repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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RESPECT | RESPECT-main/nets/graph_encoder.py | import torch
import numpy as np
from torch import nn
import math
class SkipConnection(nn.Module):
def __init__(self, module):
super(SkipConnection, self).__init__()
self.module = module
def forward(self, input):
return input + self.module(input)
class MultiHeadAttention(nn.Module):... | 6,927 | 32.148325 | 117 | py |
RESPECT | RESPECT-main/nets/critic_network.py | from torch import nn
from nets.graph_encoder import GraphAttentionEncoder
class CriticNetwork(nn.Module):
def __init__(
self,
input_dim,
embedding_dim,
hidden_dim,
n_layers,
encoder_normalization
):
super(CriticNetwork, self).__init__()
self.hi... | 965 | 22.560976 | 58 | py |
RESPECT | RESPECT-main/nets/__init__.py | 0 | 0 | 0 | py | |
RESPECT | RESPECT-main/nets/pointer_network_dataset_pick3.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import math
import numpy as np
from torch.nn import TransformerEncoder, TransformerEncoderLayer
from utils import move_to
class Encoder(nn.Module):
"""Maps a graph represented as an input sequence
to a hidden vector"""
def __init__(se... | 16,562 | 40.304239 | 201 | py |
RESPECT | RESPECT-main/problems/__init__.py | from problems.tsp.problem_tsp import TSP
from problems.vrp.problem_vrp import CVRP, SDVRP
from problems.op.problem_op import OP
from problems.pctsp.problem_pctsp import PCTSPDet, PCTSPStoch
from problems.toposort.problem_toposort import TopoSort, TopoSortDataset
| 263 | 43 | 72 | py |
RESPECT | RESPECT-main/problems/pctsp/state_pctsp.py | import torch
from typing import NamedTuple
from utils.boolmask import mask_long2bool, mask_long_scatter
import torch.nn.functional as F
bypass = super
class StatePCTSP(NamedTuple):
# Fixed input
coords: torch.Tensor # Depot + loc
expected_prize: torch.Tensor
real_prize: torch.Tensor
penalty: torc... | 7,770 | 43.405714 | 119 | py |
RESPECT | RESPECT-main/problems/pctsp/problem_pctsp.py | from torch.utils.data import Dataset
import torch
import os
import pickle
from problems.pctsp.state_pctsp import StatePCTSP
from utils.beam_search import beam_search
class PCTSP(object):
NAME = 'pctsp' # Prize Collecting TSP, without depot, with penalties
@staticmethod
def _get_costs(dataset, pi, stoch... | 7,293 | 38.215054 | 120 | py |
RESPECT | RESPECT-main/problems/pctsp/pctsp_baseline.py | import argparse
import os
import numpy as np
from utils import run_all_in_pool
from utils.data_utils import check_extension, load_dataset, save_dataset
from subprocess import check_call, check_output
import re
import time
from datetime import timedelta
import random
from scipy.spatial import distance_matrix
from .sales... | 20,018 | 42.901316 | 120 | py |
RESPECT | RESPECT-main/problems/pctsp/pctsp_ortools.py | #!/usr/bin/env python
# This Python file uses the following encoding: utf-8
# Copyright 2015 Tin Arm Engineering AB
# Copyright 2018 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
#... | 8,401 | 33.576132 | 106 | py |
RESPECT | RESPECT-main/problems/pctsp/__init__.py | 0 | 0 | 0 | py | |
RESPECT | RESPECT-main/problems/pctsp/pctsp_gurobi.py | #!/usr/bin/python
# Copyright 2017, Gurobi Optimization, Inc.
# Solve a traveling salesman problem on a set of
# points using lazy constraints. The base MIP model only includes
# 'degree-2' constraints, requiring each node to have exactly
# two incident edges. Solutions to this model may contain subtours -
# tours... | 4,796 | 37.685484 | 117 | py |
RESPECT | RESPECT-main/problems/pctsp/salesman/__init__.py | 0 | 0 | 0 | py | |
RESPECT | RESPECT-main/problems/pctsp/salesman/pctsp/__main__.py | # from qextractor.application import main
# main() | 50 | 24.5 | 41 | py |
RESPECT | RESPECT-main/problems/pctsp/salesman/pctsp/application.py | # module application.py
#
# Copyright (c) 2015 Rafael Reis
#
"""
application module - Main module that solves the Prize Collecting Travelling Salesman Problem
"""
from pctsp.model.pctsp import *
from pctsp.model import solution
from pctsp.algo.genius import genius
from pctsp.algo import ilocal_search as ils
from pkg_... | 1,467 | 23.065574 | 93 | py |
RESPECT | RESPECT-main/problems/pctsp/salesman/pctsp/__init__.py | # package qextractor
#
# Copyright (c) 2015 Rafael Reis
#
"""
Package qextractor - Packages for building and evaluating a machine learning
model to tackle the Quotation Extractor Task
"""
__version__="1.0"
__author__ = "Rafael Reis <rafael2reis@gmail.com>" | 257 | 22.454545 | 76 | py |
RESPECT | RESPECT-main/problems/pctsp/salesman/pctsp/model/solution.py | # module solution.py
#
# Copyright (c) 2018 Rafael Reis
#
"""
solution module - Implements Solution, a class that describes a solution for the problem.
"""
__version__="1.0"
import numpy as np
import copy
import sys
from random import shuffle
def random(pctsp, start_size):
s = Solution(pctsp)
length = len(pc... | 4,987 | 29.230303 | 118 | py |
RESPECT | RESPECT-main/problems/pctsp/salesman/pctsp/model/pctsp.py | # module pctsp.py
#
# Copyright (c) 2018 Rafael Reis
#
"""
pctsp module - Implements Pctsp, a class that describes an instance of the problem..
"""
__version__="1.0"
import numpy as np
import re
class Pctsp(object):
"""
Attributes:
c (:obj:`list` of :obj:`list`): Costs from i to j
p (:obj:`list... | 1,051 | 23.465116 | 84 | py |
RESPECT | RESPECT-main/problems/pctsp/salesman/pctsp/model/__init__.py | # package model
#
# Copyright (c) 2018 Rafael Reis
#
"""
Package model - Models of Prize Collecting Travelling Salesman Problem
"""
__version__="1.0"
__author__ = "Rafael Reis <rafael2reis@gmail.com>"
| 202 | 17.454545 | 70 | py |
RESPECT | RESPECT-main/problems/pctsp/salesman/pctsp/model/tests/__init__.py | 0 | 0 | 0 | py | |
RESPECT | RESPECT-main/problems/pctsp/salesman/pctsp/model/tests/test_solution.py | # python -m pctsp.model.tests.test_solution
import unittest
from pctsp.model import solution
from pctsp.model import pctsp
import numpy as np
class TestTrain(unittest.TestCase):
def setUp(self):
self.p = pctsp.Pctsp()
self.p.prize = np.array([0, 4, 8, 3])
self.p.penal = np.array([1000, 7, ... | 1,668 | 26.360656 | 88 | py |
RESPECT | RESPECT-main/problems/pctsp/salesman/pctsp/algo/genius.py | # module genius.py
#
# Copyright (c) 2018 Rafael Reis
#
"""
genius module - Implements GENIUS, an algorithm for generation of a solution.
"""
__version__="1.0"
from pctsp.model.pctsp import *
from pctsp.model import solution
import numpy as np
def genius(pctsp):
s = solution.random(pctsp, size=3)
s = geni(p... | 426 | 14.25 | 77 | py |
RESPECT | RESPECT-main/problems/pctsp/salesman/pctsp/algo/ilocal_search.py | # module ilocal_search.py
#
# Copyright (c) 2018 Rafael Reis
#
"""
ilocal_search module - Implements Iterate Local Search algorithm.
"""
__version__="1.0"
import numpy as np
import random
def ilocal_search(s, n_runs=10):
h = s.copy()
best = s.copy()
times = [1000] * n_runs # random.sample(range(1000, 20... | 2,127 | 20.494949 | 71 | py |
RESPECT | RESPECT-main/problems/pctsp/salesman/pctsp/algo/geni.py | # module geni.py
#
# Copyright (c) 2018 Rafael Reis
#
"""
geni module - Auxiliary functions to the GENI method.
"""
__version__="1.0"
import numpy as np
import sys
def geni(v, s, max_i):
quality_1 = 0
quality_2 = 0
s_star = Solution()
s_start.quality = sys.maxint
for i in range(1, max_i):
... | 641 | 19.709677 | 66 | py |
RESPECT | RESPECT-main/problems/pctsp/salesman/pctsp/algo/__init__.py | # package algo
#
# Copyright (c) 2018 Rafael Reis
#
"""
Package algo - Algorithms for solving the Prize Collecting Travelling Salesman Problem
"""
__version__="1.0"
__author__ = "Rafael Reis <rafael2reis@gmail.com>"
| 218 | 18.909091 | 87 | py |
RESPECT | RESPECT-main/problems/tsp/tsp_gurobi.py | #!/usr/bin/python
# Copyright 2017, Gurobi Optimization, Inc.
# Solve a traveling salesman problem on a set of
# points using lazy constraints. The base MIP model only includes
# 'degree-2' constraints, requiring each node to have exactly
# two incident edges. Solutions to this model may contain subtours -
# tours... | 3,951 | 31.393443 | 91 | py |
RESPECT | RESPECT-main/problems/tsp/problem_tsp.py | from torch.utils.data import Dataset
import torch,random
import os
import pickle
from problems.tsp.state_tsp import StateTSP
from utils.beam_search import beam_search
class TSP(object):
NAME = 'tsp'
@staticmethod
def get_costs(dataset, pi):
# Check that tours are valid, i.e. contain 0 to n -1
... | 3,449 | 34.9375 | 135 | py |
RESPECT | RESPECT-main/problems/tsp/__init__.py | 0 | 0 | 0 | py | |
RESPECT | RESPECT-main/problems/tsp/tsp_baseline.py | import argparse
import numpy as np
import os
import time
from datetime import timedelta
from scipy.spatial import distance_matrix
from utils import run_all_in_pool
from utils.data_utils import check_extension, load_dataset, save_dataset
from subprocess import check_call, check_output, CalledProcessError
from problems.v... | 17,311 | 37.471111 | 120 | py |
RESPECT | RESPECT-main/problems/tsp/state_tsp.py | import torch
from typing import NamedTuple
from utils.boolmask import mask_long2bool, mask_long_scatter
bypass = super
class StateTSP(NamedTuple):
# Fixed input
loc: torch.Tensor
dist: torch.Tensor
# If this state contains multiple copies (i.e. beam search) for the same instance, then for memory effi... | 5,705 | 39.468085 | 121 | py |
RESPECT | RESPECT-main/problems/vrp/problem_vrp.py | from torch.utils.data import Dataset
import torch
import os
import pickle
from problems.vrp.state_cvrp import StateCVRP
from problems.vrp.state_sdvrp import StateSDVRP
from utils.beam_search import beam_search
class CVRP(object):
NAME = 'cvrp' # Capacitated Vehicle Routing Problem
VEHICLE_CAPACITY = 1.0 ... | 7,569 | 35.570048 | 117 | py |
RESPECT | RESPECT-main/problems/vrp/vrp_baseline.py | import argparse
import os
import numpy as np
import re
from utils.data_utils import check_extension, load_dataset, save_dataset
from subprocess import check_call, check_output
from urllib.parse import urlparse
import tempfile
import time
from datetime import timedelta
from utils import run_all_in_pool
def get_lkh_exe... | 10,387 | 38.052632 | 139 | py |
RESPECT | RESPECT-main/problems/vrp/state_sdvrp.py | import torch
from typing import NamedTuple
bypass = super
class StateSDVRP(NamedTuple):
# Fixed input
coords: torch.Tensor
demand: torch.Tensor
# If this state contains multiple copies (i.e. beam search) for the same instance, then for memory efficiency
# the coords and demands tensors are not ke... | 4,979 | 39.487805 | 119 | py |
RESPECT | RESPECT-main/problems/vrp/state_cvrp.py | import torch
from typing import NamedTuple
from utils.boolmask import mask_long2bool, mask_long_scatter
bypass = super
class StateCVRP(NamedTuple):
# Fixed input
coords: torch.Tensor # Depot + loc
demand: torch.Tensor
# If this state contains multiple copies (i.e. beam search) for the same instance,... | 6,844 | 40.737805 | 118 | py |
RESPECT | RESPECT-main/problems/vrp/__init__.py | 0 | 0 | 0 | py | |
RESPECT | RESPECT-main/problems/vrp/encode-attend-navigate/data_generator.py | #-*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import math
from sklearn.decomposition import PCA
# Compute a sequence's reward
def reward(tsp_sequence):
tour = np.concatenate((tsp_sequence, np.expand_dims(tsp_sequence[0],0))) # sequence to tour (end=start)
inter_city_distances = np.... | 4,401 | 39.385321 | 129 | py |
RESPECT | RESPECT-main/problems/vrp/encode-attend-navigate/utils.py | # -*- coding: utf-8 -*-
from __future__ import print_function
import tensorflow as tf
import numpy as np
from tqdm import tqdm
# Embed input sequence [batch_size, seq_length, from_] -> [batch_size, seq_length, to_]
def embed_seq(input_seq, from_, to_, is_training, BN=True, initializer=tf.contrib.layers.xavier_initial... | 6,176 | 60.77 | 152 | py |
RESPECT | RESPECT-main/problems/vrp/encode-attend-navigate/Neural_Reinforce.py |
# coding: utf-8
# # Neural Combinatorial Optimization
# In[1]:
#-*- coding: utf-8 -*-
import tensorflow as tf
distr = tf.contrib.distributions
import numpy as np
from tqdm import tqdm
import os
import matplotlib.pyplot as plt
from utils import embed_seq, encode_seq, full_glimpse, pointer
from data_generator impo... | 21,835 | 45.95914 | 471 | py |
RESPECT | RESPECT-main/problems/toposort/state_toposort.py | import torch
from typing import NamedTuple
from utils.boolmask import mask_long2bool, mask_long_scatter
bypass = super
class StateTopoSort(NamedTuple):
# Fixed input
loc: torch.Tensor
dist: torch.Tensor
# If this state contains multiple copies (i.e. beam search) for the same instance, then for memory... | 5,725 | 39.609929 | 121 | py |
RESPECT | RESPECT-main/problems/toposort/problem_toposort_xySorting.py | from torch.utils.data import Dataset
import torch, random
import os
import pickle
from problems.toposort.state_toposort import StateTopoSort
from utils.beam_search import beam_search
from utils import orderCheck, deep_sort_x, level_sorting, level_sorting_xy_pairs, order_check
import networkx as nx
import numpy as np
... | 7,376 | 45.396226 | 192 | py |
RESPECT | RESPECT-main/problems/toposort/problem_toposort_tmp.py | from torch.utils.data import Dataset
import torch, random
import os
import pickle
from problems.toposort.state_toposort import StateTopoSort
from utils.beam_search import beam_search
from utils import orderCheck, deep_sort_x, level_sorting, level_sorting_xy_pairs, order_check
import networkx as nx
import numpy as np
... | 9,242 | 46.891192 | 224 | py |
RESPECT | RESPECT-main/problems/toposort/problem_toposort_singleTraining_reversed_label.py | from torch.utils.data import Dataset
import torch, random
import os
import pickle
from problems.toposort.state_toposort import StateTopoSort
from utils.beam_search import beam_search
#from utils import orderCheck, deep_sort_x, level_sorting, level_sorting_xy_pairs, order_check
from utils import smart_sort
import netwo... | 11,131 | 49.144144 | 224 | py |
RESPECT | RESPECT-main/problems/toposort/data_generator.py | import numpy as np
import math
import networkx as nx
import random
"""
Data generator for mathematical symbolic expression;
Given input:
File consisting of a series of mathematical equation of string type--(m0=n1+n2)
Expected output:
A bunch of 4-dim nodes of shape (variable_out, variable_in1, operation, va... | 3,467 | 33 | 133 | py |
RESPECT | RESPECT-main/problems/toposort/problem_toposort_model_run.py | from torch.utils.data import Dataset
import torch, random
import os
import pickle
from problems.toposort.state_toposort import StateTopoSort
from utils.beam_search import beam_search
#from utils import orderCheck, deep_sort_x, level_sorting, level_sorting_xy_pairs, order_check
from utils import smart_sort
import netwo... | 10,826 | 48.438356 | 224 | py |
RESPECT | RESPECT-main/problems/toposort/problem_toposort_multipleTraining_2.py | from torch.utils.data import Dataset
import torch, random
import os
import pickle
from problems.toposort.state_toposort import StateTopoSort
from utils.beam_search import beam_search
#from utils import orderCheck, deep_sort_x, level_sorting, level_sorting_xy_pairs, order_check
from utils import smart_sort
import netwo... | 11,535 | 48.939394 | 228 | py |
RESPECT | RESPECT-main/problems/toposort/dataset_generator.py | from torch.utils.data import Dataset
import torch, random
import os
import pickle
#from problems.toposort.state_toposort import StateTopoSort
#from utils.beam_search import beam_search
#from utils import orderCheck, deep_sort_x, level_sorting, level_sorting_xy_pairs, order_check, graph_sorting_DAG
from collections imp... | 5,644 | 38.753521 | 131 | py |
RESPECT | RESPECT-main/problems/toposort/problem_toposort_1.py | from torch.utils.data import Dataset
import torch, random
import os
import pickle
from problems.toposort.state_toposort import StateTopoSort
from utils.beam_search import beam_search
from utils import orderCheck, deep_sort_x, level_sorting, level_sorting_xy_pairs, order_check, graph_sorting_DAG
import networkx as nx
i... | 8,767 | 44.430052 | 192 | py |
RESPECT | RESPECT-main/problems/toposort/problem_toposort_multipleTraining.py | from torch.utils.data import Dataset
import torch, random
import os
import pickle
from problems.toposort.state_toposort import StateTopoSort
from utils.beam_search import beam_search
#from utils import orderCheck, deep_sort_x, level_sorting, level_sorting_xy_pairs, order_check
from utils import smart_sort
import netwo... | 12,543 | 51.485356 | 260 | py |
RESPECT | RESPECT-main/problems/toposort/problem_toposort_temporary_idea.py | from torch.utils.data import Dataset
import torch, random
import os
import pickle
from problems.toposort.state_toposort import StateTopoSort
from utils.beam_search import beam_search
from utils import orderCheck, deep_sort_x, level_sorting, level_sorting_xy_pairs, order_check
import networkx as nx
import numpy as np
... | 9,252 | 46.943005 | 224 | py |
RESPECT | RESPECT-main/problems/toposort/problem_toposort_2.py | from torch.utils.data import Dataset
import torch, random
import os
import pickle
from problems.toposort.state_toposort import StateTopoSort
from utils.beam_search import beam_search
from utils import orderCheck, deep_sort_x, level_sorting, level_sorting_xy_pairs, order_check
import networkx as nx
import numpy as np
... | 9,254 | 46.953368 | 224 | py |
RESPECT | RESPECT-main/problems/toposort/__init__.py | 0 | 0 | 0 | py | |
RESPECT | RESPECT-main/problems/toposort/problem_toposort_singleTraining.py | from torch.utils.data import Dataset
import torch, random
import os
import pickle
from problems.toposort.state_toposort import StateTopoSort
from utils.beam_search import beam_search
#from utils import orderCheck, deep_sort_x, level_sorting, level_sorting_xy_pairs, order_check
from utils import smart_sort
import netwo... | 11,190 | 49.183857 | 224 | py |
RESPECT | RESPECT-main/problems/toposort/problem_toposort_newEmbedding.py | from torch.utils.data import Dataset
import torch, random
import os
import pickle
from problems.toposort.state_toposort import StateTopoSort
from utils.beam_search import beam_search
from utils import orderCheck, deep_sort_x, level_sorting, level_sorting_xy_pairs, order_check, graph_sorting_DAG
from collections import... | 11,423 | 45.064516 | 192 | py |
RESPECT | RESPECT-main/problems/toposort/problem_toposort_multipleTraining_1.py | from torch.utils.data import Dataset
import torch, random
import os
import pickle
from problems.toposort.state_toposort import StateTopoSort
from utils.beam_search import beam_search
#from utils import orderCheck, deep_sort_x, level_sorting, level_sorting_xy_pairs, order_check
from utils import smart_sort
import netwo... | 10,873 | 47.328889 | 224 | py |
RESPECT | RESPECT-main/problems/toposort/problem_toposort.py | from torch.utils.data import Dataset
import torch, random
import os
import pickle
from problems.toposort.state_toposort import StateTopoSort
from utils.beam_search import beam_search
#from utils import orderCheck, deep_sort_x, level_sorting, level_sorting_xy_pairs, order_check
from utils import smart_sort
import netwo... | 11,011 | 48.160714 | 224 | py |
RESPECT | RESPECT-main/problems/op/op_gurobi.py | #!/usr/bin/python
# Copyright 2017, Gurobi Optimization, Inc.
# Solve a traveling salesman problem on a set of
# points using lazy constraints. The base MIP model only includes
# 'degree-2' constraints, requiring each node to have exactly
# two incident edges. Solutions to this model may contain subtours -
# tours... | 4,369 | 35.722689 | 109 | py |
RESPECT | RESPECT-main/problems/op/op_baseline.py | import argparse
import os
import numpy as np
from utils import run_all_in_pool
from utils.data_utils import check_extension, load_dataset, save_dataset
from subprocess import check_call, check_output
import tempfile
import time
from datetime import timedelta
from problems.op.opga.opevo import run_alg as run_opga_alg
fr... | 16,891 | 41.764557 | 118 | py |
RESPECT | RESPECT-main/problems/op/op_ortools.py | #!/usr/bin/env python
# This Python file uses the following encoding: utf-8
# Copyright 2015 Tin Arm Engineering AB
# Copyright 2018 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
#... | 9,057 | 33.441065 | 106 | py |
RESPECT | RESPECT-main/problems/op/problem_op.py | from torch.utils.data import Dataset
import torch
import os
import pickle
from problems.op.state_op import StateOP
from utils.beam_search import beam_search
class OP(object):
NAME = 'op' # Orienteering problem
@staticmethod
def get_costs(dataset, pi):
if pi.size(-1) == 1: # In case all tours d... | 4,934 | 33.51049 | 106 | py |
RESPECT | RESPECT-main/problems/op/tsiligirides.py | import torch
from problems.op.state_op import StateOP
def op_tsiligirides(batch, sample=False, power=4.0):
state = StateOP.initialize(batch)
all_a = []
while not state.all_finished():
# Compute scores
mask = state.get_mask()
p = (
(mask[..., 1:] == 0).float() *
... | 1,672 | 37.906977 | 108 | py |
RESPECT | RESPECT-main/problems/op/__init__.py | 0 | 0 | 0 | py | |
RESPECT | RESPECT-main/problems/op/state_op.py | import torch
from typing import NamedTuple
from utils.boolmask import mask_long2bool, mask_long_scatter
import torch.nn.functional as F
bypass = super
class StateOP(NamedTuple):
# Fixed input
coords: torch.Tensor # Depot + loc
prize: torch.Tensor
# Max length is not a single value, but one for each n... | 7,431 | 43.238095 | 118 | py |
RESPECT | RESPECT-main/problems/op/opga/oph.py | import math
def distance( p1, p2 ):
return math.sqrt( ( p1[0] - p2[0] ) ** 2 + ( p1[1] - p2[1] ) ** 2 )
#returns a path (list of points) through s with high value
def ellinit_replacement( s1, start_point, end_point, tmax ):
s = list( s1 )
path = [ start_point, end_point ]
length = distance( start_poin... | 5,481 | 40.530303 | 105 | py |
RESPECT | RESPECT-main/problems/op/opga/opevo.py | import sys
import random
import time
from . import oph
#fitness will take a set s and a set of weights and return a tuple containing the fitness and the best path
def fitness( chrom, s, start_point, end_point, tmax ):
augs = []
for i in range( len( s ) ):
augs.append( ( s[ i ][0],
... | 5,970 | 37.522581 | 107 | py |
RESPECT | RESPECT-main/problems/op/opga/optest.py | import time
import opevo
files = [ 'test instances/set_64_1_15.txt' ]
tmaxs = [ range( 15, 80 + 1, 5 ) ]
Ns = [ 64 ]
test_runs = 30
assert( len( files ) == len( tmaxs ) and len( tmaxs ) == len( Ns ) )
for i in range( len( files ) ):
f = open( files[ i ] )
of = open( files[ i ][ :len( files[ i ] ) - 4 ] + '... | 1,057 | 30.117647 | 95 | py |
RESPECT | RESPECT-main/problems/op/opga/__init__.py | 0 | 0 | 0 | py | |
RESPECT | RESPECT-main/utils/tensor_functions.py | import torch
def compute_in_batches(f, calc_batch_size, *args, n=None):
"""
Computes memory heavy function f(*args) in batches
:param n: the total number of elements, optional if it cannot be determined as args[0].size(0)
:param f: The function that is computed, should take only tensors as arguments a... | 1,608 | 44.971429 | 120 | py |
RESPECT | RESPECT-main/utils/monkey_patch.py | import torch
from itertools import chain
from collections import defaultdict, Iterable
from copy import deepcopy
def load_state_dict(self, state_dict):
"""Loads the optimizer state.
Arguments:
state_dict (dict): optimizer state. Should be an object returned
from a call to :meth:`state_dict... | 2,734 | 38.071429 | 90 | py |
RESPECT | RESPECT-main/utils/functions.py | import warnings
import torch
import numpy as np
import os
import json
from tqdm import tqdm
from multiprocessing.dummy import Pool as ThreadPool
from multiprocessing import Pool
import torch.nn.functional as F
import networkx as nx
import random
def load_problem(name):
from problems import TSP, CVRP, SDVRP, OP, ... | 21,760 | 33.486529 | 160 | py |
RESPECT | RESPECT-main/utils/boolmask.py | import torch
import torch.nn.functional as F
def _pad_mask(mask):
# By taking -size % 8, we get 0 if exactly divisible by 8
# and required padding otherwise (i.e. -1 % 8 = 7 pad)
pad = -mask.size(-1) % 8
if pad != 0:
mask = F.pad(mask, [0, pad])
return mask, mask.size(-1) // 8
def _mask_... | 2,809 | 37.493151 | 131 | py |
RESPECT | RESPECT-main/utils/data_utils.py | import os
import pickle
def check_extension(filename):
if os.path.splitext(filename)[1] != ".pkl":
return filename + ".pkl"
return filename
def save_dataset(dataset, filename):
filedir = os.path.split(filename)[0]
if not os.path.isdir(filedir):
os.makedirs(filedir)
with open(c... | 528 | 20.16 | 56 | py |
RESPECT | RESPECT-main/utils/lexsort.py | import torch
import numpy as np
def torch_lexsort(keys, dim=-1):
if keys[0].is_cuda:
return _torch_lexsort_cuda(keys, dim)
else:
# Use numpy lex sort
return torch.from_numpy(np.lexsort([k.numpy() for k in keys], axis=dim))
def _torch_lexsort_cuda(keys, dim=-1):
"""
Function c... | 2,382 | 41.553571 | 127 | py |
RESPECT | RESPECT-main/utils/beam_search.py | import time
import torch
from typing import NamedTuple
from utils.lexsort import torch_lexsort
def beam_search(*args, **kwargs):
beams, final_state = _beam_search(*args, **kwargs)
return get_beam_search_results(beams, final_state)
def get_beam_search_results(beams, final_state):
beam = beams[-1] # Fina... | 8,521 | 36.875556 | 117 | py |
RESPECT | RESPECT-main/utils/log_utils.py | def log_values(cost, grad_norms, epoch, batch_id, step,
log_likelihood, reinforce_loss, bl_loss, tb_logger, opts):
avg_cost = cost.mean().item()
grad_norms, grad_norms_clipped = grad_norms
# Log values to screen
print('epoch: {}, train_batch_id: {}, avg_cost: {}'.format(epoch, batch_id, ... | 1,093 | 42.76 | 90 | py |
RESPECT | RESPECT-main/utils/parameters.py | bypass = super
| 15 | 7 | 14 | py |
RESPECT | RESPECT-main/utils/__init__.py | from .functions import *
| 25 | 12 | 24 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/parser.py | import argparse
def get_args():
parser = argparse.ArgumentParser()
# dataset config
parser.add_argument("--root", "-r", default="./data", type=str, help="/path/to/dataset")
parser.add_argument("--dataset", "-d", default="cifar10", choices=['stl10', 'svhn', 'cifar10', 'cifar100'], type=str, help="datas... | 5,939 | 86.352941 | 158 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/moon_data_exp.py | """
Two moons experiment for visualization
"""
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
from sklearn.datasets import make_moons
from tqdm import tqdm
from ssl_lib.a... | 8,998 | 37.788793 | 123 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/train_val_test.py | import logging
import numpy, random, time
import torch
import torch.nn.functional as F
import torch.optim as optim
from ssl_lib.algs.builder import gen_ssl_alg
from ssl_lib.algs import utils as alg_utils
from ssl_lib.models import utils as model_utils
from ssl_lib.consistency.builder import gen_consistency
from ssl_li... | 9,950 | 35.054348 | 132 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/train_test.py | import logging
import numpy, random, time, json
import torch
import torch.nn.functional as F
import torch.optim as optim
from ssl_lib.algs.builder import gen_ssl_alg
from ssl_lib.algs import utils as alg_utils
from ssl_lib.models import utils as model_utils
from ssl_lib.consistency.builder import gen_consistency
from ... | 10,043 | 35.129496 | 121 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/__init__.py | 0 | 0 | 0 | py | |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/models/resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import leaky_relu, conv3x3, BatchNorm2d, param_init, BaseModel
class _Residual(nn.Module):
def __init__(self, input_channels, output_channels, stride=1, activate_before_residual=False):
super().__init__()
layer = []
... | 2,568 | 31.1125 | 111 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/models/utils.py | import math
import torch.nn as nn
import torch.nn.functional as F
class BaseModel(nn.Module):
def forward(self, x):
f = self.feature_extractor(x)
f = f.mean((2, 3))
return self.classifier(f)
def logits_with_feature(self, x):
f = self.feature_extractor(x)
c = self.class... | 2,915 | 30.354839 | 93 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/models/cnn13.py | import torch.nn as nn
from .utils import leaky_relu, conv3x3, BatchNorm2d, BaseModel
class CNN13(BaseModel):
"""
13-layer CNN
Parameters
--------
num_classes: int
number of classes
filters: int
number of filters
"""
def __init__(self, num_classes, filters, *args, **kwa... | 1,778 | 30.210526 | 62 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/models/__init__.py | 0 | 0 | 0 | py | |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/models/builder.py | import numpy as np
from .resnet import ResNet
from .shakenet import ShakeNet
from .cnn13 import CNN13
def gen_model(name, num_classes, img_size):
scale = int(np.ceil(np.log2(img_size)))
if name == "wrn":
return ResNet(num_classes, 32, scale, 4)
elif name == "shake":
return ShakeNet(num_c... | 449 | 25.470588 | 50 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/models/shakenet.py | import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
from .utils import conv3x3, BatchNorm2d, param_init, BaseModel
class _ShakeShake(nn.Module):
def __init__(self, branch1, branch2):
super().__init__()
self.branch1 = branch1
self.branch2 = branch2
def f... | 3,250 | 28.026786 | 107 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/param_scheduler/scheduler.py | import torch
import warnings
import math
import torch.optim as optim
def exp_warmup(base_value, max_warmup_iter, cur_step):
"""exponential warmup proposed in mean teacher
calcurate
base_value * exp(-5(1 - t)^2), t = cur_step / max_warmup_iter
Parameters
-----
base_value: float
maximu... | 1,758 | 24.128571 | 132 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/param_scheduler/__init__.py | 0 | 0 | 0 | py | |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/consistency/mean_squared.py | import torch.nn as nn
import torch.nn.functional as F
def mean_squared(y, target, mask=None):
y = y.softmax(1)
loss = F.mse_loss(y, target, reduction="none").mean(1)
if mask is not None:
loss = mask * loss
return loss.mean()
class MeanSquared(nn.Module):
def forward(self, y, target, mask=N... | 396 | 29.538462 | 61 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/consistency/cross_entropy.py | import torch.nn as nn
import torch.nn.functional as F
def cross_entropy(y, target, mask=None):
if target.ndim == 1: # for hard label
loss = F.cross_entropy(y, target, reduction="none")
else:
loss = -(target * F.log_softmax(y, 1)).sum(1)
if mask is not None:
loss = mask * loss
re... | 486 | 29.4375 | 61 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/consistency/__init__.py | 0 | 0 | 0 | py | |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/consistency/builder.py | from .cross_entropy import CrossEntropy
from .mean_squared import MeanSquared
def gen_consistency(type, cfg):
if type == "ce":
return CrossEntropy()
elif type == "ms":
return MeanSquared()
else:
return None | 244 | 21.272727 | 39 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/datasets/utils.py | import os
import numpy as np
import torch
from torch.utils.data import Sampler
from torchvision.datasets import SVHN, CIFAR10, CIFAR100, STL10
class InfiniteSampler(Sampler):
""" sampling without replacement """
def __init__(self, num_data, num_sample):
epochs = num_sample // num_data + 1
self... | 4,664 | 36.620968 | 105 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/datasets/__init__.py | 0 | 0 | 0 | py | |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/datasets/builder.py | import os
import numpy as np
from torch.utils.data import DataLoader
from torchvision import transforms
from . import utils
from . import dataset_class
from ..augmentation.builder import gen_strong_augmentation, gen_weak_augmentation
from ..augmentation.augmentation_pool import numpy_batch_gcn, ZCA, GCN
def __val_la... | 7,546 | 33.619266 | 105 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/datasets/dataset_class.py | import torch
class LabeledDataset:
"""
For labeled dataset
"""
def __init__(self, dataset, transform=None):
self.dataset = dataset
self.transform = transform
def __getitem__(self, idx):
image = torch.from_numpy(self.dataset["images"][idx]).float()
image = image.per... | 1,422 | 29.276596 | 82 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/misc/__init__.py | 0 | 0 | 0 | py | |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/misc/meter.py | class Meter:
def __init__(self, ema_coef=0.9):
self.ema_coef = ema_coef
self.params = {}
def add(self, params:dict, ignores:list = []):
for k, v in params.items():
if k in ignores:
continue
if not k in self.params.keys():
self.para... | 655 | 27.521739 | 76 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/augmentation/augmentation_pool.py | import random
import torch
import torch.nn.functional as F
import numpy as np
from PIL import ImageOps, ImageEnhance, ImageFilter, Image
"""
For PIL.Image
"""
def autocontrast(x, *args, **kwargs):
return ImageOps.autocontrast(x.convert("RGB")).convert("RGBA")
def brightness(x, level, magnitude=10, max_level=1... | 7,397 | 27.344828 | 98 | py |
pytorch-consistency-regularization | pytorch-consistency-regularization-master/ssl_lib/augmentation/utils.py | FIXMATCH_RANDAUGMENT_OPS_LIST = [
'identity',
'autocontrast',
'brightness',
'color',
'contrast',
'equalize',
'posterize',
'rotate',
'sharpness',
'shear_x',
'shear_y',
'solarize',
'translate_x',
'translate_y'
]
UDA_RANDAUGMENT_OPS_LIST = [
'invert',
'auto... | 907 | 15.214286 | 33 | py |
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