = (['PRIMARY', 'SECONDARY', 'DOWN', 'STARTUP', 'STARTUP2', 'RECOVERING', 'ROLLBACK', 'ARBITER', 'UNKNOWN']) def __len__(self): """Return the number of lines in a log file.""" return self.num_lines def _iterate_lines(self): """Count number of lines (can be expensive).""" self._num_lines = 0 self._restarts = [] self._rs_state = [] ln = 0 for ln, line in enumerate(self.filehandle): if isinstance(line, bytes): line = line.decode("utf-8", "replace") if (self._has_level is None and line[28:31].strip() in LogEvent.log_levels and line[31:39].strip() in LogEvent.log_components): self._has_level = True # find version string (fast check to eliminate most lines) if "version" in line[:100]: logevent = LogEvent(line) restart = self._check_for_restart(logevent) if restart: self._restarts.append((restart, logevent)) if "starting :" in line or "starting:" in line: # look for hostname, port match = re.search('port=(?P<port>\d+).*host=(?P<host>\S+)', line) if match: self._hostname = match.group('host') self._port = match.group('port') """ For 3.0 the "[initandlisten] options:" long entry contained the "engine" field if WiredTiger was the storage engine. There were only two engines, MMAPv1 and WiredTiger """ if "[initandlisten] options:" in line: match = re.search('replSet: "(?P<replSet>\S+)"', line) if match: self._repl_set = match.group('replSet') match = re.search('engine: "(?P<engine>\S+)"', line) if match: self._storage_engine = match.group('engine') else: self._storage_engine = 'mmapv1' """ For 3.2 the "[initandlisten] options:" no longer contains the "engine" field So now we have to look for the "[initandlisten] wiredtiger_open config:" which was present in 3.0, but would now tell us definitively that wiredTiger is being used """ if "[initandlisten] wiredtiger_open config:" in line: self._storage_engine = 'wiredTiger' if "command admin.$cmd command: { replSetInitiate:" in line: match = re.search('{ _id: "(?P<replSet>\S+)", ' 'members: (?P<replSetMembers>[^]]+ ])', line) if match: self._repl_set = match.group('replSet') self._repl_set_members = match.group('replSetMembers') # Replica set config logging in MongoDB 3.0+ new_config = ("New replica set config in use: ") if new_config in line: match = re.search('{ _id: "(?P<replSet>\S+)", ' 'version: (?P<replSetVersion>\d+), ', line) if match: self._repl_set = match.group('replSet') self._repl_set_version = match.group('replSetVersion') match = re.search(', protocolVersion: (?P<replSetProtocol>\d+), ', line) if match: self._repl_set_protocol = match.group('replSetProtocol') match = re.search('members: (?P<replSetMembers>[^]]+ ])', line) if match: self._repl_set_members = match.group('replSetMembers') # if ("is now in state" in line and # next(state for state in states if line.endswith(state))): if "is now in state" in line: tokens = line.split() # 2.6 if tokens[1].endswith(']'): pos = 4 else: pos = 5 host = tokens[pos] rs_state = tokens[-1] state = (host, rs_state, LogEvent(line)) self._rs_state.append(state) continue if "[rsMgr] replSet" in line: tokens = line.split() if self._hostname: host = self._hostname + ':' + self._port else: host = os.path.basename(self.name) host += ' (self)' if tokens[-1] in self.states: rs_state = tokens[-1] else: # 2.6 if tokens[1].endswith(']'): pos = 2 else: pos = 6 rs_state = ' '.join(tokens[pos:]) state = (host, rs_state, LogEvent(line)) self._rs_state.append(state) continue self._num_lines = ln + 1 # reset logfile self.filehandle.seek(0) def _check_for_restart(self, logevent): if (logevent.thread == 'initandlisten' and "db version v" in logevent.line_str): self._binary = 'mongod' elif logevent.thread == 'mongosMain' and ('MongoS' in logevent.line_str or 'mongos' in logevent.line_str): self._binary = 'mongos' else: return False version = re.search(r'(\d\.\d\.\d+)', logevent.line_str) if version: version = version.group(1) return version else: return False def _calculate_bounds(self): """Calculate beginning and end of logfile.""" if self._bounds_calculated: # Assume no need to recalc bounds for lifetime of a Logfile object return if self.from_stdin: return False # we should be able to find a valid log line within max_start_lines max_start_lines = 10 lines_checked = 0 # get start datetime for line in self.filehandle: logevent = LogEvent(line) lines_checked += 1 if logevent.datetime: self._start = logevent.datetime self._timezone = logevent.datetime.tzinfo self._datetime_format = logevent.datetime_format self._datetime_nextpos = logevent._datetime_nextpos break if lines_checked > max_start_lines: break # sanity check before attempting to find end date if (self._start is None): raise SystemExit("Error: <%s> does not appear to be a supported " "MongoDB log file format" % self.filehandle.name) # get end datetime (lines are at most 10k, # go back 30k at most to make sure we catch one) self.filehandle.seek(0, 2) self._filesize = self.filehandle.tell() self.filehandle.seek(-min(self._filesize, 30000), 2) for line in reversed(self.filehandle.readlines()): logevent = LogEvent(line) if logevent.datetime: self._end = logevent.datetime break # if there was a roll-over, subtract 1 year from start time if self._end < self._start: self._start = self._start.replace(year=self._start.year - 1) self._year_rollover = self._end else: self._year_rollover = False # reset logfile self.filehandle.seek(0) self._bounds_calculated = True return True def _find_curr_line(self, prev=False): """ Internal helper function. Find the current (or previous if prev=True) line in a log file based on the current seek position. """ curr_pos = self.filehandle.tell() # jump back 15k characters (at most) and find last newline char jump_back = min(self.filehandle.tell(), 15000) self.filehandle.seek(-jump_back, 1) buff = self.filehandle.read(jump_back) self.filehandle.seek(curr_pos, 0) if prev and self.prev_pos is not None and self.prev_pos == curr_pos: # Number of characters to show before/after the log offset error_context = 300 self.filehandle.seek(-error_context, 1) buff = self.filehandle.read(curr_pos) hr = "-" * 60 print("Fatal log parsing loop detected trying to find previous " "log line near offset %s in %s:\n\n%s\n%s\n" "<--- (current log parsing offset) \n%s\n%s\n" % (curr_pos, self.name, hr, buff[:error_context], buff[error_context:error_context + 1], hr), file=sys.stderr) raise SystemExit("Cannot parse %s with requested options" % self.filehandle.name) else: self.prev_pos = curr_pos if isinstance(buff, bytes): buff = buff.decode("utf-8", "replace") newline_pos = buff.rfind('\n') if prev: newline_pos = buff[:newline_pos].rfind('\n') # move back to last newline char if newline_pos == -1: self.filehandle.seek(0) return self.next() self.filehandle.seek(newline_pos - jump_back + 1, 1) # roll forward until we found a line with a datetime try: logevent = self.next() while not logevent.datetime: logevent = self.next() return logevent except StopIteration: # reached end of file return None def _find_sharding_info(self): """ Iterate over file and find any sharding related information """ self._shards = [] self._chunks_moved_from = [] self._chunks_moved_to = [] self._chunk_splits = [] prev_line = "" for line in self.filehandle: if isinstance(line, bytes): line = line.decode("utf-8", "replace") if self.binary == "mongos": if "Starting new replica set monitor for" in line: if "[mongosMain]" in line: match = re.search("for (?P<csrsName>\w+)/" "(?P<replSetMembers>\S+)", line) if match: csrs_info = (match.group('csrsName'), match.group('replSetMembers')) self._csrs = csrs_info else: match = re.search("for (?P<shardName>\w+)/" "(?P<replSetMembers>\S+)", line) if match: shard_info = (match.group('shardName'), match.group('replSetMembers')) self._shards.append(shard_info) elif self.binary == "mongod": logevent = LogEvent(line) if "New replica set config in use" in line: if "configsvr: true" in line: match = re.search(' _id: "(?P<replSet>\S+)".*' 'members: (?P<replSetMembers>[^]]+ ])', line) if match: self._csrs = ( match.group('replSet'), match.group('replSetMembers') ) if "Starting new replica set monitor for" in line: match = re.search("for (?P<replSet>\w+)/" "(?P<replSetMembers>\S+)", line) if match: if self._csrs and match.group('replSet') != self._csrs[0]: self._shards.append(( match.group('replSet'), match.group('replSetMembers') )) elif not self._csrs: self._csrs = ( match.group('replSet'), match.group('replSetMembers') ) if "moveChunk.from" in line: logevent = LogEvent(line) match = re.search('ns: "(?P<namespace>\S+)".*' 'details: { (?P<range>.*\}).*' 'to: "(?P<movedTo>\S+)".*note: "(?P<note>\S+)"', line) if match: time = logevent.datetime chunk_range = match.group('range') namespace = match.group('namespace') moved_to = match.group('movedTo') note = match.group('note') if note == "success": errmsg = None steps = re.findall('(?P<steps>step \d of \d): (?P<stepTimes>\d+)', line) else: match = re.search(':: caused by :: (?P<errmsg>\S+):', prev_line) steps = None if match: errmsg = match.group('errmsg') else: errmsg = "Unknown" chunk_migration = (time, chunk_range, moved_to, namespace, steps, note, errmsg) self._chunks_moved_from.append(chunk_migration) if "moveChunk.to" in line: logevent = LogEvent(line) match = re.search('ns: "(?P<namespace>\S+)".*' 'details: { (?P<range>.*\}).*.*note: "(?P<note>\S+)"', line) if match: time = logevent.datetime chunk_range = match.group('range') namespace = match.group('namespace') # TODO: alter this to find moved from shard name when SERVER-45770 TICKET is added moved_from = "Unknown" note = match.group('note') if note == "success": errmsg = None steps = re.findall('(?P<steps>step \d of \d): (?P<stepTimes>\d+)', line) else: steps = None match = re.search('errmsg: "(?P<errmsg>.*)"', line) if match: errmsg = match.group('errmsg') chunk_migration = (time, chunk_range, moved_from, namespace, steps, note, errmsg) self._chunks_moved_to.append(chunk_migration) if "Finding the split vector for" in line: logevent = LogEvent(line) match = re.search('for (?P<namespace>\S+).*' 'numSplits: (?P<numSplits>\d+)', line) if match: time = logevent.datetime split_range = None namespace = match.group("namespace") numSplits = match.group('numSplits') success = None time_taken = 0 error = None self._chunk_splits.append((time, split_range, namespace, numSplits, success, time_taken, error)) elif "splitVector" in line: logevent = LogEvent(line) match = re.search('splitVector: "(?P<namespace>\S+)".*,' ' (?P<range>min:.*), max.*op_msg (?P<time_taken>\d+)', line) if match: time = logevent.datetime split_range = match.group("range") namespace = match.group("namespace") time_taken = match.group("time_taken") numSplits = 0 success = True error = None self._chunk_splits.append((time, split_range, namespace, numSplits, success, time_taken, error)) elif "Unable to auto-split chunk" in line: logevent = LogEvent(line) match = re.search("chunk \[(?P<range>.*)\) " 'in namespace (?P<namespace>\S+)' ' :: caused by :: (?P<error>\S+): ', line) if match: time = logevent.datetime split_range = match.group("range") namespace = match.group("namespace") numSplits = 0 success = False time_taken = 0 error = match.group("error") self._chunk_splits.append((time, split_range, namespace, numSplits, success, time_taken, error)) elif "jumbo" in line: logevent = LogEvent(line) match = re.search('migration (?P<namespace>\S+): \[(?P<range>.*)\)', prev_line) if match: time = logevent.datetime split_range = match.group("range") namespace = match.group("namespace") numSplits = 0 success = False time_taken = 0 error = "Jumbo" self._chunk_splits.append((time, split_range, namespace, numSplits, success, time_taken, error)) prev_line = line # reset logfile self.filehandle.seek(0) def fast_forward(self, start_dt): """ Fast-forward file to given start_dt datetime obj using binary search. Only fast for files. Streams need to be forwarded manually, and it will miss the first line that would otherwise match (as it consumes the log line). """ if self.from_stdin: # skip lines until start_dt is reached return else: # fast bisection path max_mark = self.filesize step_size = max_mark # check if start_dt is already smaller than first datetime self.filehandle.seek(0) le = self.next() if le.datetime and le.datetime >= start_dt: self.filehandle.seek(0) return le = None self.filehandle.seek(0) # search for lower bound while abs(step_size) > 100: step_size = ceil(step_size / 2.) self.filehandle.seek(step_size, 1) le = self._find_curr_line() if not le: break if le.datetime >= start_dt: step_size = -abs(step_size) else: step_size = abs(step_size) if not le: return # now walk backwards until we found a truly smaller line while self.filehandle.tell() >= 2 and (le.datetime is None or le.datetime >= start_dt): self.filehandle.seek(-2, 1) le = self._find_curr_line(prev=True)

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null

0

null

0

1

0

7c26833e5360e6495c23a5b485ec7547b6bafa06

2,136

py

Python

tests/svg.py

Tillsten/pyqtgraph

0045863165fe526988c58cf4f8232ae2d261a5ee

[ "MIT" ]

null

tests/svg.py

Tillsten/pyqtgraph

0045863165fe526988c58cf4f8232ae2d261a5ee

[ "MIT" ]

null

tests/svg.py

Tillsten/pyqtgraph

0045863165fe526988c58cf4f8232ae2d261a5ee

[ "MIT" ]

null

""" SVG export test """ import test import pyqtgraph as pg app = pg.mkQApp() class SVGTest(test.TestCase): #def test_plotscene(self): #pg.setConfigOption('foreground', (0,0,0)) #w = pg.GraphicsWindow() #w.show() #p1 = w.addPlot() #p2 = w.addPlot() #p1.plot([1,3,2,3,1,6,9,8,4,2,3,5,3], pen={'color':'k'}) #p1.setXRange(0,5) #p2.plot([1,5,2,3,4,6,1,2,4,2,3,5,3], pen={'color':'k', 'cosmetic':False, 'width': 0.3}) #app.processEvents() #app.processEvents() #ex = pg.exporters.SVGExporter.SVGExporter(w.scene()) #ex.export(fileName='test.svg') def test_simple(self): scene = pg.QtGui.QGraphicsScene() #rect = pg.QtGui.QGraphicsRectItem(0, 0, 100, 100) #scene.addItem(rect) #rect.setPos(20,20) #rect.translate(50, 50) #rect.rotate(30) #rect.scale(0.5, 0.5) #rect1 = pg.QtGui.QGraphicsRectItem(0, 0, 100, 100) #rect1.setParentItem(rect) #rect1.setFlag(rect1.ItemIgnoresTransformations) #rect1.setPos(20, 20) #rect1.scale(2,2) #el1 = pg.QtGui.QGraphicsEllipseItem(0, 0, 100, 100) #el1.setParentItem(rect1) ##grp = pg.ItemGroup() #grp.setParentItem(rect) #grp.translate(200,0) ##grp.rotate(30) #rect2 = pg.QtGui.QGraphicsRectItem(0, 0, 100, 25) #rect2.setFlag(rect2.ItemClipsChildrenToShape) #rect2.setParentItem(grp) #rect2.setPos(0,25) #rect2.rotate(30) #el = pg.QtGui.QGraphicsEllipseItem(0, 0, 100, 50) #el.translate(10,-5) #el.scale(0.5,2) #el.setParentItem(rect2) grp2 = pg.ItemGroup() scene.addItem(grp2) grp2.scale(100,100) rect3 = pg.QtGui.QGraphicsRectItem(0,0,2,2) rect3.setPen(pg.mkPen(width=1, cosmetic=False)) grp2.addItem(rect3) ex = pg.exporters.SVGExporter.SVGExporter(scene) ex.export(fileName='test.svg') if __name__ == '__main__': test.unittest.main()

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0

0.25

0

null

0

null

0

1

0

7c26b3633189c7cbd7b00d1addad30f94587f9ec

993

py

Python

src/api/models/enums/apschedulerevents.py

jedicontributors/pythondataintegrator

3e877b367ab9b20185476128ec053db41087879f

[ "MIT" ]

14

2020-12-19T15:06:13.000Z

2022-01-12T19:52:17.000Z

src/api/models/enums/apschedulerevents.py

jedicontributors/pythondataintegrator

3e877b367ab9b20185476128ec053db41087879f

[ "MIT" ]

43

2021-01-06T22:05:22.000Z

2022-03-10T10:30:30.000Z

src/api/models/enums/apschedulerevents.py

jedicontributors/pythondataintegrator

3e877b367ab9b20185476128ec053db41087879f

[ "MIT" ]

4

2020-12-18T23:10:09.000Z

2021-04-02T13:03:12.000Z

EVENT_SCHEDULER_STARTED = EVENT_SCHEDULER_START = 2 ** 0 EVENT_SCHEDULER_SHUTDOWN = 2 ** 1 EVENT_SCHEDULER_PAUSED = 2 ** 2 EVENT_SCHEDULER_RESUMED = 2 ** 3 EVENT_EXECUTOR_ADDED = 2 ** 4 EVENT_EXECUTOR_REMOVED = 2 ** 5 EVENT_JOBSTORE_ADDED = 2 ** 6 EVENT_JOBSTORE_REMOVED = 2 ** 7 EVENT_ALL_JOBS_REMOVED = 2 ** 8 EVENT_JOB_ADDED = 2 ** 9 EVENT_JOB_REMOVED = 2 ** 10 EVENT_JOB_MODIFIED = 2 ** 11 EVENT_JOB_EXECUTED = 2 ** 12 EVENT_JOB_ERROR = 2 ** 13 EVENT_JOB_MISSED = 2 ** 14 EVENT_JOB_SUBMITTED = 2 ** 15 EVENT_JOB_MAX_INSTANCES = 2 ** 16 EVENT_ALL = (EVENT_SCHEDULER_STARTED | EVENT_SCHEDULER_SHUTDOWN | EVENT_SCHEDULER_PAUSED | EVENT_SCHEDULER_RESUMED | EVENT_EXECUTOR_ADDED | EVENT_EXECUTOR_REMOVED | EVENT_JOBSTORE_ADDED | EVENT_JOBSTORE_REMOVED | EVENT_ALL_JOBS_REMOVED | EVENT_JOB_ADDED | EVENT_JOB_REMOVED | EVENT_JOB_MODIFIED | EVENT_JOB_EXECUTED | EVENT_JOB_ERROR | EVENT_JOB_MISSED | EVENT_JOB_SUBMITTED | EVENT_JOB_MAX_INSTANCES)

45.136364

96

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993

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993

22

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45.136364

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0

1

0

false

0

null

0

null

0

1

0

7c272bc2beff83ce709b4ecff735eaf333a85378

25,166

py

Python

scripts/build/build/targets.py

mrninhvn/matter

c577b233db9d2f3a6f87108a062b1699a40c5169

[ "Apache-2.0" ]

2

2022-03-29T12:17:41.000Z

2022-03-30T13:25:20.000Z

scripts/build/build/targets.py

mrninhvn/matter

c577b233db9d2f3a6f87108a062b1699a40c5169

[ "Apache-2.0" ]

null

scripts/build/build/targets.py

mrninhvn/matter

c577b233db9d2f3a6f87108a062b1699a40c5169

[ "Apache-2.0" ]

2

2022-02-24T15:42:39.000Z

2022-03-04T20:38:07.000Z

# Copyright (c) 2021 Project CHIP Authors # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os from itertools import combinations from typing import List from builders.ameba import AmebaApp, AmebaBoard, AmebaBuilder from builders.android import AndroidApp, AndroidBoard, AndroidBuilder from builders.cc13x2x7_26x2x7 import cc13x2x7_26x2x7App, cc13x2x7_26x2x7Builder from builders.cyw30739 import Cyw30739App, Cyw30739Board, Cyw30739Builder from builders.efr32 import Efr32App, Efr32Board, Efr32Builder from builders.esp32 import Esp32App, Esp32Board, Esp32Builder from builders.host import HostApp, HostBoard, HostBuilder from builders.infineon import InfineonApp, InfineonBoard, InfineonBuilder from builders.k32w import K32WApp, K32WBuilder from builders.mbed import MbedApp, MbedBoard, MbedBuilder, MbedProfile from builders.nrf import NrfApp, NrfBoard, NrfConnectBuilder from builders.qpg import QpgApp, QpgBoard, QpgBuilder from builders.telink import TelinkApp, TelinkBoard, TelinkBuilder from builders.tizen import TizenApp, TizenBoard, TizenBuilder from builders.bl602 import Bl602App, Bl602Board, Bl602Builder from builders.imx import IMXApp, IMXBuilder class Target: """Represents a build target: Has a name identifier plus parameters on how to build it (what builder class to use and what arguments are required to produce the specified build) """ def __init__(self, name, builder_class, **kwargs): self.name = name self.builder_class = builder_class self.glob_blacklist_reason = None self.create_kw_args = kwargs def Clone(self): """Creates a clone of self.""" clone = Target(self.name, self.builder_class, **self.create_kw_args.copy()) clone.glob_blacklist_reason = self.glob_blacklist_reason return clone def Extend(self, suffix, **kargs): """Creates a clone of the current object extending its build parameters. Arguments: suffix: appended with a "-" as separator to the clone name **kargs: arguments needed to produce the new build variant """ clone = self.Clone() clone.name += "-" + suffix clone.create_kw_args.update(kargs) return clone def Create(self, runner, repository_path: str, output_prefix: str, enable_flashbundle: bool): builder = self.builder_class( repository_path, runner=runner, **self.create_kw_args) builder.target = self builder.identifier = self.name builder.output_dir = os.path.join(output_prefix, self.name) builder.enable_flashbundle(enable_flashbundle) return builder def GlobBlacklist(self, reason): clone = self.Clone() if clone.glob_blacklist_reason: clone.glob_blacklist_reason += ", " clone.glob_blacklist_reason += reason else: clone.glob_blacklist_reason = reason return clone @property def IsGlobBlacklisted(self): return self.glob_blacklist_reason is not None @property def GlobBlacklistReason(self): return self.glob_blacklist_reason class AcceptAnyName: def Accept(self, name: str): return True class AcceptNameWithSubstrings: def __init__(self, substr: List[str]): self.substr = substr def Accept(self, name: str): for s in self.substr: if s in name: return True return False class BuildVariant: def __init__(self, name: str, validator=AcceptAnyName(), conflicts: List[str] = [], requires: List[str] = [], **buildargs): self.name = name self.validator = validator self.conflicts = conflicts self.buildargs = buildargs self.requires = requires def HasConflicts(items: List[BuildVariant]) -> bool: for a, b in combinations(items, 2): if (a.name in b.conflicts) or (b.name in a.conflicts): return True return False def AllRequirementsMet(items: List[BuildVariant]) -> bool: """ Check that item.requires is satisfied for all items in the given list """ available = set([item.name for item in items]) for item in items: for requirement in item.requires: if requirement not in available: return False return True class VariantBuilder: """Handles creating multiple build variants based on a starting target. """ def __init__(self, targets: List[Target] = []): # note the clone in case the default arg is used self.targets = targets[:] self.variants = [] self.glob_whitelist = [] def WhitelistVariantNameForGlob(self, name): """ Whitelist the specified variant to be allowed for globbing. By default we do not want a 'build all' to select all variants, so variants are generally glob-blacklisted. """ self.glob_whitelist.append(name) def AppendVariant(self, **args): """ Add another variant to accepted variants. Arguments are construction variants to BuildVariant. Example usage: builder.AppendVariant(name="ipv6only", enable_ipv4=False) """ self.variants.append(BuildVariant(**args)) def AllVariants(self): """ Yields a list of acceptable variants for the given targets. Handles conflict resolution between build variants and globbing whitelist targets. """ for target in self.targets: yield target # skip variants that do not work for this target ok_variants = [ v for v in self.variants if v.validator.Accept(target.name)] # Build every possible variant for variant_count in range(1, len(ok_variants) + 1): for subgroup in combinations(ok_variants, variant_count): if HasConflicts(subgroup): continue if not AllRequirementsMet(subgroup): continue # Target ready to be created - no conflicts variant_target = target.Clone() for option in subgroup: variant_target = variant_target.Extend( option.name, **option.buildargs) # Only a few are whitelisted for globs name = '-'.join([o.name for o in subgroup]) if name not in self.glob_whitelist: if not variant_target.IsGlobBlacklisted: variant_target = variant_target.GlobBlacklist( 'Reduce default build variants') yield variant_target def HostTargets(): target = Target(HostBoard.NATIVE.PlatformName(), HostBuilder) target_native = target.Extend(HostBoard.NATIVE.BoardName(), board=HostBoard.NATIVE) targets = [target_native] # x64 linux supports cross compile cross_compile = (HostBoard.NATIVE.PlatformName() == 'linux') and (HostBoard.NATIVE.BoardName() != HostBoard.ARM64.BoardName()) if cross_compile: targets.append(target.Extend('arm64', board=HostBoard.ARM64)) app_targets = [] # Don't cross compile some builds app_targets.append( target_native.Extend('rpc-console', app=HostApp.RPC_CONSOLE)) app_targets.append( target_native.Extend('tv-app', app=HostApp.TV_APP)) app_targets.append( target_native.Extend('tv-casting-app', app=HostApp.TV_CASTING_APP)) app_targets.append( target_native.Extend('nl-test-runner', app=HostApp.NL_TEST_RUNNER)) for target in targets: app_targets.append(target.Extend( 'all-clusters', app=HostApp.ALL_CLUSTERS)) if (HostBoard.NATIVE.PlatformName() == 'darwin'): app_targets.append(target.Extend( 'chip-tool-darwin', app=HostApp.CHIP_TOOL_DARWIN)) app_targets.append(target.Extend('chip-tool', app=HostApp.CHIP_TOOL)) app_targets.append(target.Extend('thermostat', app=HostApp.THERMOSTAT)) app_targets.append(target.Extend('minmdns', app=HostApp.MIN_MDNS)) app_targets.append(target.Extend('light', app=HostApp.LIGHT)) app_targets.append(target.Extend('lock', app=HostApp.LOCK)) app_targets.append(target.Extend('shell', app=HostApp.SHELL)) app_targets.append(target.Extend( 'ota-provider', app=HostApp.OTA_PROVIDER, enable_ble=False)) app_targets.append(target.Extend( 'ota-requestor', app=HostApp.OTA_REQUESTOR, enable_ble=False)) app_targets.append(target.Extend('python-bindings', app=HostApp.PYTHON_BINDINGS)) builder = VariantBuilder() # Possible build variants. Note that number of potential # builds is exponential here builder.AppendVariant(name="same-event-loop", validator=AcceptNameWithSubstrings( ['-chip-tool', '-chip-tool-darwin']), separate_event_loop=False), builder.AppendVariant(name="no-interactive", validator=AcceptNameWithSubstrings( ['-chip-tool']), interactive_mode=False), builder.AppendVariant(name="ipv6only", enable_ipv4=False), builder.AppendVariant(name="no-ble", enable_ble=False), builder.AppendVariant(name="no-wifi", enable_wifi=False), builder.AppendVariant(name="tsan", conflicts=['asan'], use_tsan=True), builder.AppendVariant(name="asan", conflicts=['tsan'], use_asan=True), builder.AppendVariant(name="libfuzzer", requires=[ "clang"], use_libfuzzer=True), builder.AppendVariant(name="clang", use_clang=True), builder.AppendVariant(name="test", extra_tests=True), builder.WhitelistVariantNameForGlob('no-interactive-ipv6only') builder.WhitelistVariantNameForGlob('ipv6only') for target in app_targets: if ('-rpc-console' in target.name) or ('-python-bindings' in target.name) or ('nl-test-runner' in target.name): # Single-variant builds yield target else: builder.targets.append(target) for target in builder.AllVariants(): if cross_compile and 'chip-tool' in target.name and 'arm64' in target.name and '-no-interactive' not in target.name: # Interactive builds will not compile by default on arm cross compiles # because libreadline is not part of the default sysroot yield target.GlobBlacklist('Arm crosscompile does not support libreadline-dev') else: yield target # Without extra build variants yield target_native.Extend('chip-cert', app=HostApp.CERT_TOOL) yield target_native.Extend('address-resolve-tool', app=HostApp.ADDRESS_RESOLVE) yield target_native.Extend('address-resolve-tool-clang', app=HostApp.ADDRESS_RESOLVE, use_clang=True).GlobBlacklist("Reduce default build variants") yield target_native.Extend('address-resolve-tool-platform-mdns', app=HostApp.ADDRESS_RESOLVE, use_platform_mdns=True).GlobBlacklist("Reduce default build variants") yield target_native.Extend('address-resolve-tool-platform-mdns-ipv6only', app=HostApp.ADDRESS_RESOLVE, use_platform_mdns=True, enable_ipv4=False).GlobBlacklist("Reduce default build variants") test_target = Target(HostBoard.NATIVE.PlatformName(), HostBuilder) for board in [HostBoard.NATIVE, HostBoard.FAKE]: yield test_target.Extend(board.BoardName() + '-tests', board=board, app=HostApp.TESTS) def Esp32Targets(): esp32_target = Target('esp32', Esp32Builder) yield esp32_target.Extend('m5stack-all-clusters', board=Esp32Board.M5Stack, app=Esp32App.ALL_CLUSTERS) yield esp32_target.Extend('m5stack-all-clusters-ipv6only', board=Esp32Board.M5Stack, app=Esp32App.ALL_CLUSTERS, enable_ipv4=False) yield esp32_target.Extend('m5stack-all-clusters-rpc', board=Esp32Board.M5Stack, app=Esp32App.ALL_CLUSTERS, enable_rpcs=True) yield esp32_target.Extend('m5stack-all-clusters-rpc-ipv6only', board=Esp32Board.M5Stack, app=Esp32App.ALL_CLUSTERS, enable_rpcs=True, enable_ipv4=False) yield esp32_target.Extend('c3devkit-all-clusters', board=Esp32Board.C3DevKit, app=Esp32App.ALL_CLUSTERS) devkitc = esp32_target.Extend('devkitc', board=Esp32Board.DevKitC) yield devkitc.Extend('all-clusters', app=Esp32App.ALL_CLUSTERS) yield devkitc.Extend('all-clusters-ipv6only', app=Esp32App.ALL_CLUSTERS, enable_ipv4=False) yield devkitc.Extend('shell', app=Esp32App.SHELL) yield devkitc.Extend('light', app=Esp32App.LIGHT) yield devkitc.Extend('lock', app=Esp32App.LOCK) yield devkitc.Extend('bridge', app=Esp32App.BRIDGE) yield devkitc.Extend('temperature-measurement', app=Esp32App.TEMPERATURE_MEASUREMENT) yield devkitc.Extend('temperature-measurement-rpc', app=Esp32App.TEMPERATURE_MEASUREMENT, enable_rpcs=True) yield esp32_target.Extend('qemu-tests', board=Esp32Board.QEMU, app=Esp32App.TESTS) def Efr32Targets(): efr_target = Target('efr32', Efr32Builder) board_targets = [ efr_target.Extend('brd4161a', board=Efr32Board.BRD4161A), efr_target.Extend('brd4163a', board=Efr32Board.BRD4163A).GlobBlacklist( 'only user requested'), efr_target.Extend('brd4164a', board=Efr32Board.BRD4164A).GlobBlacklist( 'only user requested'), efr_target.Extend('brd4166a', board=Efr32Board.BRD4166A).GlobBlacklist( 'only user requested'), efr_target.Extend('brd4170a', board=Efr32Board.BRD4170A).GlobBlacklist( 'only user requested'), efr_target.Extend('brd4186a', board=Efr32Board.BRD4186A).GlobBlacklist( 'only user requested'), efr_target.Extend('brd4187a', board=Efr32Board.BRD4187A).GlobBlacklist( 'only user requested'), efr_target.Extend('brd4304a', board=Efr32Board.BRD4304A).GlobBlacklist( 'only user requested') ] builder = VariantBuilder() for board_target in board_targets: builder.targets.append(board_target.Extend( 'window-covering', app=Efr32App.WINDOW_COVERING)) builder.targets.append(board_target.Extend( 'switch', app=Efr32App.SWITCH)) builder.targets.append(board_target.Extend( 'unit-test', app=Efr32App.UNIT_TEST)) builder.targets.append( board_target.Extend('light', app=Efr32App.LIGHT)) builder.targets.append(board_target.Extend('lock', app=Efr32App.LOCK)) # Possible build variants. Note that number of potential # builds is exponential here builder.AppendVariant(name="rpc", validator=AcceptNameWithSubstrings( ['-light', '-lock']), enable_rpcs=True) builder.AppendVariant(name="with-ota-requestor", enable_ota_requestor=True) builder.WhitelistVariantNameForGlob('rpc') for target in builder.AllVariants(): yield target def NrfTargets(): target = Target('nrf', NrfConnectBuilder) yield target.Extend('native-posix-64-tests', board=NrfBoard.NATIVE_POSIX_64, app=NrfApp.UNIT_TESTS) targets = [ target.Extend('nrf5340dk', board=NrfBoard.NRF5340DK), target.Extend('nrf52840dk', board=NrfBoard.NRF52840DK), ] # Enable nrf52840dongle for all-clusters and lighting app only yield target.Extend('nrf52840dongle-all-clusters', board=NrfBoard.NRF52840DONGLE, app=NrfApp.ALL_CLUSTERS) yield target.Extend('nrf52840dongle-light', board=NrfBoard.NRF52840DONGLE, app=NrfApp.LIGHT) for target in targets: yield target.Extend('all-clusters', app=NrfApp.ALL_CLUSTERS) yield target.Extend('lock', app=NrfApp.LOCK) yield target.Extend('light', app=NrfApp.LIGHT) yield target.Extend('shell', app=NrfApp.SHELL) yield target.Extend('pump', app=NrfApp.PUMP) yield target.Extend('pump-controller', app=NrfApp.PUMP_CONTROLLER) rpc = target.Extend('light-rpc', app=NrfApp.LIGHT, enable_rpcs=True) if '-nrf5340dk-' in rpc.name: rpc = rpc.GlobBlacklist( 'Compile failure due to pw_build args not forwarded to proto compiler. ' 'https://pigweed-review.googlesource.com/c/pigweed/pigweed/+/66760') yield rpc def AndroidTargets(): target = Target('android', AndroidBuilder) yield target.Extend('arm-chip-tool', board=AndroidBoard.ARM, app=AndroidApp.CHIP_TOOL) yield target.Extend('arm64-chip-tool', board=AndroidBoard.ARM64, app=AndroidApp.CHIP_TOOL) yield target.Extend('x64-chip-tool', board=AndroidBoard.X64, app=AndroidApp.CHIP_TOOL) yield target.Extend('x86-chip-tool', board=AndroidBoard.X86, app=AndroidApp.CHIP_TOOL) yield target.Extend('arm64-chip-test', board=AndroidBoard.ARM64, app=AndroidApp.CHIP_TEST) yield target.Extend('androidstudio-arm-chip-tool', board=AndroidBoard.AndroidStudio_ARM, app=AndroidApp.CHIP_TOOL) yield target.Extend('androidstudio-arm64-chip-tool', board=AndroidBoard.AndroidStudio_ARM64, app=AndroidApp.CHIP_TOOL) yield target.Extend('androidstudio-x86-chip-tool', board=AndroidBoard.AndroidStudio_X86, app=AndroidApp.CHIP_TOOL) yield target.Extend('androidstudio-x64-chip-tool', board=AndroidBoard.AndroidStudio_X64, app=AndroidApp.CHIP_TOOL) yield target.Extend('arm64-chip-tvserver', board=AndroidBoard.ARM64, app=AndroidApp.CHIP_TVServer) yield target.Extend('arm-chip-tvserver', board=AndroidBoard.ARM, app=AndroidApp.CHIP_TVServer) yield target.Extend('x86-chip-tvserver', board=AndroidBoard.X86, app=AndroidApp.CHIP_TVServer) yield target.Extend('x64-chip-tvserver', board=AndroidBoard.X64, app=AndroidApp.CHIP_TVServer) yield target.Extend('arm64-chip-tv-casting-app', board=AndroidBoard.ARM64, app=AndroidApp.CHIP_TV_CASTING_APP) yield target.Extend('arm-chip-tv-casting-app', board=AndroidBoard.ARM, app=AndroidApp.CHIP_TV_CASTING_APP) def MbedTargets(): target = Target('mbed', MbedBuilder) targets = [ target.Extend('CY8CPROTO_062_4343W', board=MbedBoard.CY8CPROTO_062_4343W), ] app_targets = [] for target in targets: app_targets.append(target.Extend('lock', app=MbedApp.LOCK)) app_targets.append(target.Extend('light', app=MbedApp.LIGHT)) app_targets.append(target.Extend( 'all-clusters', app=MbedApp.ALL_CLUSTERS)) app_targets.append(target.Extend('pigweed', app=MbedApp.PIGWEED)) app_targets.append(target.Extend('shell', app=MbedApp.SHELL)) for target in app_targets: yield target.Extend('release', profile=MbedProfile.RELEASE) yield target.Extend('develop', profile=MbedProfile.DEVELOP).GlobBlacklist( 'Compile only for debugging purpose - ' 'https://os.mbed.com/docs/mbed-os/latest/program-setup/build-profiles-and-rules.html') yield target.Extend('debug', profile=MbedProfile.DEBUG).GlobBlacklist( 'Compile only for debugging purpose - ' 'https://os.mbed.com/docs/mbed-os/latest/program-setup/build-profiles-and-rules.html') def InfineonTargets(): target = Target('infineon', InfineonBuilder) yield target.Extend('p6-lock', board=InfineonBoard.P6BOARD, app=InfineonApp.LOCK) yield target.Extend('p6-all-clusters', board=InfineonBoard.P6BOARD, app=InfineonApp.ALL_CLUSTERS) yield target.Extend('p6-light', board=InfineonBoard.P6BOARD, app=InfineonApp.LIGHT) def AmebaTargets(): ameba_target = Target('ameba', AmebaBuilder) yield ameba_target.Extend('amebad-all-clusters', board=AmebaBoard.AMEBAD, app=AmebaApp.ALL_CLUSTERS) yield ameba_target.Extend('amebad-light', board=AmebaBoard.AMEBAD, app=AmebaApp.LIGHT) yield ameba_target.Extend('amebad-pigweed', board=AmebaBoard.AMEBAD, app=AmebaApp.PIGWEED) def K32WTargets(): target = Target('k32w', K32WBuilder) yield target.Extend('light-ota-se', app=K32WApp.LIGHT, release=True, disable_ble=True, se05x=True).GlobBlacklist("Only on demand build") yield target.Extend('light-release-no-ota', app=K32WApp.LIGHT, tokenizer=True, disable_ota=True, release=True) yield target.Extend('shell-release', app=K32WApp.SHELL, release=True) yield target.Extend('lock-release', app=K32WApp.LOCK, release=True) yield target.Extend('lock-low-power-release', app=K32WApp.LOCK, low_power=True, release=True).GlobBlacklist("Only on demand build") def cc13x2x7_26x2x7Targets(): target = Target('cc13x2x7_26x2x7', cc13x2x7_26x2x7Builder) yield target.Extend('lock-ftd', app=cc13x2x7_26x2x7App.LOCK, openthread_ftd=True) yield target.Extend('lock-mtd', app=cc13x2x7_26x2x7App.LOCK, openthread_ftd=False) yield target.Extend('pump', app=cc13x2x7_26x2x7App.PUMP) yield target.Extend('pump-controller', app=cc13x2x7_26x2x7App.PUMP_CONTROLLER) yield target.Extend('all-clusters', app=cc13x2x7_26x2x7App.ALL_CLUSTERS) yield target.Extend('shell', app=cc13x2x7_26x2x7App.SHELL) def Cyw30739Targets(): yield Target('cyw30739-cyw930739m2evb_01-light', Cyw30739Builder, board=Cyw30739Board.CYW930739M2EVB_01, app=Cyw30739App.LIGHT) yield Target('cyw30739-cyw930739m2evb_01-lock', Cyw30739Builder, board=Cyw30739Board.CYW930739M2EVB_01, app=Cyw30739App.LOCK) yield Target('cyw30739-cyw930739m2evb_01-ota-requestor', Cyw30739Builder, board=Cyw30739Board.CYW930739M2EVB_01, app=Cyw30739App.OTA_REQUESTOR).GlobBlacklist( "Running out of XIP flash space") yield Target('cyw30739-cyw930739m2evb_01-ota-requestor-no-progress-logging', Cyw30739Builder, board=Cyw30739Board.CYW930739M2EVB_01, app=Cyw30739App.OTA_REQUESTOR, progress_logging=False) def QorvoTargets(): target = Target('qpg', QpgBuilder) yield target.Extend('lock', board=QpgBoard.QPG6105, app=QpgApp.LOCK) yield target.Extend('light', board=QpgBoard.QPG6105, app=QpgApp.LIGHT) yield target.Extend('shell', board=QpgBoard.QPG6105, app=QpgApp.SHELL) yield target.Extend('persistent-storage', board=QpgBoard.QPG6105, app=QpgApp.PERSISTENT_STORAGE) def TizenTargets(): # Possible build variants. # NOTE: The number of potential builds is exponential here. builder = VariantBuilder() builder.AppendVariant(name="no-ble", enable_ble=False) builder.AppendVariant(name="no-wifi", enable_wifi=False) builder.AppendVariant(name="asan", use_asan=True) target = Target('tizen-arm', TizenBuilder, board=TizenBoard.ARM) builder.targets.append(target.Extend('light', app=TizenApp.LIGHT)) for target in builder.AllVariants(): yield target def Bl602Targets(): target = Target('bl602', Bl602Builder) yield target.Extend('light', board=Bl602Board.BL602BOARD, app=Bl602App.LIGHT) def IMXTargets(): target = Target('imx', IMXBuilder) yield target.Extend('chip-tool', app=IMXApp.CHIP_TOOL) yield target.Extend('lighting-app', app=IMXApp.LIGHT) yield target.Extend('thermostat', app=IMXApp.THERMOSTAT) yield target.Extend('all-clusters-app', app=IMXApp.ALL_CLUSTERS) yield target.Extend('ota-provider-app', app=IMXApp.OTA_PROVIDER) yield target.Extend('chip-tool-release', app=IMXApp.CHIP_TOOL, release=True) yield target.Extend('lighting-app-release', app=IMXApp.LIGHT, release=True) yield target.Extend('thermostat-release', app=IMXApp.THERMOSTAT, release=True) yield target.Extend('all-clusters-app-release', app=IMXApp.ALL_CLUSTERS, release=True) yield target.Extend('ota-provider-app-release', app=IMXApp.OTA_PROVIDER, release=True) ALL = [] target_generators = [ HostTargets(), Esp32Targets(), Efr32Targets(), NrfTargets(), AndroidTargets(), MbedTargets(), InfineonTargets(), AmebaTargets(), K32WTargets(), cc13x2x7_26x2x7Targets(), Cyw30739Targets(), QorvoTargets(), TizenTargets(), Bl602Targets(), IMXTargets(), ] for generator in target_generators: for target in generator: ALL.append(target) # Simple targets added one by one ALL.append(Target('telink-tlsr9518adk80d-light', TelinkBuilder, board=TelinkBoard.TLSR9518ADK80D, app=TelinkApp.LIGHT)) ALL.append(Target('telink-tlsr9518adk80d-light-switch', TelinkBuilder, board=TelinkBoard.TLSR9518ADK80D, app=TelinkApp.SWITCH)) # have a consistent order overall ALL.sort(key=lambda t: t.name)

42.510135

140

0.695581

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0.072449

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0.025543

0.409613

0.303088

0.218913

0.133055

0.080518

0.054453

0

0.036247

0.197528

25,166

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42.582064

0.816737

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0.035727

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1

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false

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0.172932

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null

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null

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1

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7c279f6e16ec9934410f291dea61230ff38bf396

4,608

py

Python

src/musegan/data.py

TRINITRONIC/musegan

0a62e0303a8ff357d7f385dcc6edba76afb132b2

[ "MIT" ]

null

src/musegan/data.py

TRINITRONIC/musegan

0a62e0303a8ff357d7f385dcc6edba76afb132b2

[ "MIT" ]

null

src/musegan/data.py

TRINITRONIC/musegan

0a62e0303a8ff357d7f385dcc6edba76afb132b2

[ "MIT" ]

null

"""This file contains functions for loading and preprocessing pianoroll data. """ import logging import numpy as np import tensorflow.compat.v1 as tf from musegan.config import SHUFFLE_BUFFER_SIZE, PREFETCH_SIZE LOGGER = logging.getLogger(__name__) # --- Data loader -------------------------------------------------------------- def load_data_from_npy(filename): """Load and return the training data from a npy file.""" return np.load(filename) def load_data_from_npz(filename): """Load and return the training data from a npz file (sparse format).""" with np.load(filename) as f: data = np.zeros(f['shape'], np.bool_) data[[x for x in f['nonzero']]] = True return data def load_data(data_source, data_filename): """Load and return the training data.""" if data_source == 'sa': import SharedArray as sa return sa.attach(data_filename) if data_source == 'npy': return load_data_from_npy(data_filename) if data_source == 'npz': return load_data_from_npz(data_filename) raise ValueError("Expect `data_source` to be one of 'sa', 'npy', 'npz'. " "But get " + str(data_source)) # --- Dataset Utilities ------------------------------------------------------- def random_transpose(pianoroll): """Randomly transpose a pianoroll with [-5, 6] semitones.""" semitone = np.random.randint(-5, 6) if semitone > 0: pianoroll[:, semitone:, 1:] = pianoroll[:, :-semitone, 1:] pianoroll[:, :semitone, 1:] = 0 elif semitone < 0: pianoroll[:, :semitone, 1:] = pianoroll[:, -semitone:, 1:] pianoroll[:, semitone:, 1:] = 0 return pianoroll def set_pianoroll_shape(pianoroll, data_shape): """Set the pianoroll shape and return the pianoroll.""" pianoroll.set_shape(data_shape) return pianoroll def set_label_shape(label): """Set the label shape and return the label.""" label.set_shape([1]) return label # --- Sampler ------------------------------------------------------------------ def get_samples(n_samples, data, labels=None, use_random_transpose=False): """Return some random samples of the training data.""" indices = np.random.choice(len(data), n_samples, False) if np.issubdtype(data.dtype, np.bool_): sample_data = data[indices] * 2. - 1. else: sample_data = data[indices] if use_random_transpose: sample_data = np.array([random_transpose(x) for x in sample_data]) if labels is None: return sample_data return sample_data, labels[indices] # --- Tensorflow Dataset ------------------------------------------------------- def _gen_data(data, labels=None): """Data Generator.""" if labels is None: for item in data: if np.issubdtype(data.dtype, np.bool_): yield item * 2. - 1. else: yield item else: for i, item in enumerate(data): if np.issubdtype(data.dtype, np.bool_): yield (item * 2. - 1., labels[i]) else: yield (item, labels[i]) def get_dataset(data, labels=None, batch_size=None, data_shape=None, use_random_transpose=False, num_threads=1): """Create and return a tensorflow dataset from an array.""" if labels is None: dataset = tf.data.Dataset.from_generator( lambda: _gen_data(data), tf.float32) if use_random_transpose: dataset = dataset.map( lambda pianoroll: tf.py_func( random_transpose, [pianoroll], tf.float32), num_parallel_calls=num_threads) dataset = dataset.map(lambda pianoroll: set_pianoroll_shape( pianoroll, data_shape), num_parallel_calls=num_threads) else: assert len(data) == len(labels), ( "Lengths of `data` and `lables` do not match.") dataset = tf.data.Dataset.from_generator( lambda: _gen_data(data, labels), [tf.float32, tf.int32]) if use_random_transpose: dataset = dataset.map( lambda pianoroll, label: ( tf.py_func(random_transpose, [pianoroll], tf.float32), label), num_parallel_calls=num_threads) dataset = dataset.map( lambda pianoroll, label: (set_pianoroll_shape( pianoroll, data_shape), set_label_shape(label)), num_parallel_calls=num_threads) dataset = dataset.shuffle(SHUFFLE_BUFFER_SIZE).repeat().batch(batch_size) return dataset.prefetch(PREFETCH_SIZE)

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0.223022

0.050886

0.040709

0.401055

0.352808

0.335846

0.282699

0.232944

0.162835

0

0.009494

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4,608

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0.75374

0.175781

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false

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null

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7c2b65379c3bd0e388f419a0d07e73a9770aad35

48,787

py

Python

visnav/algo/orig/tools.py

oknuutti/hw_visnav

5254b8bdd146548413554c00e6e76264a2540e8b

[ "MIT" ]

null

visnav/algo/orig/tools.py

oknuutti/hw_visnav

5254b8bdd146548413554c00e6e76264a2540e8b

[ "MIT" ]

null

visnav/algo/orig/tools.py

oknuutti/hw_visnav

5254b8bdd146548413554c00e6e76264a2540e8b

[ "MIT" ]

null

import math import time import numpy as np import numba as nb import quaternion # adds to numpy # noqa # pylint: disable=unused-import import sys import scipy from astropy.coordinates import SkyCoord from scipy.interpolate import RectBivariateSpline from scipy.interpolate import NearestNDInterpolator # from scipy.spatial.ckdtree import cKDTree from visnav.settings import * class PositioningException(Exception): pass class Stopwatch: # from https://www.safaribooksonline.com/library/view/python-cookbook-3rd/9781449357337/ch13s13.html def __init__(self, elapsed=0.0, func=time.perf_counter): self._elapsed = elapsed self._func = func self._start = None @property def elapsed(self): return self._elapsed + ((self._func() - self._start) if self.running else 0) def start(self): if self._start is not None: raise RuntimeError('Already started') self._start = self._func() def stop(self): if self._start is None: raise RuntimeError('Not started') end = self._func() self._elapsed += end - self._start self._start = None def reset(self): self._elapsed = 0.0 @property def running(self): return self._start is not None def __enter__(self): self.start() return self def __exit__(self, *args): self.stop() def sphere_angle_radius(loc, r): return np.arcsin(r / np.linalg.norm(loc, axis=1)) def dist_across_and_along_vect(A, b): """ A: array of vectors, b: axis vector """ lat, lon, r = cartesian2spherical(*b) q = ypr_to_q(lat, lon, 0).conj() R = quaternion.as_rotation_matrix(q) Ab = R.dot(A.T).T d = Ab[:, 0:1] r = np.linalg.norm(Ab[:, 1:3], axis=1).reshape((-1, 1)) return r, d def point_vector_dist(A, B, dist_along_v=False): """ A: point, B: vector """ # (length of b)**2 normB2 = (B ** 2).sum(-1).reshape((-1, 1)) # a dot b vector product (project a on b but also times length of b) diagAB = (A * B).sum(-1).reshape((-1, 1)) # A projected along B (projection = a dot b/||b|| * b/||b||) A_B = (diagAB / normB2) * B # vector from projected A to A, it is perpendicular to B AB2A = A - A_B # diff vector lengths normD = np.sqrt((AB2A ** 2).sum(-1)).reshape((-1, 1)) return (normD, diagAB / np.sqrt(normB2)) if dist_along_v else normD def sc_asteroid_max_shift_error(A, B): """ Calculate max error between two set of vertices when projected to camera, A = estimated vertex positions B = true vertex positions Error is a vector perpendicular to B, i.e. A - A|| """ # diff vector lengths normD = point_vector_dist(A, B) # max length of diff vectors return np.max(normD) @nb.njit(nb.f8[:](nb.f8[:], nb.f8[:])) def cross3d(left, right): # for short vectors cross product is faster in pure python than with numpy.cross x = ((left[1] * right[2]) - (left[2] * right[1])) y = ((left[2] * right[0]) - (left[0] * right[2])) z = ((left[0] * right[1]) - (left[1] * right[0])) return np.array((x, y, z)) def normalize_v(v): norm = np.linalg.norm(v) return v / norm if norm != 0 else v @nb.njit(nb.types.f8[:](nb.types.f8[:])) def normalize_v_f8(v): norm = np.linalg.norm(v) return v / norm if norm != 0 else v def generate_field_fft(shape, sd=(0.33, 0.33, 0.34), len_sc=(0.5, 0.5 / 4, 0.5 / 16)): from visnav.algo.image import ImageProc sds = sd if getattr(sd, '__len__', False) else [sd] len_scs = len_sc if getattr(len_sc, '__len__', False) else [len_sc] assert len(shape) == 2, 'only 2d shapes are valid' assert len(sds) == len(len_scs), 'len(sd) differs from len(len_sc)' n = np.prod(shape) kernel = np.sum( np.stack([1 / len_sc * sd * n * ImageProc.gkern2d(shape, 1 / len_sc) for sd, len_sc in zip(sds, len_scs)], axis=2), axis=2) f_img = np.random.normal(0, 1, shape) + np.complex(0, 1) * np.random.normal(0, 1, shape) f_img = np.real(np.fft.ifft2(np.fft.fftshift(kernel * f_img))) return f_img @nb.njit(nb.types.f8[:](nb.types.f8[:], nb.types.f8[:], nb.types.f8[:])) def _surf_normal(x1, x2, x3): # a, b, c = np.array(x1, dtype=np.float64), np.array(x2, dtype=np.float64), np.array(x3, dtype=np.float64) return normalize_v_f8(cross3d(x2-x1, x3-x1)) def surf_normal(x1, x2, x3): a, b, c = np.array(x1, dtype=np.float64), np.array(x2, dtype=np.float64), np.array(x3, dtype=np.float64) return _surf_normal(a, b, c) # return normalize_v_f8(cross3d(b-a, c-a)) def vector_projection(a, b): return a.dot(b) / b.dot(b) * b def vector_rejection(a, b): return a - vector_projection(a, b) def angle_between_v(v1, v2): # Notice: only returns angles between 0 and 180 deg try: v1 = np.reshape(v1, (1, -1)) v2 = np.reshape(v2, (-1, 1)) n1 = v1 / np.linalg.norm(v1) n2 = v2 / np.linalg.norm(v2) cos_angle = n1.dot(n2) except TypeError as e: raise Exception('Bad vectors:\n\tv1: %s\n\tv2: %s' % (v1, v2)) from e return math.acos(np.clip(cos_angle, -1, 1)) def angle_between_v_mx(a, B, normalize=True): Bn = B / np.linalg.norm(B, axis=1).reshape((-1, 1)) if normalize else B an = normalize_v(a).reshape((-1, 1)) if normalize else a return np.arccos(np.clip(Bn.dot(an), -1.0, 1.0)) def angle_between_mx(A, B): return angle_between_rows(A, B) def angle_between_rows(A, B, normalize=True): assert A.shape[1] == 3 and B.shape[1] == 3, 'matrices need to be of shape (n, 3) and (m, 3)' if A.shape[0] == B.shape[0]: # from https://stackoverflow.com/questions/50772176/calculate-the-angle-between-the-rows-of-two-matrices-in-numpy/50772253 cos_angles = np.einsum('ij,ij->i', A, B) if normalize: p2 = np.einsum('ij,ij->i', A, A) p3 = np.einsum('ij,ij->i', B, B) cos_angles /= np.sqrt(p2 * p3) else: if normalize: A = A / np.linalg.norm(A, axis=1).reshape((-1, 1)) B = B / np.linalg.norm(B, axis=1).reshape((-1, 1)) cos_angles = B.dot(A.T) return np.arccos(np.clip(cos_angles, -1.0, 1.0)) def rand_q(angle): r = normalize_v(np.random.normal(size=3)) return angleaxis_to_q(np.hstack((angle, r))) def angle_between_q(q1, q2): # from https://chrischoy.github.io/research/measuring-rotation/ qd = q1.conj() * q2 return abs(wrap_rads(2 * math.acos(qd.normalized().w))) def angle_between_q_arr(q1, q2): qd = quaternion.as_float_array(q1.conj() * q2) qd = qd / np.linalg.norm(qd, axis=1).reshape((-1, 1)) return np.abs(wrap_rads(2 * np.arccos(qd[:, 0]))) def angle_between_ypr(ypr1, ypr2): q1 = ypr_to_q(*ypr1) q2 = ypr_to_q(*ypr2) return angle_between_q(q1, q2) def distance_mx(A, B): assert A.shape[1] == B.shape[1], 'matrices must have same amount of columns' k = A.shape[1] O = np.repeat(A.reshape((-1, 1, k)), B.shape[0], axis=1) - np.repeat(B.reshape((1, -1, k)), A.shape[0], axis=0) D = np.linalg.norm(O, axis=2) return D def q_to_unitbase(q): U0 = quaternion.as_quat_array([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1.]]) Uq = q * U0 * q.conj() return quaternion.as_float_array(Uq)[:, 1:] def equatorial_to_ecliptic(ra, dec): """ translate from equatorial ra & dec to ecliptic ones """ sc = SkyCoord(ra, dec, unit='deg', frame='icrs', obstime='J2000') \ .transform_to('barycentrictrueecliptic') return sc.lat.value, sc.lon.value def q_to_angleaxis(q, compact=False): theta = math.acos(np.clip(q.w, -1, 1)) * 2.0 v = normalize_v(np.array([q.x, q.y, q.z])) if compact: return theta * v else: return np.array((theta,) + tuple(v)) def angleaxis_to_q(rv): """ first angle, then axis """ if len(rv) == 4: theta = rv[0] v = normalize_v(np.array(rv[1:])) elif len(rv) == 3: theta = math.sqrt(sum(x ** 2 for x in rv)) v = np.array(rv) / (1 if theta == 0 else theta) else: raise Exception('Invalid angle-axis vector: %s' % (rv,)) w = math.cos(theta / 2) v = v * math.sin(theta / 2) return np.quaternion(w, *v).normalized() def ypr_to_q(lat, lon, roll): # Tait-Bryan angles, aka yaw-pitch-roll, nautical angles, cardan angles # intrinsic euler rotations z-y'-x'', pitch=-lat, yaw=lon return ( np.quaternion(math.cos(lon / 2), 0, 0, math.sin(lon / 2)) * np.quaternion(math.cos(-lat / 2), 0, math.sin(-lat / 2), 0) * np.quaternion(math.cos(roll / 2), math.sin(roll / 2), 0, 0) ) def eul_to_q(angles, order='xyz', reverse=False): assert len(angles) == len(order), 'len(angles) != len(order)' q = quaternion.one idx = {'x': 0, 'y': 1, 'z': 2} for angle, axis in zip(angles, order): w = math.cos(angle / 2) v = [0, 0, 0] v[idx[axis]] = math.sin(angle / 2) dq = np.quaternion(w, *v) q = (dq * q) if reverse else (q * dq) return q def q_to_ypr(q): # from https://math.stackexchange.com/questions/687964/getting-euler-tait-bryan-angles-from-quaternion-representation q0, q1, q2, q3 = quaternion.as_float_array(q) roll = np.arctan2(q2 * q3 + q0 * q1, .5 - q1 ** 2 - q2 ** 2) lat = -np.arcsin(np.clip(-2 * (q1 * q3 - q0 * q2), -1, 1)) lon = np.arctan2(q1 * q2 + q0 * q3, .5 - q2 ** 2 - q3 ** 2) return lat, lon, roll def mean_q(qs, ws=None): """ returns a (weighted) mean of a set of quaternions idea is to rotate a bit in the direction of new quaternion from the sum of previous rotations NOTE: not tested properly, might not return same mean quaternion if order of input changed """ wtot = 0 qtot = quaternion.one for q, w in zip(qs, np.ones((len(qs),)) if ws is None else ws): ddaa = q_to_angleaxis(qtot.conj() * q) ddaa[0] = wrap_rads(ddaa[0]) * w / (w + wtot) qtot = angleaxis_to_q(ddaa) * qtot wtot += w return qtot def q_times_v(q, v): qv = np.quaternion(0, *v) qv2 = q * qv * q.conj() return np.array([qv2.x, qv2.y, qv2.z]) def q_times_mx(q, mx): qqmx = q * mx2qmx(mx) * q.conj() aqqmx = quaternion.as_float_array(qqmx) return aqqmx[:, 1:] def mx2qmx(mx): qmx = np.zeros((mx.shape[0], 4)) qmx[:, 1:] = mx return quaternion.as_quat_array(qmx) def wrap_rads(a): return (a + math.pi) % (2 * math.pi) - math.pi def wrap_degs(a): return (a + 180) % 360 - 180 def eccentric_anomaly(eccentricity, mean_anomaly, tol=1e-6): # from http://www.jgiesen.de/kepler/kepler.html E = mean_anomaly if eccentricity < 0.8 else math.pi F = E - eccentricity * math.sin(mean_anomaly) - mean_anomaly; for i in range(30): if abs(F) < tol: break E = E - F / (1.0 - eccentricity * math.cos(E)) F = E - eccentricity * math.sin(E) - mean_anomaly return round(E / tol) * tol def solar_elongation(ast_v, sc_q): sco_x, sco_y, sco_z = q_to_unitbase(sc_q) if USE_ICRS: sc = SkyCoord(x=ast_v[0], y=ast_v[1], z=ast_v[2], frame='icrs', unit='m', representation_type='cartesian', obstime='J2000') \ .transform_to('hcrs') \ .represent_as('cartesian') ast_v = np.array([sc.x.value, sc.y.value, sc.z.value]) # angle between camera axis and the sun, 0: right ahead, pi: behind elong = angle_between_v(-ast_v, sco_x) # direction the sun is at when looking along camera axis nvec = np.cross(sco_x, ast_v) direc = angle_between_v(nvec, sco_z) # decide if direction needs to be negative or not if np.cross(nvec, sco_z).dot(sco_x) < 0: direc = -direc return elong, direc def find_nearest_lesser(array, value): I = np.where(array < value) idx = (np.abs(array - value)).argmin() return array[I[idx]], I[idx] def find_nearest_greater(array, value): I = np.where(array > value) idx = (np.abs(array - value)).argmin() return array[I[idx]], I[idx] def find_nearest(array, value): idx = (np.abs(array - value)).argmin() return array[idx], idx def find_nearest_arr(array, value, ord=None, fun=None): diff = array - value idx = np.linalg.norm(diff if fun is None else list(map(fun, diff)), ord=ord, axis=1).argmin() return array[idx], idx def find_nearest_n(array, value, r, ord=None, fun=None): diff = array - value d = np.linalg.norm(diff if fun is None else list(map(fun, diff)), ord=ord, axis=1) idxs = np.where(d < r) return idxs[0] def find_nearest_each(haystack, needles, ord=None): assert len(haystack.shape) == 2 and len(needles.shape) == 2 and haystack.shape[1] == needles.shape[1], \ 'wrong shapes for haystack and needles, %s and %s, respectively' % (haystack.shape, needles.shape) c = haystack.shape[1] diff_mx = np.repeat(needles.reshape((-1, 1, c)), haystack.shape[0], axis=1) - np.repeat( haystack.reshape((1, -1, c)), needles.shape[0], axis=0) norm_mx = np.linalg.norm(diff_mx, axis=2, ord=ord) idxs = norm_mx.argmin(axis=1) return haystack[idxs], idxs def cartesian2spherical(x, y, z): r = math.sqrt(x ** 2 + y ** 2 + z ** 2) theta = math.acos(z / r) phi = math.atan2(y, x) lat = math.pi / 2 - theta lon = phi return np.array([lat, lon, r]) def spherical2cartesian(lat, lon, r): theta = math.pi / 2 - lat phi = lon x = r * math.sin(theta) * math.cos(phi) y = r * math.sin(theta) * math.sin(phi) z = r * math.cos(theta) return np.array([x, y, z]) def spherical2cartesian_arr(A, r=None): theta = math.pi / 2 - A[:, 0] phi = A[:, 1] r = (r or A[:, 2]) x = r * np.sin(theta) y = x * np.sin(phi) x *= np.cos(phi) # x = r * np.sin(theta) * np.cos(phi) # y = r * np.sin(theta) * np.sin(phi) z = r * np.cos(theta) return np.vstack([x, y, z]).T def discretize_v(v, tol=None, lat_range=(-math.pi / 2, math.pi / 2), points=None): """ simulate feature database by giving closest light direction with given tolerance """ if tol is not None and points is not None or tol is None and points is None: assert False, 'Give either tol or points' elif tol is not None: points = bf2_lat_lon(tol, lat_range=lat_range) lat, lon, r = cartesian2spherical(*v) (nlat, nlon), idx = find_nearest_arr( points, np.array((lat, lon)), ord=2, fun=wrap_rads, ) ret = spherical2cartesian(nlat, nlon, r) return ret, idx def discretize_q(q, tol=None, lat_range=(-math.pi / 2, math.pi / 2), points=None): """ simulate feature database by giving closest lat & roll with given tolerance and set lon to zero as feature detectors are rotation invariant (in opengl coords) """ if tol is not None and points is not None or tol is None and points is None: assert False, 'Give either tol or points' elif tol is not None: points = bf2_lat_lon(tol, lat_range=lat_range) lat, lon, roll = q_to_ypr(q) (nlat, nroll), idx = find_nearest_arr( points, np.array((lat, roll)), ord=2, fun=wrap_rads, ) nq0 = ypr_to_q(nlat, 0, nroll) return nq0, idx def bf_lat_lon(tol, lat_range=(-math.pi / 2, math.pi / 2)): # tol**2 == (step/2)**2 + (step/2)**2 -- 7deg is quite nice in terms of len(lon)*len(lat) == 1260 step = math.sqrt(2) * tol lat_steps = np.linspace(*lat_range, num=math.ceil((lat_range[1] - lat_range[0]) / step), endpoint=False)[1:] lon_steps = np.linspace(-math.pi, math.pi, num=math.ceil(2 * math.pi / step), endpoint=False) return lat_steps, lon_steps def bf2_lat_lon(tol, lat_range=(-math.pi / 2, math.pi / 2)): # tol**2 == (step/2)**2 + (step/2)**2 -- 7deg is quite nice in terms of len(lon)*len(lat) == 1260 step = math.sqrt(2) * tol lat_steps = np.linspace(*lat_range, num=math.ceil((lat_range[1] - lat_range[0]) / step), endpoint=False)[1:] # similar to https://www.cmu.edu/biolphys/deserno/pdf/sphere_equi.pdf points = [] for lat in lat_steps: Mphi = math.ceil(2 * math.pi * math.cos(lat) / step) lon_steps = np.linspace(-math.pi, math.pi, num=Mphi, endpoint=False) points.extend(zip([lat] * len(lon_steps), lon_steps)) return points def robust_mean(arr, discard_percentile=0.2, ret_n=False, axis=None): J = np.logical_not(np.isnan(arr)) if axis is not None: J = np.all(J, axis=1 if axis == 0 else 0) if axis == 0: arr = arr[J, :] elif axis == 1: arr = arr[:, J] else: arr = arr[J] low = np.percentile(arr, discard_percentile, axis=axis) high = np.percentile(arr, 100 - discard_percentile, axis=axis) I = np.logical_and(low < arr, arr < high) if axis is not None: I = np.all(I, axis=1 if axis == 0 else 0) m = np.mean(arr[:, I] if axis == 1 else arr[I], axis=axis) return (m, np.sum(I, axis=axis)) if ret_n else m def robust_std(arr, discard_percentile=0.2, mean=None, axis=None): corr = 1 if mean is None: mean, n = robust_mean(arr, discard_percentile=discard_percentile, ret_n=True, axis=axis) corr = n / (n - 1) return np.sqrt(robust_mean((arr - mean) ** 2, discard_percentile=discard_percentile, axis=axis) * corr) def mv_normal(mean, cov=None, L=None, size=None): if size is None: final_shape = [] elif isinstance(size, (int, np.integer)): final_shape = [size] else: final_shape = size final_shape = list(final_shape[:]) final_shape.append(mean.shape[0]) if L is None and cov is None \ or L is not None and cov is not None: raise ValueError("you must provide either cov or L (cholesky decomp result)") if len(mean.shape) != 1: raise ValueError("mean must be 1 dimensional") if L is not None: if (len(L.shape) != 2) or (L.shape[0] != L.shape[1]): raise ValueError("L must be 2 dimensional and square") if mean.shape[0] != L.shape[0]: raise ValueError("mean and L must have same length") if cov is not None: if (len(cov.shape) != 2) or (cov.shape[0] != cov.shape[1]): raise ValueError("cov must be 2 dimensional and square") if mean.shape[0] != cov.shape[0]: raise ValueError("mean and cov must have same length") L = np.linalg.cholesky(cov) from numpy.random import standard_normal z = standard_normal(final_shape).reshape(mean.shape[0], -1) x = L.dot(z).T x += mean x.shape = tuple(final_shape) return x, L def point_cloud_vs_model_err(points: np.ndarray, model) -> np.ndarray: faces = np.array([f[0] for f in model.faces], dtype='uint') vertices = np.array(model.vertices) errs = get_model_errors(points, vertices, faces) return errs # @nb.njit(nb.f8[:](nb.f8[:, :], nb.f8[:, :]), nogil=True) @nb.njit(nb.f8(nb.f8[:, :], nb.f8[:, :]), nogil=True, cache=True) def poly_line_intersect(poly, line): # extend_line = True eps = 1e-6 none = np.inf # np.zeros(1) v0v1 = poly[1, :] - poly[0, :] v0v2 = poly[2, :] - poly[0, :] dir = line[1, :] - line[0, :] line_len = math.sqrt(np.sum(dir ** 2)) if line_len < eps: return none dir = dir / line_len pvec = cross3d(dir, v0v2).ravel() det = np.dot(v0v1, pvec) if abs(det) < eps: return none # backface culling if False and det < 0: return none # frontface culling if False and det > 0: return none inv_det = 1.0 / det tvec = line[0, :] - poly[0, :] u = tvec.dot(pvec) * inv_det if u + eps < 0 or u - eps > 1: return none qvec = cross3d(tvec, v0v1).ravel() v = dir.dot(qvec) * inv_det if v + eps < 0 or u + v - eps > 1: return none t = v0v2.dot(qvec) * inv_det if True: # return error directly return t - line_len else: # return actual 3d intersect point if not extend_line and t - eps > line_len: return none return line[0, :] + t * dir # INVESTIGATE: parallel = True does not speed up at all (or marginally) for some reason even though all cores are in use @nb.njit(nb.f8(nb.u4[:, :], nb.f8[:, :], nb.f8[:, :]), nogil=True, parallel=False, cache=True) def intersections(faces, vertices, line): # pts = np.zeros((10, 3)) # i = 0 min_err = np.ones(faces.shape[0]) * np.inf for k in nb.prange(1, faces.shape[0]): err = poly_line_intersect(vertices[faces[k, :], :], line) min_err[k] = err # if abs(err) < min_err: # min_err = err # if len(pt) == 3: # pts[i, :] = pt # i += 1 # if i >= pts.shape[0]: # print('too many intersects') # i -= 1 i = np.argmin(np.abs(min_err)) return min_err[i] # pts[0:i, :] # @nb.jit(nb.f8[:](nb.f8[:, :], nb.f8[:, :], nb.i4[:, :]), nogil=True, parallel=False) def get_model_errors(points, vertices, faces): count = len(points) show_progress(count // 10, 0) j = 0 devs = np.empty(points.shape[0]) for i in nb.prange(count): vx = points[i, :] err = intersections(faces, vertices, np.array(((0, 0, 0), vx))) if math.isinf(err): # len(pts) == 0: print('no intersections!') continue if False: idx = np.argmin([np.linalg.norm(pt - vx) for pt in pts]) err = np.linalg.norm(pts[idx]) - np.linalg.norm(vx) devs[i] = err if j < i // 10: show_progress(count // 10, i // 10) j = i // 10 return devs def crop_model(model, cam_v, cam_q, x_fov, y_fov): assert False, 'not implemented' def augment_model(model, multiplier=3, length_scales=(0, 0.1, 1), sds=(1e-5, 1.6e-4, 2.4e-4)): assert multiplier > 1 and multiplier % 1 == 0, 'multiplier must be integer and >1' from scipy.interpolate import LinearNDInterpolator try: from sklearn.gaussian_process.kernels import Matern, WhiteKernel except: print('Requires scikit-learn, install using "conda install scikit-learn"') sys.exit() points = np.array(model.vertices) max_rng = np.max(np.ptp(points, axis=0)) # white noise to ensure positive definite covariance matrix ls = dict(zip(length_scales, sds)) sd0 = ls.pop(0, 1e-5) kernel = WhiteKernel(noise_level=sd0 * max_rng) for l, s in ls.items(): kernel += s ** 2 * Matern(length_scale=l * max_rng, nu=1.5) assert False, 'not implemented' # TODO: how is the covariance mx constructed again? y_cov = kernel(points) # TODO: sample gp ??? how to tie existing points and generate the new points in between? aug_points, L = mv_normal(points, cov=y_cov) # TODO: how to interpolate faces? pass # interpolate texture # TODO: augment texture interp = LinearNDInterpolator(points, model.texcoords) aug_texcoords = interp(aug_points) data = model.as_dict() data['faces'] = aug_faces data['vertices'] = aug_points data['texcoords'] = aug_texcoords from visnav.iotools import objloader aug_model = objloader.ShapeModel(data=data) aug_model.recalc_norms() return aug_model, L def apply_noise(model, support=(None, None), L=(None, None), len_sc=SHAPE_MODEL_NOISE_LEN_SC, noise_lv=SHAPE_MODEL_NOISE_LV['lo'], only_z=False, tx_noise=0, tx_noise_len_sc=SHAPE_MODEL_NOISE_LEN_SC, tx_hf_noise=True): Sv, St = support Lv, Lt = L inplace = noise_lv == 0 and model.texfile is None if noise_lv > 0: noisy_points, avg_dev, Lv = points_with_noise(points=model.vertices, support=Sv, L=Lv, noise_lv=noise_lv, len_sc=len_sc, only_z=only_z) else: noisy_points, avg_dev, Lv = model.vertices, 0, None tex = model.tex if tx_noise > 0: if inplace: model.tex = np.ones(model.tex.shape) Lt = Lv if Lt is None and tx_noise == noise_lv and tx_noise_len_sc == len_sc else Lt tex, tx_avg_dev, Lt = texture_noise(model, support=St, L=Lt, noise_sd=tx_noise, len_sc=tx_noise_len_sc, hf_noise=tx_hf_noise) if inplace: model.tex = tex noisy_model = model else: data = model.as_dict() data['vertices'] = noisy_points if tx_noise > 0: data['tex'] = tex data['texfile'] = None from visnav.iotools import objloader noisy_model = objloader.ShapeModel(data=data) if noise_lv > 0: noisy_model.recalc_norms() else: noisy_model.normals = model.normals return noisy_model, avg_dev, (Lv, Lt) def texture_noise(model, support=None, L=None, noise_sd=SHAPE_MODEL_NOISE_LV['lo'], len_sc=SHAPE_MODEL_NOISE_LEN_SC, max_rng=None, max_n=1e4, hf_noise=True): tex = model.load_texture() if tex is None: print('tools.texture_noise: no texture loaded') return [None] * 3 r = np.sqrt(max_n / np.prod(tex.shape[:2])) ny, nx = (np.array(tex.shape[:2]) * r).astype(np.int) n = nx * ny tx_grid_xx, tx_grid_yy = np.meshgrid(np.linspace(0, 1, nx), np.linspace(0, 1, ny)) tx_grid = np.hstack((tx_grid_xx.reshape((-1, 1)), tx_grid_yy.reshape((-1, 1)))) support = support if support else model points = np.array(support.vertices) max_rng = np.max(np.ptp(points, axis=0)) if max_rng is None else max_rng # use vertices for distances, find corresponding vertex for each pixel y_cov = None if L is None: try: from sklearn.gaussian_process.kernels import Matern, WhiteKernel except: print('Requires scikit-learn, install using "conda install scikit-learn"') sys.exit() kernel = 1.0 * noise_sd * Matern(length_scale=len_sc * max_rng, nu=1.5) \ + 0.5 * noise_sd * Matern(length_scale=0.1 * len_sc * max_rng, nu=1.5) \ + WhiteKernel( noise_level=1e-5 * noise_sd * max_rng) # white noise for positive definite covariance matrix only # texture coordinates given so that x points left and *Y POINTS UP* tex_img_coords = np.array(support.texcoords) tex_img_coords[:, 1] = 1 - tex_img_coords[:, 1] _, idxs = find_nearest_each(haystack=tex_img_coords, needles=tx_grid) tx2vx = support.texture_to_vertex_map() y_cov = kernel(points[tx2vx[idxs], :] - np.mean(points, axis=0)) if 0: # for debugging distances import matplotlib.pyplot as plt import cv2 from visnav.algo.image import ImageProc orig_tx = cv2.imread(os.path.join(DATA_DIR, '67p+tex.png'), cv2.IMREAD_GRAYSCALE) gx, gy = np.gradient(points[tx2vx[idxs], :].reshape((ny, nx, 3)), axis=(1, 0)) gxy = np.linalg.norm(gx, axis=2) + np.linalg.norm(gy, axis=2) gxy = (gxy - np.min(gxy)) / (np.max(gxy) - np.min(gxy)) grad_img = cv2.resize((gxy * 255).astype('uint8'), orig_tx.shape) overlaid = ImageProc.merge((orig_tx, grad_img)) plt.figure(1) plt.imshow(overlaid) plt.show() # sample gp e0, L = mv_normal(np.zeros(n), cov=y_cov, L=L) e0 = e0.reshape((ny, nx)) # interpolate for final texture x = np.linspace(np.min(tx_grid_xx), np.max(tx_grid_xx), tex.shape[1]) y = np.linspace(np.min(tx_grid_yy), np.max(tx_grid_yy), tex.shape[0]) interp0 = RectBivariateSpline(tx_grid_xx[0, :], tx_grid_yy[:, 0], e0, kx=1, ky=1) err0 = interp0(x, y) if 0: import matplotlib.pyplot as plt import cv2 from visnav.algo.image import ImageProc orig_tx = cv2.imread(os.path.join(DATA_DIR, '67p+tex.png'), cv2.IMREAD_GRAYSCALE) err_ = err0 if 1 else e0 eimg = (err_ - np.min(err_)) / (np.max(err_) - np.min(err_)) eimg = cv2.resize((eimg * 255).astype('uint8'), orig_tx.shape) overlaid = ImageProc.merge((orig_tx, eimg)) plt.figure(1) plt.imshow(overlaid) plt.show() err1 = 0 if hf_noise: e1, L = mv_normal(np.zeros(n), L=L) e1 = e1.reshape((ny, nx)) interp1 = RectBivariateSpline(tx_grid_xx[0, :], tx_grid_yy[:, 0], e1, kx=1, ky=1) err_coef = interp1(x, y) lo, hi = np.min(err_coef), np.max(err_coef) err_coef = (err_coef - lo) / (hi - lo) len_sc = 10 err1 = generate_field_fft(tex.shape, (6 * noise_sd, 4 * noise_sd), (len_sc / 1000, len_sc / 4500)) if hf_noise else 0 err1 *= err_coef noisy_tex = tex + err0 + err1 noisy_tex /= np.max(noisy_tex) if 0: import matplotlib.pyplot as plt plt.figure(1) plt.imshow(noisy_tex) plt.figure(2) plt.imshow(err0) plt.figure(3) plt.imshow(err1) plt.show() return noisy_tex, np.std(err0 + err1), L class NearestKernelNDInterpolator(NearestNDInterpolator): def __init__(self, *args, k_nearest=None, kernel='gaussian', kernel_sc=None, kernel_eps=1e-12, query_eps=0.05, max_distance=None, **kwargs): """ Parameters ---------- kernel : one of the following functions of distance that give weight to neighbours: 'linear': (kernel_sc/(r + kernel_eps)) 'quadratic': (kernel_sc/(r + kernel_eps))**2 'cubic': (kernel_sc/(r + kernel_eps))**3 'gaussian': exp(-(r/kernel_sc)**2) k_nearest : if given, uses k_nearest neighbours for interpolation regardless of their distances """ choices = ('linear', 'quadratic', 'cubic', 'gaussian') assert kernel in choices, 'kernel must be one of %s' % (choices,) self._tree_options = kwargs.get('tree_options', {}) super(NearestKernelNDInterpolator, self).__init__(*args, **kwargs) if max_distance is None: if kernel_sc is None: d, _ = self.tree.query(self.points, k=k_nearest) kernel_sc = np.mean(d) * k_nearest / (k_nearest - 1) max_distance = kernel_sc * 3 assert kernel_sc is not None, 'kernel_sc need to be set' self.kernel = kernel self.kernel_sc = kernel_sc self.kernel_eps = kernel_eps self.k_nearest = k_nearest self.max_distance = max_distance self.query_eps = query_eps def _linear(self, r): if scipy.sparse.issparse(r): return self.kernel_sc / (r + self.kernel_eps) else: return self.kernel_sc / (r + self.kernel_eps) def _quadratic(self, r): if scipy.sparse.issparse(r): return np.power(self.kernel_sc / (r.data + self.kernel_eps), 2, out=r.data) else: return (self.kernel_sc / (r + self.kernel_eps)) ** 2 def _cubic(self, r): if scipy.sparse.issparse(r): return self.kernel_sc / (r + self.kernel_eps).power(3) else: return (self.kernel_sc / (r + self.kernel_eps)) ** 3 def _gaussian(self, r): if scipy.sparse.issparse(r): return np.exp((-r.data / self.kernel_sc) ** 2, out=r.data) else: return np.exp(-(r / self.kernel_sc) ** 2) def __call__(self, *args): """ Evaluate interpolator at given points. Parameters ---------- xi : ndarray of float, shape (..., ndim) Points where to interpolate data at. """ from scipy.interpolate.interpnd import _ndim_coords_from_arrays xi = _ndim_coords_from_arrays(args, ndim=self.points.shape[1]) xi = self._check_call_shape(xi) xi = self._scale_x(xi) r, idxs = self.tree.query(xi, self.k_nearest, eps=self.query_eps, distance_upper_bound=self.max_distance or np.inf) w = getattr(self, '_' + self.kernel)(r).reshape((-1, self.k_nearest, 1)) + self.kernel_eps w /= np.sum(w, axis=1).reshape((-1, 1, 1)) yt = np.vstack((self.values, [0])) # if idxs[i, j] == len(values), then i:th point doesnt have j:th match yi = np.sum(yt[idxs, :] * w, axis=1) return yi def points_with_noise(points, support=None, L=None, noise_lv=SHAPE_MODEL_NOISE_LV['lo'], len_sc=SHAPE_MODEL_NOISE_LEN_SC, max_rng=None, only_z=False): try: from sklearn.gaussian_process.kernels import Matern, WhiteKernel except: print('Requires scikit-learn, install using "conda install scikit-learn"') sys.exit() if support is None: support = points # [random.sample(list(range(len(points))), min(3000,len(points)))] n = len(support) mean = np.mean(points, axis=0) max_rng = np.max(np.ptp(points, axis=0)) if max_rng is None else max_rng y_cov = None if L is None: kernel = 0.6 * noise_lv * Matern(length_scale=len_sc * max_rng, nu=1.5) \ + 0.4 * noise_lv * Matern(length_scale=0.1 * len_sc * max_rng, nu=1.5) \ + WhiteKernel( noise_level=1e-5 * noise_lv * max_rng) # white noise for positive definite covariance matrix only y_cov = kernel(support - mean) # sample gp e0, L = mv_normal(np.zeros(n), cov=y_cov, L=L) err = np.exp(e0.astype(points.dtype)).reshape((-1, 1)) if len(err) == len(points): full_err = err if DEBUG: print('using orig gp sampled err') else: # interpolate sc = 0.05 * len_sc * max_rng interp = NearestKernelNDInterpolator(support - mean, err, k_nearest=12, kernel='gaussian', kernel_sc=sc, max_distance=sc * 6) full_err = interp(points - mean).astype(points.dtype) # maybe extrapolate nanidx = tuple(np.isnan(full_err).flat) if np.any(nanidx): assert False, 'shouldnt happen' # if DEBUG or not BATCH_MODE: # print('%sx nans'%np.sum(nanidx)) # naninterp = NearestNDInterpolator(support, err) # try: # full_err[nanidx,] = naninterp(points[nanidx, :]).astype(points.dtype) # except IndexError as e: # raise IndexError('%s,%s,%s'%(err.shape, full_err.shape, points.shape)) from e # extra high frequency noise # white_noise = 1 if True else np.exp(np.random.normal(scale=0.2*noise_lv*max_rng, size=(len(full_err),1))) if only_z: add_err_z = (max_rng / 2) * (full_err - 1) add_err = np.concatenate((np.zeros((len(full_err), 2)), add_err_z), axis=1) noisy_points = points + add_err devs = np.abs(noisy_points[:, 2] - points[:, 2]) / (max_rng / 2) assert np.isclose(devs.flatten(), np.abs(full_err - 1).flatten()).all(), 'something wrong' else: # noisy_points = (points-mean)*full_err*white_noise +mean # r = np.sqrt(np.sum((points - mean)**2, axis=-1)).reshape(-1, 1) # noisy_points = (points - mean) * (1 + np.log(full_err)/r) + mean noisy_points = (points - mean) * full_err + mean devs = np.sqrt(np.sum((noisy_points - points) ** 2, axis=-1) / np.sum((points - mean) ** 2, axis=-1)) if DEBUG or not BATCH_MODE: print('noise (lv=%.3f): %.3f, %.3f; avg=%.3f' % ( (noise_lv,) + tuple(np.percentile(devs, (68, 95))) + (np.mean(devs),))) if False: import matplotlib.pyplot as plt plt.figure(1, figsize=(8, 8)) # plt.plot(np.concatenate((points[:,0], err0[:,0], err[:,0], points[:,0]*err[:,0]))) plt.subplot(2, 2, 1) plt.plot(points[:, 0]) plt.title('original', fontsize=12) plt.subplot(2, 2, 2) plt.plot(err0[:, 0]) plt.title('norm-err', fontsize=12) plt.subplot(2, 2, 3) plt.plot(err[:, 0]) plt.title('exp-err', fontsize=12) plt.subplot(2, 2, 4) plt.plot(noisy_points[:, 0]) plt.title('noisy', fontsize=12) plt.tight_layout() plt.show() assert False, 'exiting' return noisy_points, np.mean(devs), L def foreground_idxs(array, max_val=None): iy, ix = np.where(array < max_val) idxs = np.concatenate(((iy,), (ix,)), axis=0).T return idxs def interp2(array, x, y, max_val=None, max_dist=30, idxs=None, discard_bg=False): assert y < array.shape[0] and x < array.shape[1], 'out of bounds %s: %s' % (array.shape, (y, x)) v = array[int(y):int(y) + 2, int(x):int(x) + 2] xf = x - int(x) yf = y - int(y) w = np.array(( ((1 - yf) * (1 - xf), (1 - yf) * xf), (yf * (1 - xf), yf * xf), )) # ignore background depths if max_val is not None: idx = v.reshape(1, -1) < max_val * 0.999 else: idx = ~np.isnan(v.reshape(1, -1)) w_sum = np.sum(w.reshape(1, -1)[idx]) if w_sum > 0: # ignore background values val = np.sum(w.reshape(1, -1)[idx] * v.reshape(1, -1)[idx]) / w_sum elif discard_bg: return float('nan') else: # no foreground values in 2x2 matrix, find nearest foreground value if idxs is None: idxs = foreground_idxs(array, max_val) fallback = len(idxs) == 0 if not fallback: dist = np.linalg.norm(idxs - np.array((y, x)), axis=1) i = np.argmin(dist) val = array[idxs[i, 0], idxs[i, 1]] # print('\n%s, %s, %s, %s, %s, %s, %s'%(v, x,y,dist[i],idxs[i,1],idxs[i,0],val)) fallback = dist[i] > max_dist if fallback: val = np.sum(w * v) / np.sum(w) return val def solve_rotation(src_q, dst_q): """ q*src_q*q.conj() == dst_q, solve for q """ # based on http://web.cs.iastate.edu/~cs577/handouts/quaternion.pdf # and https://en.wikipedia.org/wiki/Quaternions_and_spatial_rotation#Pairs_of_unit_quaternions_as_rotations_in_4D_space # NOTE: not certain if works.. M = np.zeros((4, 4)) for i in range(len(src_q)): si = src_q[i] Pi = np.array(( (si.w, -si.x, -si.y, -si.z), (si.x, si.w, si.z, -si.y), (si.y, -si.z, si.w, si.x), (si.z, si.y, -si.x, si.w), )) qi = dst_q[i] Qi = np.array(( (qi.w, -qi.x, -qi.y, -qi.z), (qi.x, qi.w, -qi.z, qi.y), (qi.y, qi.z, qi.w, -qi.x), (qi.z, -qi.y, qi.x, qi.w), )) M += Pi.T * Qi w, v = np.linalg.eig(M) i = np.argmax(w) res_q = np.quaternion(*v[:, i]) # alt = v.dot(w) # print('%s,%s'%(res_q, alt)) # res_q = np.quaternion(*alt).normalized() return res_q def solve_q_bf(src_q, dst_q): qs = [] d = [] for res_q in ( np.quaternion(0, 0, 0, 1).normalized(), np.quaternion(0, 0, 1, 0).normalized(), np.quaternion(0, 0, 1, 1).normalized(), np.quaternion(0, 0, -1, 1).normalized(), np.quaternion(0, 1, 0, 0).normalized(), np.quaternion(0, 1, 0, 1).normalized(), np.quaternion(0, 1, 0, -1).normalized(), np.quaternion(0, 1, 1, 0).normalized(), np.quaternion(0, 1, -1, 0).normalized(), np.quaternion(0, 1, 1, 1).normalized(), np.quaternion(0, 1, 1, -1).normalized(), np.quaternion(0, 1, -1, 1).normalized(), np.quaternion(0, 1, -1, -1).normalized(), np.quaternion(1, 0, 0, 1).normalized(), np.quaternion(1, 0, 0, -1).normalized(), np.quaternion(1, 0, 1, 0).normalized(), np.quaternion(1, 0, -1, 0).normalized(), np.quaternion(1, 0, 1, 1).normalized(), np.quaternion(1, 0, 1, -1).normalized(), np.quaternion(1, 0, -1, 1).normalized(), np.quaternion(1, 0, -1, -1).normalized(), np.quaternion(1, 1, 0, 0).normalized(), np.quaternion(1, -1, 0, 0).normalized(), np.quaternion(1, 1, 0, 1).normalized(), np.quaternion(1, 1, 0, -1).normalized(), np.quaternion(1, -1, 0, 1).normalized(), np.quaternion(1, -1, 0, -1).normalized(), np.quaternion(1, 1, 1, 0).normalized(), np.quaternion(1, 1, -1, 0).normalized(), np.quaternion(1, -1, 1, 0).normalized(), np.quaternion(1, -1, -1, 0).normalized(), np.quaternion(1, 1, 1, -1).normalized(), np.quaternion(1, 1, -1, 1).normalized(), np.quaternion(1, 1, -1, -1).normalized(), np.quaternion(1, -1, 1, 1).normalized(), np.quaternion(1, -1, 1, -1).normalized(), np.quaternion(1, -1, -1, 1).normalized(), np.quaternion(1, -1, -1, -1).normalized(), ): tq = res_q * src_q * res_q.conj() qs.append(res_q) # d.append(1-np.array((tq.w, tq.x, tq.y, tq.z)).dot(np.array((dst_q.w, dst_q.x, dst_q.y, dst_q.z)))**2) d.append(angle_between_q(tq, dst_q)) i = np.argmin(d) return qs[i] def hover_annotate(fig, ax, line, annotations): annot = ax.annotate("", xy=(0, 0), xytext=(-20, 20), textcoords="offset points", bbox=dict(boxstyle="round", fc="w"), arrowprops=dict(arrowstyle="->")) annot.set_visible(False) def update_annot(ind): idx = ind["ind"][0] try: # for regular plots x, y = line.get_data() annot.xy = (x[idx], y[idx]) except AttributeError: # for scatter plots annot.xy = tuple(line.get_offsets()[idx]) text = ", ".join([annotations[n] for n in ind["ind"]]) annot.set_text(text) annot.get_bbox_patch().set_alpha(0.4) def hover(event): vis = annot.get_visible() if event.inaxes == ax: cont, ind = line.contains(event) if cont: update_annot(ind) annot.set_visible(True) fig.canvas.draw_idle() else: if vis: annot.set_visible(False) fig.canvas.draw_idle() fig.canvas.mpl_connect("motion_notify_event", hover) def plot_vectors(pts3d, scatter=True, conseq=True, neg_z=True): import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D fig = plt.figure() ax = Axes3D(fig) if scatter: ax.scatter(pts3d[:, 0], pts3d[:, 1], pts3d[:, 2]) else: if conseq: ax.set_prop_cycle('color', map(lambda c: '%f' % c, np.linspace(1, 0, len(pts3d)))) for i, v1 in enumerate(pts3d): if v1 is not None: ax.plot((0, v1[0]), (0, v1[1]), (0, v1[2])) ax.set_xlabel('X') ax.set_ylabel('Y') ax.set_zlabel('Z') if neg_z: ax.view_init(90, -90) else: ax.view_init(-90, -90) plt.show() def numeric(s): try: float(s) except ValueError: return False return True def pseudo_huber_loss(a, delta): # from https://en.wikipedia.org/wiki/Huber_loss # first +1e-15 is to avoid divide by zero, second to avoid loss becoming zero if delta > 1e7 due to float precision return delta ** 2 * (np.sqrt(1 + a ** 2 / (delta ** 2 + 1e-15)) - 1 + 1e-15) def fixed_precision(val, precision, as_str=False): if val == 0: return ('%%.%df' % precision) % val if as_str else val d = math.ceil(math.log10(abs(val))) - precision c = 10 ** d fp_val = round(val / c) * c return ('%%.%df' % max(0, -d)) % fp_val if as_str else fp_val def plot_quats(quats, conseq=True, wait=True): import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D fig = plt.figure() ax = Axes3D(fig) ax.set_xlim(-1, 1) ax.set_ylim(-1, 1) ax.set_zlim(-1, 1) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') if conseq: ax.set_prop_cycle('color', map(lambda c: '%f' % c, np.linspace(1, 0, len(quats)))) for i, q in enumerate(quats): if q is not None: lat, lon, _ = q_to_ypr(q) v1 = spherical2cartesian(lat, lon, 1) v2 = (v1 + normalize_v(np.cross(np.cross(v1, np.array([0, 0, 1])), v1)) * 0.1) * 0.85 v2 = q_times_v(q, v2) ax.plot((0, v1[0], v2[0]), (0, v1[1], v2[1]), (0, v1[2], v2[2])) while (wait and not plt.waitforbuttonpress()): pass def plot_poses(poses, conseq=True, wait=True, arrow_len=1): import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D fig = plt.figure() ax = Axes3D(fig) ax.set_xlabel('x') ax.set_ylabel('y') ax.set_zlabel('z') if conseq: plt.hsv() # ax.set_prop_cycle('color', map(lambda c: '%f' % c, np.linspace(.7, 0, len(poses)))) for i, pose in enumerate(poses): if pose is not None: q = np.quaternion(*pose[3:]) lat, lon, _ = q_to_ypr(q) v1 = spherical2cartesian(lat, lon, 1) * arrow_len v2 = (v1 + normalize_v(np.cross(np.cross(v1, np.array([0, 0, 1])), v1)) * 0.1 * arrow_len) * 0.85 v2 = q_times_v(q, v2) ax.plot((pose[0], v1[0], v2[0]), (pose[1], v1[1], v2[1]), (pose[2], v1[2], v2[2])) while (wait and not plt.waitforbuttonpress()): pass # # Not sure if unitbase_to_q works, haven't deleted just in case still need: # # def unitbase_to_q(b_dst, b_src = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]): # # based on http://stackoverflow.com/questions/16648452/calculating-\ # # quaternion-for-transformation-between-2-3d-cartesian-coordinate-syst # # , which is based on http://dx.doi.org/10.1117/12.57955 # # M = np.zeros((3, 3)) # # for i, v in enumerate(b_src): # x = np.matrix(np.outer(v, b_dst[i])) # M = M + x # # N11 = M[0, 0] + M[1, 1] + M[2, 2] # N22 = M[0, 0] - M[1, 1] - M[2, 2] # N33 = -M[0, 0] + M[1, 1] - M[2, 2] # N44 = -M[0, 0] - M[1, 1] + M[2, 2] # N12 = M[1, 2] - M[2, 1] # N13 = M[2, 0] - M[0, 2] # N14 = M[0, 1] - M[1, 0] # N21 = N12 # N23 = M[0, 1] + M[1, 0] # N24 = M[2, 0] + M[0, 2] # N31 = N13 # N32 = N23 # N34 = M[1, 2] + M[2, 1] # N41 = N14 # N42 = N24 # N43 = N34 # # N=np.matrix([[N11, N12, N13, N14],\ # [N21, N22, N23, N24],\ # [N31, N32, N33, N34],\ # [N41, N42, N43, N44]]) # # values, vectors = np.linalg.eig(N) # quat = vectors[:, np.argmax(values)] # #quat = np.array(quat).reshape(-1,).tolist() # # return np.quaternion(*quat) import tracemalloc import os import linecache def display_top(top_stats, key_type='lineno', limit=10): # snapshot = snapshot.filter_traces(( # tracemalloc.Filter(False, "<frozen importlib._bootstrap>"), # tracemalloc.Filter(False, "<unknown>"), # )) # top_stats = snapshot.statistics(key_type, cumulative=True) print("Top %s lines" % limit) for index, stat in enumerate(top_stats[:limit], 1): frame = stat.traceback[0] # replace "/path/to/module/file.py" with "module/file.py" filename = os.sep.join(frame.filename.split(os.sep)[-2:]) print("#%s: %s:%s: %.1f MB (x%.0f)" % (index, filename, frame.lineno, stat.size / 1024 / 1024, stat.count)) line = linecache.getline(frame.filename, frame.lineno).strip() if line: print(' %s' % line) other = top_stats[limit:] if other: size = sum(stat.size for stat in other) print("%s other: %.1f MB" % (len(other), size / 1024 / 1024)) total = sum(stat.size for stat in top_stats) print("Total allocated size: %.1f MB" % (total / 1024 / 1024)) def show_progress(tot, i): digits = int(math.ceil(math.log10(tot + 1))) if i == 0: print('%s/%d' % ('0' * digits, tot), end='', flush=True) else: print(('%s%0' + str(digits) + 'd/%d') % ('\b' * (digits * 2 + 1), i + 1, tot), end='', flush=True) def smooth1d(xt, x, Y, weight_fun=lambda d: 0.9 ** abs(d)): if xt.ndim != 1 or x.ndim != 1: raise ValueError("smooth1d only accepts 1 dimension arrays for location") if x.shape[0] != Y.shape[0]: raise ValueError("different lenght x and Y") D = np.repeat(np.expand_dims(xt, 1), len(x), axis=1) - np.repeat(np.expand_dims(x, 0), len(xt), axis=0) weights = np.array(list(map(weight_fun, D.flatten()))).reshape(D.shape) Yt = np.sum(Y * weights, axis=1) / np.sum(weights, axis=1) return Yt

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7c2bf254c4e2082b3c9d6ed73d3f8891d0fa09df

4,245

py

Python

cirtorch/filters/sobel.py

Tarekbouamer/Image-Retrieval-for-Image-Based-Localization

fcad9af4f558bebb3cbec1d08e49603a452f439d

[ "BSD-3-Clause" ]

3

2021-01-15T13:58:22.000Z

2021-01-22T00:03:34.000Z

cirtorch/filters/sobel.py

Tarekbouamer/Image-Retrieval-for-Image-Based-Localization

fcad9af4f558bebb3cbec1d08e49603a452f439d

[ "BSD-3-Clause" ]

null

cirtorch/filters/sobel.py

Tarekbouamer/Image-Retrieval-for-Image-Based-Localization

fcad9af4f558bebb3cbec1d08e49603a452f439d

[ "BSD-3-Clause" ]

null

import torch import torch.nn as nn import torch.nn.functional as F from .kernels import ( get_spatial_gradient_kernel2d, get_spatial_gradient_kernel3d, normalize_kernel2d ) def spatial_gradient(input, mode='sobel', order=1, normalized=True): """ Computes the first order image derivative in both x and y using a Sobel operator. """ if not len(input.shape) == 4: raise ValueError("Invalid input shape, we expect BxCxHxW. Got: {}" .format(input.shape)) # allocate kernel kernel = get_spatial_gradient_kernel2d(mode, order) if normalized: kernel = normalize_kernel2d(kernel) # prepare kernel b, c, h, w = input.shape tmp_kernel = kernel.to(input).detach() tmp_kernel = tmp_kernel.unsqueeze(1).unsqueeze(1) # convolve input tensor with sobel kernel kernel_flip = tmp_kernel.flip(-3) # Pad with "replicate for spatial dims, but with zeros for channel spatial_pad = [ kernel.size(1) // 2, kernel.size(1) // 2, kernel.size(2) // 2, kernel.size(2) // 2 ] out_channels = 3 if order == 2 else 2 padded_inp = F.pad(input.reshape(b * c, 1, h, w), spatial_pad, 'replicate')[:, :, None] return F.conv3d(padded_inp, kernel_flip, padding=0).view(b, c, out_channels, h, w) def spatial_gradient3d(input, mode='diff', order=1): """ Computes the first and second order volume derivative in x, y and d using a diff operator. """ if not len(input.shape) == 5: raise ValueError("Invalid input shape, we expect BxCxDxHxW. Got: {}" .format(input.shape)) # allocate kernel kernel = get_spatial_gradient_kernel3d(mode, order) # prepare kernel b, c, d, h, w = input.shape tmp_kernel = kernel.to(input).detach() tmp_kernel = tmp_kernel.repeat(c, 1, 1, 1, 1) # convolve input tensor with grad kernel kernel_flip = tmp_kernel.flip(-3) # Pad with "replicate for spatial dims, but with zeros for channel spatial_pad = [ kernel.size(2) // 2, kernel.size(2) // 2, kernel.size(3) // 2, kernel.size(3) // 2, kernel.size(4) // 2, kernel.size(4) // 2 ] out_ch = 6 if order == 2 else 3 return F.conv3d(F.pad( input, spatial_pad, 'replicate'), kernel_flip, padding=0, groups=c).view(b, c, out_ch, d, h, w) def sobel(input, normalized=True, eps=1e-6): """ Computes the Sobel operator and returns the magnitude per channel. """ if not len(input.shape) == 4: raise ValueError("Invalid input shape, we expect BxCxHxW. Got: {}" .format(input.shape)) # comput the x/y gradients edges = spatial_gradient(input, normalized=normalized) # unpack the edges gx = edges[:, :, 0] gy = edges[:, :, 1] # compute gradient maginitude magnitude = torch.sqrt(gx * gx + gy * gy + eps) return magnitude class SpatialGradient(nn.Module): """ Computes the first order image derivative in both x and y using a Sobel operator. """ def __init__(self, mode='sobel', order=1, normalized=True): super(SpatialGradient, self).__init__() self.normalized = normalized self.order = order self.mode = mode def forward(self, input): return spatial_gradient(input, self.mode, self.order, self.normalized) class SpatialGradient3d(nn.Module): """ Computes the first and second order volume derivative in x, y and d using a diff operator. """ def __init__(self, mode='diff', order=1): super(SpatialGradient3d, self).__init__() self.order = order self.mode = mode self.kernel = get_spatial_gradient_kernel3d(mode, order) def forward(self, input): return spatial_gradient3d(input, self.mode, self.order) class Sobel(nn.Module): """ Computes the Sobel operator and returns the magnitude per channel. """ def __init__(self, normalized=True, eps=1e-6): super(Sobel, self).__init__() self.normalized = normalized self.eps = eps def forward(self, input): return sobel(input, self.normalized, self.eps)

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0.033898

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0.54584

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0.389445

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0

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null

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null

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7c2c03c407ba0a2ba9a613836bc2fb4601d6b4a8

896

py

Python

PythonCookbook/concurrent_test/findrobots.py

xu6148152/Binea_Python_Project

d943eb5f4685d08f080b372dcf1a7cbd5d63efed

[ "MIT" ]

null

PythonCookbook/concurrent_test/findrobots.py

xu6148152/Binea_Python_Project

d943eb5f4685d08f080b372dcf1a7cbd5d63efed

[ "MIT" ]

null

PythonCookbook/concurrent_test/findrobots.py

xu6148152/Binea_Python_Project

d943eb5f4685d08f080b372dcf1a7cbd5d63efed

[ "MIT" ]

null

# -*- encoding: utf-8 -*- import gzip import io import glob from concurrent import futures def find_robots(filename): ''' Find all of the hosts that access robots.txt in a single log file ''' robots = set() with gzip.open(filename) as f: for line in io.TextIOWrapper(f, encoding='ascii'): fields = line.split() if fields[6] == '/robots.txt': robots.add(fields[0]) return robots def find_all_robots(logdir): ''' Find all hosts across and entire sequence of files ''' files = glob.glob(logdir + '/*.log.gz') all_robots = set() with futures.ProcessPoolExecutor() as pool: for robots in pool.map(find_robots, files): all_robots.update(robots) return all_robots if __name__ == '__main__': robots = find_all_robots('logs') for ipaddr in robots: print(ipaddr)

23.578947

69

0.618304

118

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7c2c664c7e1b0b10556e368192b5c6b6dfeac1d6

13,634

py

Python

cnnblstm_with_adabn/cnnblstm_with_adabn.py

Fassial/Air-Writing-with-TL

9b9047c5bd5aef3a869e2d5166be1c0cf0c5ccf0

[ "MIT" ]

1

2021-06-16T16:45:01.000Z

cnnblstm_with_adabn/cnnblstm_with_adabn.py

Fassial/Air-Writing-with-TL

9b9047c5bd5aef3a869e2d5166be1c0cf0c5ccf0

[ "MIT" ]

null

cnnblstm_with_adabn/cnnblstm_with_adabn.py

Fassial/Air-Writing-with-TL

9b9047c5bd5aef3a869e2d5166be1c0cf0c5ccf0

[ "MIT" ]

1

2020-04-21T01:31:26.000Z

import os import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable import numpy as np import matplotlib.pyplot as plt # local model import sys sys.path.append("../network") import Coral from lstm import LSTMHardSigmoid from AdaBN import AdaBN sys.path.append("../network/AutoEncoder") import AutoEncoder class cnnblstm_with_adabn(nn.Module): PARAMS_FILE = "params.pkl" PARAMS_AE = "params_ae.pkl" NET1_ADABN = "net1_adabn" NET2_ADABN = "net2_adabn" NET3_ADABN = "net3_adabn" def __init__(self, time_steps = 800, n_features = 3, n_outputs = 10, use_cuda = False, params_dir = "./params", enable_CORAL = False): super(cnnblstm_with_adabn, self).__init__() self.time_steps = time_steps self.n_features = n_features self.n_outputs = n_outputs self.use_cuda = use_cuda self.params_dir = params_dir if not os.path.exists(self.params_dir): os.mkdir(self.params_dir) self.enable_CORAL = enable_CORAL self.n_filters = 128 self.kernel_size = 15 self.n_hidden = 150 # 150 self.n_layers = 1 self.bidirectional = True # self.ae = AutoEncoder.load_AE(type = "ConvAE", time_steps = self.time_steps, n_features = self.n_features, use_cuda = self.use_cuda, params_pkl = os.path.join(self.params_dir, cnnblstm_with_adabn.PARAMS_AE)) # build net1 cnn self.net1 = nn.Sequential( nn.Conv1d(in_channels = self.n_features, out_channels = self.n_filters, kernel_size = self.kernel_size), # nn.Conv1d(in_channels = self.ae.n_filters3, out_channels = self.n_filters, kernel_size = self.kernel_size), nn.ReLU(), # nn.Sigmoid(), nn.Dropout(p = 0.5), nn.MaxPool1d(kernel_size = 2) ) # build net1_adabn self.net1_adabn = AdaBN(self.n_filters, variables_dir = os.path.join(self.params_dir, cnnblstm_with_adabn.NET1_ADABN), use_cuda = self.use_cuda) # build net2 blstm # self.net2 = nn.LSTM(input_size = self.n_filters, hidden_size = self.n_hidden, num_layers = self.n_layers, dropout = 0.2, batch_first = True, bidirectional = self.bidirectional, bias = True) self.net2 = LSTMHardSigmoid(input_size = self.n_filters, hidden_size = self.n_hidden, num_layers = self.n_layers, dropout = 0.2, batch_first = True, bidirectional = self.bidirectional, bias = True) # build net2_adabn if self.bidirectional: n_blstm_output = self.n_hidden * 2 else: n_blstm_output = self.n_hidden self.net2_adabn = AdaBN(n_blstm_output, variables_dir = os.path.join(self.params_dir, cnnblstm_with_adabn.NET2_ADABN), use_cuda = self.use_cuda) # build net3 fc self.net3 = nn.Sequential( nn.Linear(n_blstm_output, 50, bias = True), nn.ReLU(), # nn.Sigmoid(), ) # build net3_adabn self.net3_adabn = AdaBN(50, variables_dir = os.path.join(self.params_dir, cnnblstm_with_adabn.NET3_ADABN), use_cuda = self.use_cuda) # build net4 fc self.net4 = nn.Sequential( nn.Dropout(p = 0.2), nn.Linear(50, self.n_outputs, bias = True), nn.Softmax(dim = 1) ) def init_hidden(self, batch_size): """ init blstm's hidden states """ if self.bidirectional: n_layers = self.n_layers * 2 else: n_layers = self.n_layers if self.use_cuda: hidden_state = torch.zeros(n_layers, batch_size, self.n_hidden).cuda() cell_state = torch.zeros(n_layers, batch_size, self.n_hidden).cuda() else: hidden_state = torch.zeros(n_layers, batch_size, self.n_hidden) cell_state = torch.zeros(n_layers, batch_size, self.n_hidden) self.hidden = (hidden_state, cell_state) def reset_parameters(self): """ temp useless Here we reproduce Keras default initialization weights for consistency with Keras version """ # get weights & bias set net1_weights = ((name, param.data) for name, param in self.named_parameters() if (("weight" in name) and (("net1" in name) and ("net1_adabn" not in name)))) net1_biases = ((name, param.data) for name, param in self.named_parameters() if (("bias" in name) and (("net1" in name) and ("net1_adabn" not in name)))) # net2_weights = ((name, param.data) for name, param in self.named_parameters() if (("weight" in name) and (("net2" in name) and ("net2_adabn" not in name)))) # net2_biases = ((name, param.data) for name, param in self.named_parameters() if (("bias" in name) and (("net2" in name) and ("net2_adabn" not in name)))) net3_weights = ((name, param.data) for name, param in self.named_parameters() if (("weight" in name) and (("net3" in name) and ("net3_adabn" not in name)))) net3_biases = ((name, param.data) for name, param in self.named_parameters() if (("bias" in name) and (("net3" in name) and ("net3_adabn" not in name)))) net4_weights = ((name, param.data) for name, param in self.named_parameters() if (("weight" in name) and (("net4" in name) and ("net4_adabn" not in name)))) net4_biases = ((name, param.data) for name, param in self.named_parameters() if (("bias" in name) and (("net4" in name) and ("net4_adabn" not in name)))) # init weights & bias # self.ae.reset_parameters() for name, params_data in net1_weights: # print(name) nn.init.xavier_uniform_(params_data) for name, params_data in net1_biases: nn.init.constant_(params_data, 0) self.net1_adabn.reset_parameters() self.net2.reset_parameters() # lstm reset parameters self.net2_adabn.reset_parameters() for name, params_data in net3_weights: nn.init.xavier_uniform_(params_data) for name, params_data in net3_biases: nn.init.constant_(params_data, 0) self.net3_adabn.reset_parameters() for name, params_data in net4_weights: nn.init.xavier_uniform_(params_data) for name, params_data in net4_biases: nn.init.constant_(params_data, 0) def forward(self, input): """ compute the output of input according to the entire network model """ # print(input.shape) # AutoEncoder # input = self.ae.encoder(input) # input = self.ae(input) # MaxPool1d maxPool1d_output = self.net1(input) # maxPool1d_adabn_output = maxPool1d_output maxPool1d_adabn_output, maxPool1d_output = self.net1_adabn(maxPool1d_output), None maxPool1d_adabn_t_output = maxPool1d_adabn_output.permute(0, 2, 1).contiguous() # BiLSTM (bilstm_output, _), maxPool1d_adabn_t_output = self.net2(maxPool1d_adabn_t_output, None), None # MaxPooling1D time_steps bilstm_output = bilstm_output.permute(0, 2, 1) maxPooling_output, bilstm_output = F.max_pool1d(bilstm_output, kernel_size = bilstm_output.size(2)).squeeze(2), None # maxPooling_adabn_output = maxPooling_output maxPooling_adabn_output, maxPooling_output = self.net2_adabn(maxPooling_output), None # get classifier net3_output, maxPooling_adabn_output = self.net3(maxPooling_adabn_output), None net3_adabn_output, net3_output = self.net3_adabn(net3_output), None linear2_softmax_output, net3_adabn_output = self.net4(net3_adabn_output), None return linear2_softmax_output def update_adabn_running_stats(self): """ update adabn running states, update mu_j with mu_j_next to start next round """ self.net1_adabn.update_running_stats() self.net2_adabn.update_running_stats() self.net3_adabn.update_running_stats() def trainAllLayers(self, train_x, train_y, test_x = None, learning_rate = 0.001, n_epoches = 20, batch_size = 20, shuffle = True): """ train all layers of network model """ # print(os.environ["CUDA_VISIBLE_DEVICES"]) # CORAL if self.enable_CORAL: if test_x == None: print("ERROR: (in cnnblstm_with_adabn.trainAllLayers) test_x == None!") return # review train_x & test_x train_x = train_x.view(-1, self.time_steps * self.n_features) test_x = test_x.view(-1, self.time_steps * self.n_features) # get CORAL(train_x, test_x) train_x = Coral.CORAL_torch(train_x, test_x) # review train_x train_x = train_x.view(-1, self.n_features, self.time_steps) # optimize all cnn parameters params = [{"params": model.parameters()} for model in self.children() if model not in [self.ae]] optimizer = torch.optim.Adam(params, lr = learning_rate) # the target label is not one-hotted loss_func = nn.CrossEntropyLoss() # init params self.reset_parameters() # load params self.load_params() # set train mode True self.train() # get parallel model parallel_cba = self if self.use_cuda: # print("we use cuda!") parallel_cba = torch.nn.DataParallel(self, device_ids = range(torch.cuda.device_count())) # parallel_cba = parallel_cba.cuda() # if use_cuda if self.use_cuda: train_x = train_x.cuda() train_y = train_y.cuda() """ # get autoencoder self.ae = AutoEncoder.train_AE(self.ae, train_x, train_x, n_epoches = 20) self.ae.save_params() """ # get train_data train_data = torch.utils.data.TensorDataset(train_x, train_y) # Data Loader for easy mini-batch return in training train_loader = torch.utils.data.DataLoader(dataset = train_data, batch_size = batch_size, shuffle = shuffle) # training and testing for epoch in range(n_epoches): # init loss & acc train_loss = 0 train_acc = 0 for step, (b_x, b_y) in enumerate(train_loader): # gives batch data b_x = b_x.view(-1, self.n_features, self.time_steps) # reshape x to (batch, n_features, time_step) if self.use_cuda: b_x, b_y = Variable(b_x).cuda(), Variable(b_y).cuda() else: b_x, b_y = Variable(b_x), Variable(b_y) """ # get hidden if self.use_cuda: self.init_hidden(b_x.size(0) // torch.cuda.device_count()) else: self.init_hidden(b_x.size(0)) """ # update adabn running stats self.update_adabn_running_stats() # get output output = parallel_cba(b_x) # CNN_BLSTM output # get loss loss = loss_func(output, b_y) # cross entropy loss train_loss += loss.item() * len(b_y) _, pre = torch.max(output, 1) num_acc = (pre == b_y).sum() train_acc += num_acc.item() # backward optimizer.zero_grad() # clear gradients for this training step loss.backward() # backpropagation, compute gradients optimizer.step() # apply gradients # print loss # if (step + 1) % 5 == 0: # print("[{}/{}], train loss is: {:.6f}, train acc is: {:.6f}".format(step, len(train_loader), train_loss / ((step + 1) * batch_size), train_acc / ((step + 1) * batch_size))) print("[{}/{}], train loss is: {:.6f}, train acc is: {:.6f}".format(len(train_loader), len(train_loader), train_loss / (len(train_loader) * batch_size), train_acc / (len(train_loader) * batch_size))) # save params self.save_params() # print("train finish!") def getTestAccuracy(self, test_x, test_y): """ test network model with test set """ # init params self.reset_parameters() # load params self.load_params() # set eval self.eval() # get parallel model parallel_cba = self if self.use_cuda: # print("we use cuda!") parallel_cba = torch.nn.DataParallel(self, device_ids = range(torch.cuda.device_count())) # parallel_cba = parallel_cba.cuda() # cuda test_data with torch.no_grad(): if self.use_cuda: test_x, test_y = Variable(test_x).cuda(), Variable(test_y).cuda() else: test_x, test_y = Variable(test_x), Variable(test_y) """ # get hidden if self.use_cuda: self.init_hidden(test_x.size(0) // torch.cuda.device_count()) else: self.init_hidden(test_x.size(0)) """ # update adabn running stats self.update_adabn_running_stats() # get output with torch.no_grad(): output = parallel_cba(test_x) # print(output) prediction = torch.max(output, 1)[1] pred_y = prediction.cpu().data.numpy() # print(pred_y) target_y = test_y.cpu().data.numpy() # print(test_y) accuracy = float((pred_y == target_y).astype(int).sum()) / float(target_y.size) # print("Accuracy: ", str(accuracy)) return accuracy def save_params(self): """ save params & adabn's inner stats """ self.save_adabn_variables() torch.save(self.state_dict(), os.path.join(self.params_dir, cnnblstm_with_adabn.PARAMS_FILE)) # self.ae.save_params() # print("save_params success!") def save_adabn_variables(self): """ save adabn's inner stats """ self.net1_adabn.save_attrs() self.net2_adabn.save_attrs() self.net3_adabn.save_attrs() def load_params(self): """ load params & adabn's inner stats """ self.load_adabn_variables() if os.path.exists(os.path.join(self.params_dir, cnnblstm_with_adabn.PARAMS_FILE)): if self.use_cuda: self.load_state_dict(torch.load(os.path.join(self.params_dir, cnnblstm_with_adabn.PARAMS_FILE), map_location = torch.device('cuda'))) else: self.load_state_dict(torch.load(os.path.join(self.params_dir, cnnblstm_with_adabn.PARAMS_FILE), map_location = torch.device('cpu'))) # print("load_params success!") # self.ae.load_params() def load_adabn_variables(self): """ load adabn's inner stats """ self.net1_adabn.load_attrs() self.net2_adabn.load_attrs() self.net3_adabn.load_attrs() def get_model(self, pre_trained = False): """ get pretrained model """ if pre_trained: self.load_params() return self if __name__ == '__main__': use_cuda = torch.cuda.is_available() if use_cuda: cnnblstm = cnnblstm_with_adabn(use_cuda = use_cuda).cuda() else: cnnblstm = cnnblstm_with_adabn(use_cuda = use_cuda) print(cnnblstm) # get train_x, train_y train_x = torch.rand(20, 3, 800, dtype = torch.float32) train_y = torch.randint(10, (20, ), dtype = torch.int64) # train_y = torch.LongTensor(20, 1).random_() % 10 print(train_x.type()) # train_y = torch.zeros(20, 10).scatter_(1, train_y, 1) print(train_y) train_data = torch.utils.data.TensorDataset(train_x, train_y) cnnblstm.trainAllLayers(train_data)

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7c2d2c77ae28e087d253ce05110db6593a6b0fcc

26,658

py

Python

src/emmental/model.py

woffett/emmental

87884fcd89662cca45f0ea0f78cff73380cc47c8

[ "MIT" ]

null

src/emmental/model.py

woffett/emmental

87884fcd89662cca45f0ea0f78cff73380cc47c8

[ "MIT" ]

null

src/emmental/model.py

woffett/emmental

87884fcd89662cca45f0ea0f78cff73380cc47c8

[ "MIT" ]

null

"""Emmental model.""" import itertools import logging import os from collections import defaultdict from collections.abc import Iterable from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Union import numpy as np import torch from numpy import ndarray from torch import Tensor, nn as nn from torch.nn import ModuleDict from tqdm import tqdm from emmental.data import EmmentalDataLoader from emmental.meta import Meta from emmental.scorer import Scorer from emmental.task import EmmentalTask from emmental.utils.utils import construct_identifier, move_to_device, prob_to_pred logger = logging.getLogger(__name__) class EmmentalModel(nn.Module): """A class to build multi-task model. Args: name: Name of the model, defaults to None. tasks: A task or a list of tasks. """ def __init__( self, name: Optional[str] = None, tasks: Optional[Union[EmmentalTask, List[EmmentalTask]]] = None, ) -> None: """Initialize EmmentalModel.""" super().__init__() self.name = name if name is not None else type(self).__name__ # Initiate the model attributes self.module_pool: ModuleDict = ModuleDict() self.task_names: Set[str] = set() self.task_flows: Dict[str, Any] = dict() # TODO: make it concrete self.loss_funcs: Dict[str, Callable] = dict() self.output_funcs: Dict[str, Callable] = dict() self.scorers: Dict[str, Scorer] = dict() self.action_outputs: Dict[ str, Optional[List[Union[Tuple[str, str], Tuple[str, int]]]] ] = dict() self.weights: Dict[str, float] = dict() # Build network with given tasks if tasks is not None: self.add_tasks(tasks) if Meta.config["meta_config"]["verbose"]: logger.info( f"Created emmental model {self.name} that contains " f"task {self.task_names}." ) # Move model to specified device self._move_to_device() def _move_to_device(self) -> None: """Move model to specified device.""" if Meta.config["model_config"]["device"] != -1: if torch.cuda.is_available(): device = ( f"cuda:{Meta.config['model_config']['device']}" if isinstance(Meta.config["model_config"]["device"], int) else Meta.config["model_config"]["device"] ) if Meta.config["meta_config"]["verbose"]: logger.info(f"Moving model to GPU ({device}).") self.to(torch.device(device)) else: if Meta.config["meta_config"]["verbose"]: logger.info("No cuda device available. Switch to cpu instead.") def _to_dataparallel(self) -> None: for key in self.module_pool.keys(): self.module_pool[key] = torch.nn.DataParallel(self.module_pool[key]) def _to_distributed_dataparallel(self) -> None: for key in self.module_pool.keys(): self.module_pool[ key ] = torch.nn.parallel.DistributedDataParallel( # type: ignore self.module_pool[key], device_ids=[Meta.config["learner_config"]["local_rank"]], output_device=Meta.config["learner_config"]["local_rank"], find_unused_parameters=True, ) def add_tasks(self, tasks: Union[EmmentalTask, List[EmmentalTask]]) -> None: """Build the MTL network using all tasks. Args: tasks: A task or a list of tasks. """ if not isinstance(tasks, Iterable): tasks = [tasks] for task in tasks: self.add_task(task) def add_task(self, task: EmmentalTask) -> None: """Add a single task into MTL network. Args: task: A task to add. """ if not isinstance(task, EmmentalTask): raise ValueError(f"Unrecognized task type {task}.") if task.name in self.task_names: raise ValueError( f"Found duplicate task {task.name}, different task should use " f"different task name." ) # Combine module_pool from all tasks for key in task.module_pool.keys(): if key in self.module_pool.keys(): task.module_pool[key] = self.module_pool[key] else: self.module_pool[key] = task.module_pool[key] # Collect task name self.task_names.add(task.name) # Collect task flow self.task_flows[task.name] = task.task_flow # Collect loss function self.loss_funcs[task.name] = task.loss_func # Collect output function self.output_funcs[task.name] = task.output_func # Collect action outputs self.action_outputs[task.name] = task.action_outputs # Collect scorer self.scorers[task.name] = task.scorer # Collect weight self.weights[task.name] = task.weight # Move model to specified device self._move_to_device() def update_task(self, task: EmmentalTask) -> None: """Update a existing task in MTL network. Args: task: A task to update. """ # Update module_pool with task for key in task.module_pool.keys(): # Update the model's module with the task's module self.module_pool[key] = task.module_pool[key] # Update task flow self.task_flows[task.name] = task.task_flow # Update loss function self.loss_funcs[task.name] = task.loss_func # Update output function self.output_funcs[task.name] = task.output_func # Update action outputs self.action_outputs[task.name] = task.action_outputs # Update scorer self.scorers[task.name] = task.scorer # Update weight self.weights[task.name] = task.weight # Move model to specified device self._move_to_device() def remove_task(self, task_name: str) -> None: """Remove a existing task from MTL network. Args: task_name: The task name to remove. """ if task_name not in self.task_flows: if Meta.config["meta_config"]["verbose"]: logger.info(f"Task ({task_name}) not in the current model, skip...") return # Remove task by task_name if Meta.config["meta_config"]["verbose"]: logger.info(f"Removing Task {task_name}.") self.task_names.remove(task_name) del self.task_flows[task_name] del self.loss_funcs[task_name] del self.output_funcs[task_name] del self.action_outputs[task_name] del self.scorers[task_name] del self.weights[task_name] # TODO: remove the modules only associate with that task def __repr__(self) -> str: """Represent the model as a string.""" cls_name = type(self).__name__ return f"{cls_name}(name={self.name})" def flow(self, X_dict: Dict[str, Any], task_names: List[str]) -> Dict[str, Any]: """Forward based on input and task flow. Note: We assume that all shared modules from all tasks are based on the same input. Args: X_dict: The input data task_names: The task names that needs to forward. Returns: The output of all forwarded modules """ X_dict = move_to_device(X_dict, Meta.config["model_config"]["device"]) output_dict = dict(_input_=X_dict) # Call forward for each task for task_name in task_names: for action in self.task_flows[task_name]: if action["name"] not in output_dict: if action["inputs"]: try: input = [ output_dict[action_name][output_index] for action_name, output_index in action["inputs"] ] except Exception: raise ValueError(f"Unrecognized action {action}.") output = self.module_pool[action["module"]].forward(*input) else: output = self.module_pool[action["module"]].forward(output_dict) if isinstance(output, tuple): output = list(output) if not isinstance(output, list) and not isinstance(output, dict): output = [output] output_dict[action["name"]] = output return output_dict def forward( # type: ignore self, uids: List[str], X_dict: Dict[str, Any], Y_dict: Dict[str, Tensor], task_to_label_dict: Dict[str, str], return_action_outputs=False, ) -> Union[ Tuple[ Dict[str, List[str]], Dict[str, ndarray], Dict[str, ndarray], Dict[str, ndarray], Dict[str, Dict[str, ndarray]], ], Tuple[ Dict[str, List[str]], Dict[str, ndarray], Dict[str, ndarray], Dict[str, ndarray], ], ]: """Forward function. Args: uids: The uids of input data. X_dict: The input data. Y_dict: The output data. task_to_label_dict: The task to label mapping. return_action_outputs: Whether return action_outputs or not, defaults to False. Returns: The (active) uids, loss, prob, gold, action_output (optional) in the batch of all tasks. """ uid_dict: Dict[str, List[str]] = defaultdict(list) loss_dict: Dict[str, ndarray] = defaultdict(float) gold_dict: Dict[str, ndarray] = defaultdict(list) prob_dict: Dict[str, ndarray] = defaultdict(list) out_dict: Dict[str, Dict[str, ndarray]] = defaultdict(lambda: defaultdict(list)) task_names = ( list(task_to_label_dict.keys()) if isinstance(task_to_label_dict, dict) else list(task_to_label_dict) ) output_dict = self.flow(X_dict, task_names) if Y_dict is not None: # Calculate logit and loss for each task for task_name, label_name in task_to_label_dict.items(): Y = Y_dict[label_name] # Select the active samples if Meta.config["learner_config"]["ignore_index"] is not None: if len(Y.size()) == 1: active = ( Y.detach() != Meta.config["learner_config"]["ignore_index"] ) else: active = torch.any( Y.detach() != Meta.config["learner_config"]["ignore_index"], dim=1, ) else: active = torch.BoolTensor([True] * Y.size()[0]) # type: ignore # Only calculate the loss when active example exists if active.any(): uid_dict[task_name] = [*itertools.compress(uids, active.numpy())] loss_dict[task_name] = self.loss_funcs[task_name]( output_dict, move_to_device( Y_dict[label_name], Meta.config["model_config"]["device"] ), move_to_device(active, Meta.config["model_config"]["device"]), ) prob_dict[task_name] = ( self.output_funcs[task_name](output_dict)[ move_to_device( active, Meta.config["model_config"]["device"] ) ] .cpu() .detach() .numpy() ) gold_dict[task_name] = Y_dict[label_name][active].cpu().numpy() if ( return_action_outputs and self.action_outputs[task_name] is not None ): for action_name, output_index in self.action_outputs[task_name]: out_dict[task_name][f"{action_name}_{output_index}"] = ( output_dict[action_name][output_index][ move_to_device( active, Meta.config["model_config"]["device"] ) ] .cpu() .detach() .numpy() ) else: # Calculate logit for each task for task_name in task_to_label_dict: uid_dict[task_name] = uids prob_dict[task_name] = ( self.output_funcs[task_name](output_dict).cpu().detach().numpy() ) if return_action_outputs and self.action_outputs[task_name] is not None: for action_name, output_index in self.action_outputs[task_name]: out_dict[task_name][f"{action_name}_{output_index}"] = ( output_dict[action_name][output_index] .cpu() .detach() .numpy() ) loss_dict = None gold_dict = None if return_action_outputs: return uid_dict, loss_dict, prob_dict, gold_dict, out_dict else: return uid_dict, loss_dict, prob_dict, gold_dict @torch.no_grad() def predict( self, dataloader: EmmentalDataLoader, return_preds: bool = False, return_action_outputs: bool = True, ) -> Dict[str, Any]: """Predict from dataloader. Args: dataloader: The dataloader to predict. return_preds: Whether return predictions or not, defaults to False. return_action_outputs: Whether return action_outputs or not, defaults to True. Returns: The result dict. """ self.eval() uid_dict: Dict[str, List[str]] = defaultdict(list) prob_dict: Dict[str, List[Union[ndarray, int, float]]] = defaultdict(list) pred_dict: Dict[str, List[ndarray]] = defaultdict(list) gold_dict: Dict[str, List[Union[ndarray, int, float]]] = defaultdict(list) out_dict: Dict[str, Dict[str, List[Union[ndarray, int, float]]]] = defaultdict( lambda: defaultdict(list) ) loss_dict: Dict[str, Union[ndarray, float]] = defaultdict(list) # type: ignore if not dataloader.is_learnable: gold_dict = None loss_dict = None # Collect dataloader information task_to_label_dict = dataloader.task_to_label_dict uid = dataloader.uid for batch_num, bdict in tqdm( enumerate(dataloader), total=len(dataloader), desc=f"Evaluating {dataloader.data_name} ({dataloader.split})", ): if isinstance(bdict, dict) == 1: X_bdict = bdict Y_bdict = None else: X_bdict, Y_bdict = bdict if not dataloader.is_learnable: Y_bdict = None if return_action_outputs: ( uid_bdict, loss_bdict, prob_bdict, gold_bdict, out_bdict, ) = self.forward( # type: ignore X_bdict[uid], X_bdict, Y_bdict, task_to_label_dict, return_action_outputs=return_action_outputs, ) else: ( uid_bdict, loss_bdict, prob_bdict, gold_bdict, ) = self.forward( # type: ignore X_bdict[uid], X_bdict, Y_bdict, task_to_label_dict, return_action_outputs=return_action_outputs, ) out_bdict = None for task_name in uid_bdict.keys(): uid_dict[task_name].extend(uid_bdict[task_name]) prob_dict[task_name].extend(prob_bdict[task_name]) if dataloader.is_learnable: gold_dict[task_name].extend(gold_bdict[task_name]) if len(loss_bdict[task_name].size()) == 0: if loss_dict[task_name] == []: loss_dict[task_name] = 0 loss_dict[task_name] += loss_bdict[task_name].item() * len( uid_bdict[task_name] ) else: loss_dict[task_name].extend( # type: ignore loss_bdict[task_name].cpu().numpy() ) if return_action_outputs and out_bdict: for task_name in out_bdict.keys(): for action_name in out_bdict[task_name].keys(): out_dict[task_name][action_name].extend( out_bdict[task_name][action_name] ) # Calculate average loss if dataloader.is_learnable: for task_name in uid_dict.keys(): if not isinstance(loss_dict[task_name], list): loss_dict[task_name] /= len(uid_dict[task_name]) res = { "uids": uid_dict, "golds": gold_dict, "probs": prob_dict, "losses": loss_dict, } if return_action_outputs: res["outputs"] = out_dict if return_preds: for task_name, prob in prob_dict.items(): pred_dict[task_name] = prob_to_pred(prob) res["preds"] = pred_dict return res @torch.no_grad() def score( self, dataloaders: Union[EmmentalDataLoader, List[EmmentalDataLoader]], return_average: bool = True, ) -> Dict[str, float]: """Score the data from dataloader. Args: dataloaders: The dataloaders to score. return_average: Whether to return average score. Returns: Score dict. """ self.eval() if not isinstance(dataloaders, list): dataloaders = [dataloaders] metric_score_dict = dict() if return_average: micro_score_dict: defaultdict = defaultdict(list) macro_score_dict: defaultdict = defaultdict(list) macro_loss_dict: defaultdict = defaultdict(list) for dataloader in dataloaders: if not dataloader.is_learnable: logger.warning( f"Dataloader {dataloader.data_name} doesn't have gold data, " f"continue..." ) continue predictions = self.predict(dataloader, return_preds=True) for task_name in predictions["uids"].keys(): metric_score = self.scorers[task_name].score( predictions["golds"][task_name], predictions["probs"][task_name], predictions["preds"][task_name], predictions["uids"][task_name], ) for metric_name, metric_value in metric_score.items(): identifier = construct_identifier( task_name, dataloader.data_name, dataloader.split, metric_name ) metric_score_dict[identifier] = metric_value # Store the loss identifier = construct_identifier( task_name, dataloader.data_name, dataloader.split, "loss" ) metric_score_dict[identifier] = np.mean( predictions["losses"][task_name] ) if return_average: # Collect average score identifier = construct_identifier( task_name, dataloader.data_name, dataloader.split, "average" ) metric_score_dict[identifier] = np.mean(list(metric_score.values())) micro_score_dict[dataloader.split].extend( list(metric_score.values()) ) macro_score_dict[dataloader.split].append( metric_score_dict[identifier] ) # Store the loss identifier = construct_identifier( task_name, dataloader.data_name, dataloader.split, "loss" ) macro_loss_dict[dataloader.split].append( metric_score_dict[identifier] ) if return_average: # Collect split-wise micro/macro average score for split in micro_score_dict.keys(): identifier = construct_identifier( "model", "all", split, "micro_average" ) metric_score_dict[identifier] = np.mean(micro_score_dict[split]) identifier = construct_identifier( "model", "all", split, "macro_average" ) metric_score_dict[identifier] = np.mean(macro_score_dict[split]) identifier = construct_identifier("model", "all", split, "loss") metric_score_dict[identifier] = np.mean(macro_loss_dict[split]) # Collect overall micro/macro average score/loss if len(micro_score_dict): identifier = construct_identifier( "model", "all", "all", "micro_average" ) metric_score_dict[identifier] = np.mean( list(itertools.chain.from_iterable(micro_score_dict.values())) ) if len(macro_score_dict): identifier = construct_identifier( "model", "all", "all", "macro_average" ) metric_score_dict[identifier] = np.mean( list(itertools.chain.from_iterable(macro_score_dict.values())) ) if len(macro_loss_dict): identifier = construct_identifier("model", "all", "all", "loss") metric_score_dict[identifier] = np.mean( list(itertools.chain.from_iterable(macro_loss_dict.values())) ) # TODO: have a better to handle global evaluation metric if Meta.config["learner_config"]["global_evaluation_metric_dict"]: global_evaluation_metric_dict = Meta.config["learner_config"][ "global_evaluation_metric_dict" ] for metric_name, metric in global_evaluation_metric_dict.items(): metric_score_dict[metric_name] = metric(metric_score_dict) return metric_score_dict def save(self, model_path: str) -> None: """Save the current model. Args: model_path: Saved model path. """ # Check existence of model saving directory and create if does not exist. if not os.path.exists(os.path.dirname(model_path)): os.makedirs(os.path.dirname(model_path)) state_dict = { "model": { "name": self.name, "module_pool": self.collect_state_dict(), # "task_names": self.task_names, # "task_flows": self.task_flows, # "loss_funcs": self.loss_funcs, # "output_funcs": self.output_funcs, # "scorers": self.scorers, } } try: torch.save(state_dict, model_path) except BaseException: logger.warning("Saving failed... continuing anyway.") if Meta.config["meta_config"]["verbose"]: logger.info(f"[{self.name}] Model saved in {model_path}") def load(self, model_path: str) -> None: """Load model state_dict from file and reinitialize the model weights. Args: model_path: Saved model path. """ if not os.path.exists(model_path): logger.error("Loading failed... Model does not exist.") try: checkpoint = torch.load(model_path, map_location=torch.device("cpu")) except BaseException: logger.error(f"Loading failed... Cannot load model from {model_path}") raise self.load_state_dict(checkpoint["model"]["module_pool"]) if Meta.config["meta_config"]["verbose"]: logger.info(f"[{self.name}] Model loaded from {model_path}") # Move model to specified device self._move_to_device() def collect_state_dict(self) -> Dict[str, Any]: """Collect the state dict.""" state_dict: Dict[str, Any] = defaultdict(list) for module_name, module in self.module_pool.items(): if hasattr(module, "module"): state_dict[module_name] = module.module.state_dict() # type: ignore else: state_dict[module_name] = module.state_dict() return state_dict def load_state_dict(self, state_dict: Dict[str, Any]) -> None: # type: ignore """Load the state dict. Args: state_dict: The state dict to load. """ for module_name, module_state_dict in state_dict.items(): if module_name in self.module_pool: if hasattr(self.module_pool[module_name], "module"): self.module_pool[module_name].module.load_state_dict( module_state_dict ) else: self.module_pool[module_name].load_state_dict(module_state_dict) else: logger.info(f"Missing {module_name} in module_pool, skip it..")

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null

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null

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7c2daa2465bd8777ef8940cbc518e195f59d4ad9

4,578

py

Python

server/ws_server.py

jangxx/OVRT_Soundpad

2f9b2cd19421bc7b5586a3dcded2998d381ba688

[ "MIT" ]

1

2021-09-29T01:45:35.000Z

server/ws_server.py

jangxx/OVRT_Soundpad

2f9b2cd19421bc7b5586a3dcded2998d381ba688

[ "MIT" ]

2

2021-09-28T08:53:09.000Z

2021-10-20T01:06:15.000Z

server/ws_server.py

jangxx/OVRT_Soundpad

2f9b2cd19421bc7b5586a3dcded2998d381ba688

[ "MIT" ]

null

import asyncio, json from config import Config from soundpad_manager import SoundpadManager from version import BRIDGE_VERSION import websockets from sanic.log import logger # yes I know that it's very lazy to run a separate WS and HTTP server, when both could be run on the same port # I don't like sanics WS implementation tho and this is just a quick and dirty project anyway, so there is no reason to get all that fancy class WebsocketServer: def __init__(self, config: Config, sp_manager: SoundpadManager): self._server = None self._config = config self._soundpad = sp_manager # ephemeral state self._state = { "edit_mode": False, "soundpad_connected": False, "version": BRIDGE_VERSION, } self._index_sockets = set() self._control_sockets = set() def start(self): port = self._config.get(["server", "ws_port"]) logger.info(f"Websocket server is running on port {port}") self._server = asyncio.get_event_loop().run_until_complete(websockets.serve(self.connHandler, "localhost", port)) async def stop(self): self._server.close() await self._server.wait_closed() async def changeState(self, key, value): self._state[key] = value await self.emitEvent("state-update", self._state) async def commandHandler(self, socket, command, params): if command == "register": if params["as"] == "index": self._index_sockets.add(socket) elif params["as"] == "control": self._control_sockets.add(socket) await self.emitEvent("settings-change", self._config.getExternalSerialized(), socket=socket, index_sockets=False, control_sockets=False) await self.emitEvent("state-update", self._state, socket=socket, index_sockets=False, control_sockets=False) elif command == "change-settings": if params["setting"] == [ "board", "rows" ] or params["setting"] == [ "board", "columns" ]: if not 1 <= params["value"] <= 10: return # invalid values are not allowed self._config.set(params["setting"], params["value"]) await self.emitEvent("settings-change", self._config.getExternalSerialized()) elif command == "set-edit-mode": self._state["edit_mode"] = params["value"] await self.emitEvent("state-update", self._state) elif command == "select-sound": if not 0 <= params['page'] <= 9 or not 0 <= params['row'] <= 9 or not 0 <= params['col'] <= 9: return # out of bounds if params['page'] == 0 and self._config.exists([ "sounds", f"{params['row']},{params['col']}" ]): self._config.delete([ "sounds", f"{params['row']},{params['col']}" ]) sound_index = f"{params['page']}:{params['row']},{params['col']}" self._config.set([ "sounds", sound_index ], params["sound"]) await self.emitEvent("settings-change", self._config.getExternalSerialized(), index_sockets=False) elif command == "play-sound": sound_id = params["sound"] self._soundpad.playSound(sound_id) elif command == "stop-sound": self._soundpad.stopSound() elif command == "pause-sound": self._soundpad.pauseSound() elif command == "log": if "message" in params: logger.info("Log: " + params["message"]) else: logger.info("Log: " + json.dumps(params)) async def emitEvent(self, event, data, socket=None, index_sockets=True, control_sockets=True): msg = json.dumps({ "type": "event", "event": event, "data": data }) if socket is not None: await socket.send(msg) if index_sockets: for socket in self._index_sockets: await socket.send(msg) if control_sockets: for socket in self._control_sockets: await socket.send(msg) async def connHandler(self, socket, path): print("Client connected") try: async for raw_msg in socket: try: msg = json.loads(raw_msg) except Exception as err: logger.error(f"Could not parse JSON: {repr(err)}") continue if not "type" in msg: continue if msg["type"] == "command": if not "command" in msg or not "params" in msg: continue try: await self.commandHandler(socket, msg["command"], msg["params"]) except Exception as e: # if we get garbage data just ignore print(f"Error in commandHandler: {msg['command']}({msg['params']}): {repr(e)}") pass except websockets.ConnectionClosedError: pass finally: if socket in self._index_sockets: self._index_sockets.discard(socket) if socket in self._control_sockets: self._control_sockets.discard(socket) print("Client disconnected")

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null

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null

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7c2f595fee4e21dc84c6666b03b2174e6d5731e0

8,108

py

Python

tensorforce/tests/test_model_save_restore.py

gian1312/suchen

df863140fd8df1ac2e195cbdfa4756f09f962270

[ "Apache-2.0" ]

null

tensorforce/tests/test_model_save_restore.py

gian1312/suchen

df863140fd8df1ac2e195cbdfa4756f09f962270

[ "Apache-2.0" ]

null

tensorforce/tests/test_model_save_restore.py

gian1312/suchen

df863140fd8df1ac2e195cbdfa4756f09f962270

[ "Apache-2.0" ]

1

2019-11-29T12:28:33.000Z

from __future__ import absolute_import from __future__ import print_function from __future__ import division import unittest import pytest from tensorforce import TensorForceError from tensorforce.core.networks import LayeredNetwork from tensorforce.models import DistributionModel from tensorforce.tests.minimal_test import MinimalTest from tensorforce.agents import PPOAgent from tensorforce.execution import Runner import tensorflow as tf import numpy as np from tensorforce.util import SavableComponent import os class SavableNetwork(LayeredNetwork, SavableComponent): """ Minimal implementation of a Network that can be saved and restored independently of the Model. """ def get_savable_variables(self): return super(SavableNetwork, self).get_variables(include_nontrainable=False) def _get_base_variable_scope(self): return self.apply.variable_scope_name def create_environment(spec): return MinimalTest(spec) def create_agent(environment, network_spec): return PPOAgent( update_mode=dict( unit='episodes', batch_size=4, frequency=4 ), memory=dict( type='latest', include_next_states=False, capacity=100 ), step_optimizer=dict( type='adam', learning_rate=1e-3 ), subsampling_fraction=0.3, optimization_steps=20, states=environment.states, actions=environment.actions, network=network_spec ) class TestModelSaveRestore(unittest.TestCase): @pytest.fixture(autouse=True) def initdir(self, tmpdir): tmpdir.chdir() self._tmp_dir_path = str(tmpdir) print("Using %s" % (self._tmp_dir_path, )) def test_save_restore(self): environment_spec = {"float": ()} environment = create_environment(environment_spec) network_spec = [ dict(type='dense', size=32) ] agent = create_agent(environment, network_spec) runner = Runner(agent=agent, environment=environment) runner.run(episodes=100) model_values = agent.model.session.run(agent.model.get_variables( include_submodules=True, include_nontrainable=False )) save_path = agent.model.save(directory=self._tmp_dir_path + "/model") print("Saved at: %s" % (save_path,)) runner.close() agent = create_agent(environment, network_spec) agent.model.restore(directory="", file=save_path) restored_model_values = agent.model.session.run(agent.model.get_variables( include_submodules=True, include_nontrainable=False )) assert len(model_values) == len(restored_model_values) assert all([np.array_equal(v1, v2) for v1, v2 in zip(model_values, restored_model_values)]) agent.close() def test_save_network(self): """ Test to validate that calls to save and restore of a SavableComponent successfully save and restore the component's state. """ environment_spec = {"float": ()} environment = create_environment(environment_spec) network_spec = dict( type=SavableNetwork, layers=[dict(type='dense', size=1)] ) agent = create_agent(environment, network_spec) assert isinstance(agent.model.network, SavableComponent) runner = Runner(agent=agent, environment=environment) runner.run(episodes=100) network_values = agent.model.session.run(agent.model.network.get_variables()) distribution = next(iter(agent.model.distributions.values())) distribution_values = agent.model.session.run(distribution.get_variables()) save_path = self._tmp_dir_path + "/network" agent.model.save_component(component_name=DistributionModel.COMPONENT_NETWORK, save_path=save_path) runner.close() assert os.path.isfile(save_path + ".data-00000-of-00001") assert os.path.isfile(save_path + ".index") agent = create_agent(environment, network_spec) agent.model.restore_component(component_name=DistributionModel.COMPONENT_NETWORK, save_path=save_path) # Ensure only the network variables are loaded restored_network_values = agent.model.session.run(agent.model.network.get_variables(include_nontrainable=True)) distribution = next(iter(agent.model.distributions.values())) restored_distribution_values = agent.model.session.run(distribution.get_variables()) assert len(restored_network_values) == len(network_values) assert all([np.array_equal(v1, v2) for v1, v2 in zip(network_values, restored_network_values)]) assert len(restored_distribution_values) == len(distribution_values) assert not all([np.array_equal(v1, v2) for v1, v2 in zip(distribution_values, restored_distribution_values)]) agent.close() environment.close() def test_pretrain_network(self): """ Simulates training outside of Tensorforce and then loading the parameters in the agent's network. """ environment_spec = {"float": ()} environment = create_environment(environment_spec) size = environment.states["shape"] output_size = 1 save_path = self._tmp_dir_path + "/network" g = tf.Graph() with g.as_default(): x = tf.placeholder(dtype=environment.states["type"], shape=[None, size]) layer = tf.layers.Dense(units=output_size) y = layer(x) y_ = tf.placeholder(dtype=environment.states["type"], shape=[None, output_size]) loss = tf.losses.mean_squared_error(y_, y) optimizer = tf.train.AdamOptimizer(learning_rate=0.1) train_step = optimizer.minimize(loss) batch_size = 64 with tf.Session(graph=g) as sess: sess.run(tf.global_variables_initializer()) for epoch in range(100): batch = np.random.random([batch_size, size]) correct = np.ones(shape=[batch.shape[0], output_size]) loss_value, _ = sess.run([loss, train_step], {x: batch, y_: correct}) if epoch % 10 == 0: print("epoch %d: %f" % (epoch, loss_value)) var_map = { "dense0/apply/linear/apply/W:0": layer.kernel, "dense0/apply/linear/apply/b:0": layer.bias } saver = tf.train.Saver(var_list=var_map) saver.save(sess=sess, write_meta_graph=False, save_path=save_path) network_spec = dict( type=SavableNetwork, layers=[dict(type='dense', size=output_size)], ) agent = create_agent(environment, network_spec) agent.model.restore_component(component_name=agent.model.COMPONENT_NETWORK, save_path=save_path) agent.close() def test_non_savable_component(self): environment_spec = {"float": ()} environment = create_environment(environment_spec) network_spec = [dict(type='dense', size=32)] agent = create_agent(environment, network_spec) expected_message = "Component network must implement SavableComponent but is " with pytest.raises(TensorForceError) as excinfo: agent.model.restore_component(component_name="network", save_path=self._tmp_dir_path + "/network") assert expected_message in str(excinfo.value) with pytest.raises(TensorForceError) as excinfo: agent.model.save_component(component_name="network", save_path=self._tmp_dir_path + "/network") assert expected_message in str(excinfo.value) with pytest.raises(TensorForceError) as excinfo: agent.model.restore_component(component_name="non-existent", save_path=self._tmp_dir_path + "/network") assert "Component non-existent must implement SavableComponent but is None" == str(excinfo.value) agent.close()

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0.189873

0.025316

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null

0

null

0

1

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7c32d21e81a25b4bfc714d53125ce26089327176

263

py

Python

what_can_i_cook/urls.py

s-maibuecher/what_can_i_cook

07d0eb1e1862fad299477b800654e895d7f8829a

[ "MIT" ]

null

what_can_i_cook/urls.py

s-maibuecher/what_can_i_cook

07d0eb1e1862fad299477b800654e895d7f8829a

[ "MIT" ]

null

what_can_i_cook/urls.py

s-maibuecher/what_can_i_cook

07d0eb1e1862fad299477b800654e895d7f8829a

[ "MIT" ]

null

from django.urls import path from what_can_i_cook.views import WCICFilterView, WCICResultView app_name = "wcic" urlpatterns = [ path("", WCICFilterView.as_view(), name="wcic-start"), path("results/", WCICResultView.as_view(), name="wcic-results"), ]

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0.285714

0

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null

0

1

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7c32daa41ae2a8f92a0d91d061b5264ea9984602

436

py

Python

shared/templates/grub2_bootloader_argument/template.py

justchris1/scap-security-guide

030097afa80041fcdffc537a49c09896efedadca

[ "BSD-3-Clause" ]

1,138

2018-09-05T06:31:44.000Z

2022-03-31T03:38:24.000Z

shared/templates/grub2_bootloader_argument/template.py

justchris1/scap-security-guide

030097afa80041fcdffc537a49c09896efedadca

[ "BSD-3-Clause" ]

4,743

2018-09-04T15:14:04.000Z

2022-03-31T23:17:57.000Z

shared/templates/grub2_bootloader_argument/template.py

justchris1/scap-security-guide

030097afa80041fcdffc537a49c09896efedadca

[ "BSD-3-Clause" ]

400

2018-09-08T20:08:49.000Z

2022-03-30T20:54:32.000Z

import ssg.utils def preprocess(data, lang): data["arg_name_value"] = data["arg_name"] + "=" + data["arg_value"] if lang == "oval": # escape dot, this is used in oval regex data["escaped_arg_name_value"] = data["arg_name_value"].replace(".", "\\.") # replace . with _, this is used in test / object / state ids data["sanitized_arg_name"] = ssg.utils.escape_id(data["arg_name"]) return data

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0

null

0

null

0

1

0

7c34376a6bdd5ec8372f4490b569f441abff9288

3,598

py

Python

preprocess.py

NNDEV1/NMTWithLuongAttention

e6f11d9e8c5f999d413fa0dc51219e979a8f975c

[ "MIT" ]

4

2021-07-09T19:17:47.000Z

2022-01-04T14:54:11.000Z

preprocess.py

NNDEV1/NMTWithLuongAttention

e6f11d9e8c5f999d413fa0dc51219e979a8f975c

[ "MIT" ]

null

preprocess.py

NNDEV1/NMTWithLuongAttention

e6f11d9e8c5f999d413fa0dc51219e979a8f975c

[ "MIT" ]

null

import tensorflow as tf import os import contractions import tensorflow as tf import pandas as pd import numpy as np import time import rich from rich.progress import track import spacy from config import params #Preprocessing Text class preprocess_text(): def __init__(self): pass def remove_pattern(self, text, pattern= r'[^a-zA-Z0-9.!?, ]', replace_with= ""): return re.sub(pattern, replace_with, text) def tokenize_sent(self, text, nlp): doc= nlp(text) return [sent.text for sent in doc.sents] def tokenize_words(self, text, nlp): doc= nlp(text) return " ".join(tok.text for tok in doc) def expand_contractions(self, text): return contractions.fix(text) def do_lemmatization(self, text, nlp): doc= nlp(text) return ' '.join(tok.lemma_ if tok.lemma_ != "-PRON-" else tok.text for tok in doc) def add_sos_eos(self, text, sos= False, eos= False): if (sos and eos): return "<sos> " + text + " <eos>" if eos: return text + " <eos>" if sos: return "<sos> " + text return text def remove_accents(self, text): return unicodedata.normalize('NFKD', text).encode('ascii', 'ignore').decode('UTF-8', 'ignore') def call_preprocessing(df_col, nlp_en= True, lower_= True, remove_pattern_= False, tokenize_words_= False, expand_contractions_= False, do_lemmatization_= False, sos= False, eos= False, remove_accents_= False): nlp= spacy.load('en_core_web_sm') if nlp_en else spacy.load('de_core_news_sm') prep= preprocess_text() if expand_contractions_: df_col= df_col.map(lambda text: prep.expand_contractions(text)) if remove_accents_: df_col= df_col.map(lambda text: prep.remove_accents(text)) if do_lemmatization_: df_col= df_col.map(lambda text: prep.do_lemmatization(text, nlp)) if tokenize_words_: df_col= df_col.map(lambda text: prep.tokenize_words(text, nlp)) if remove_pattern_: df_col= df_col.map(lambda text: prep.remove_pattern_(text)) if eos or sos: df_col= df_col.map(lambda text: prep.add_sos_eos(text, sos, eos)) if lower_: df_col= df_col.map(lambda text: text.lower()) return df_col def tokenizer(df_col, nlp_en= True): vocab= set() _= [[vocab.update([tok]) for tok in text.split(" ")] for text in df_col] if not nlp_en: vocab.update(["<sos>"]) vocab.update(["<eos>"]) tokenize= dict(zip(vocab, range(1, 1+len(vocab)))) detokenize= dict(zip(range(1, 1+len(vocab)), vocab)) return tokenize, detokenize, len(vocab) def padding(txt_toks, max_len): curr_ls= txt_toks.split(" ") len_ls= len(curr_ls) _= [curr_ls.append("<pad>") for i in range(max_len-len_ls) if len(curr_ls)<max_len] return " ".join(curr_ls) def make_minibatches(df, col1= 'rev_eng_tok', col2= 'teach_force_tok', col3= 'target_tok'): enc_seq= np.array([df[col1].values[i] for i in range(len(df[col1]))]) enc_seq= tf.data.Dataset.from_tensor_slices(enc_seq).batch(params.batch_size) teach_force_seq= np.array([df[col2].values[i] for i in range(len(df[col2]))]) teach_force_seq= tf.data.Dataset.from_tensor_slices(teach_force_seq).batch(params.batch_size) y= np.array([df[col3].values[i] for i in range(len(df[col3]))]) y= tf.data.Dataset.from_tensor_slices(y).batch(params.batch_size) return enc_seq, teach_force_seq, y

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0.229294

3,598

110

107

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false

0.012658

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0.481013

0

null

0

null

0

1

0

7c3462f9e646dbe27aad64fea0cc1723870ee413

1,665

py

Python

setup.py

johannesulf/dsigma

729337c94669f4a0fdacb51b175df1e13e26304c

[ "MIT" ]

4

2020-06-09T01:09:58.000Z

2021-09-26T16:39:16.000Z

setup.py

johannesulf/dsigma

729337c94669f4a0fdacb51b175df1e13e26304c

[ "MIT" ]

null

setup.py

johannesulf/dsigma

729337c94669f4a0fdacb51b175df1e13e26304c

[ "MIT" ]

null

from setuptools import setup, find_packages from distutils.extension import Extension from distutils.command.sdist import sdist try: from Cython.Build import cythonize USE_CYTHON = True except ImportError: USE_CYTHON = False ext = 'pyx' if USE_CYTHON else 'c' extensions = [Extension( 'dsigma.precompute_engine', ['dsigma/precompute_engine.{}'.format(ext)], extra_compile_args=['-Ofast', '-march=native'])] if USE_CYTHON: extensions = cythonize(extensions) class sdist_with_cythonize(sdist): def run(self): cythonize(['dsigma/precompute_engine.pyx']) sdist.run(self) with open('README.md', 'r') as fstream: long_description = fstream.read() setup( name='dsigma', version='0.5.0', description=('A Galaxy-Galaxy Lensing Pipeline'), long_description=long_description, long_description_content_type='text/markdown', classifiers=[ 'Intended Audience :: Science/Research', 'Topic :: Scientific/Engineering :: Astronomy', 'Operating System :: OS Independent', 'Programming Language :: Python :: 3 :: Only', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', ], keywords='astronomy, weak-lensing', url='https://github.com/johannesulf/dsigma', author='Johannes Lange, Song Huang', author_email='jolange@ucsc.edu', packages=find_packages(), install_requires=['numpy', 'astropy', 'scipy', 'scikit-learn', 'healpy'], python_requires='>=3.4', ext_modules=extensions, cmdclass={'sdist': sdist_with_cythonize} )

30.272727

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0.091996

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0

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0.196396

1,665

54

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0

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false

0

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0

0.152174

0

null

0

null

0

1

0

7c34972839ffa0fc13d463ba6725ab4c70743477

1,967

py

Python

face_detector/modules/mod_faceDetection.py

jtfan3/face_detection

82e3bc839bf12c956f3166c07012912a0638048f

[ "MIT" ]

null

face_detector/modules/mod_faceDetection.py

jtfan3/face_detection

82e3bc839bf12c956f3166c07012912a0638048f

[ "MIT" ]

null

face_detector/modules/mod_faceDetection.py

jtfan3/face_detection

82e3bc839bf12c956f3166c07012912a0638048f

[ "MIT" ]

null

import cv2 import mediapipe as mp class FaceDetection(): # initialize the face detection class with arguments from https://google.github.io/mediapipe/solutions/face_detection.html def __init__(self, model_selection = 0, threshold = 0.5): self.model_selection = model_selection self.threshold = threshold self.mp_draw = mp.solutions.drawing_utils self.face_detection = mp.solutions.face_detection.FaceDetection(model_selection = self.model_selection, min_detection_confidence = self.threshold) # gets bounding boxes using self.face_detection, returns a list of element, elment = (score, bbox_dict) def get_bboxs(self, frame): mp_detections = self.face_detection.process(frame) score_bboxs = [] if mp_detections.detections: for detection in mp_detections.detections: score = detection.score[0] mp_bbox = detection.location_data.relative_bounding_box bbox_dict = { 'x_min': mp_bbox.xmin, 'y_min': mp_bbox.ymin, 'w': mp_bbox.width, 'h': mp_bbox.height } score_bboxs.append([score, bbox_dict]) return score_bboxs # draws the bbox onto the frame def draw_bbox(self, face_probs, bbox_dict, frame, col = (255, 0, 255), gender = None, gender_score = None): x_min, y_min, w, h = bbox_dict.values() frame_h, frame_w, _ = frame.shape bbox = int(x_min * frame_w), int(y_min * frame_h), int(w * frame_w), int(h * frame_h) # prepare text, depending on what atributes we predict text = str(round(face_probs, 3)) if gender: text = gender + ": " + str(round(gender_score, 2)) # draw bbox cv2.rectangle(frame, bbox, col, 2) cv2.putText(frame, text, (bbox[0], bbox[1] - 10), cv2.FONT_HERSHEY_COMPLEX, 0.5, col, 1)

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1,967

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false

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0

0.212121

0

null

0

null

0

1

0

7c378f7b0a34c442460ca831372ef84873f73309

768

py

Python

pymc/mc_enum.py

cherish-web/pymc

9c322abfdcceca0a78b633d85da23e1290c036c8

[ "Apache-2.0" ]

4

2021-05-01T12:43:24.000Z

2022-01-25T03:44:32.000Z

pymc/mc_enum.py

cherish-web/pymc

9c322abfdcceca0a78b633d85da23e1290c036c8

[ "Apache-2.0" ]

null

pymc/mc_enum.py

cherish-web/pymc

9c322abfdcceca0a78b633d85da23e1290c036c8

[ "Apache-2.0" ]

2

2021-07-10T03:56:08.000Z

2021-09-30T14:59:35.000Z

# _*_ coding: utf-8 _*_ # @Time : 2021/3/29 上午 08:57 # @Author : cherish_peng # @Email : 1058386071@qq.com # @File : cmd.py # @Software : PyCharm from enum import Enum class EnumSubTitle(Enum): Request4e = 0x5400 # 请求 Request = 0x5000 # 应答 Respond = 0xD000 Respond4e = 0xD400 class EnumEndCode(Enum): # 正常应答 Ok = 0x0000 # 异常应答 Err = 0x51C0 class EnumCmd(Enum): # 成批读 ReadBatch = 0x0401 # 成批写 WriteBatch = 0x1401 class EnumSubCmd(Enum): # 有存储扩展模块b7=0，b6=0：随机读出,监视数据注册用外 # 按位读写 Bit = 0x0001 # 按字读写 Word = 0x0000 # 有存储扩展模块b7=1，b6=0：随机读出,监视数据注册用外 # 按位读写 BitEx = 0x0081 # 按字读写 WordEx = 0x0080 class EnumType(Enum): # 位类型 Bit = 0 # 字类型 Word = 1

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