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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arc | arc-master/third_party/nonconformist/cp.py | from nonconformist.icp import *
# TODO: move contents from nonconformist.icp here
# -----------------------------------------------------------------------------
# TcpClassifier
# -----------------------------------------------------------------------------
class TcpClassifier(BaseEstimator, ClassifierMixin):
"""Tra... | 5,299 | 29.813953 | 79 | py |
arc | arc-master/third_party/nonconformist/util.py | from __future__ import division
import numpy as np
def calc_p(ncal, ngt, neq, smoothing=False):
if smoothing:
return (ngt + (neq + 1) * np.random.uniform(0, 1)) / (ncal + 1)
else:
return (ngt + neq + 1) / (ncal + 1)
| 223 | 23.888889 | 65 | py |
arc | arc-master/third_party/nonconformist/__init__.py | #!/usr/bin/env python
"""
docstring
"""
# Authors: Henrik Linusson
# Yaniv Romano modified np.py file to include CQR
__version__ = '2.1.0'
__all__ = ['icp', 'nc', 'acp']
| 174 | 12.461538 | 49 | py |
arc | arc-master/third_party/nonconformist/evaluation.py | #!/usr/bin/env python
"""
Evaluation of conformal predictors.
"""
# Authors: Henrik Linusson
# TODO: cross_val_score/run_experiment should possibly allow multiple to be evaluated on identical folding
from __future__ import division
from nonconformist.base import RegressorMixin, ClassifierMixin
import sys
import n... | 14,339 | 30.378556 | 106 | py |
arc | arc-master/third_party/nonconformist/acp.py | #!/usr/bin/env python
"""
Aggregated conformal predictors
"""
# Authors: Henrik Linusson
import numpy as np
from sklearn.cross_validation import KFold, StratifiedKFold
from sklearn.cross_validation import ShuffleSplit, StratifiedShuffleSplit
from sklearn.base import clone
from nonconformist.base import BaseEstimator... | 10,138 | 26.402703 | 79 | py |
arc | arc-master/third_party/cqr/torch_models.py |
import sys
import copy
import torch
import numpy as np
import torch.nn as nn
from cqr import helper
from sklearn.model_selection import train_test_split
if torch.cuda.is_available():
device = "cuda:0"
else:
device = "cpu"
###############################################################################
# Help... | 17,313 | 33.217391 | 215 | py |
arc | arc-master/third_party/cqr/tune_params_cv.py |
from cqr import helper
from skgarden import RandomForestQuantileRegressor
from sklearn.model_selection import train_test_split
def CV_quntiles_rf(params,
X,
y,
target_coverage,
grid_q,
test_ratio,
random... | 3,123 | 42.388889 | 109 | py |
arc | arc-master/third_party/cqr/helper.py |
import sys
import torch
import numpy as np
from cqr import torch_models
from functools import partial
from cqr import tune_params_cv
from nonconformist.cp import IcpRegressor
from nonconformist.base import RegressorAdapter
from skgarden import RandomForestQuantileRegressor
if torch.cuda.is_available():
device = "... | 22,414 | 36.927242 | 133 | py |
arc | arc-master/third_party/cqr/__init__.py | #!/usr/bin/env python
| 22 | 10.5 | 21 | py |
arc | arc-master/third_party/cqr/nonconformist/nc.py | #!/usr/bin/env python
"""
Nonconformity functions.
"""
# Authors: Henrik Linusson
# Yaniv Romano modified RegressorNc class to include CQR
from __future__ import division
import abc
import numpy as np
import sklearn.base
from nonconformist.base import ClassifierAdapter, RegressorAdapter
from nonconformist.base impo... | 17,678 | 27.79316 | 79 | py |
arc | arc-master/third_party/cqr/nonconformist/base.py | #!/usr/bin/env python
"""
docstring
"""
# Authors: Henrik Linusson
import abc
import numpy as np
from sklearn.base import BaseEstimator
class RegressorMixin(object):
def __init__(self):
super(RegressorMixin, self).__init__()
@classmethod
def get_problem_type(cls):
return 'regression'
class ClassifierMix... | 3,379 | 20.528662 | 63 | py |
arc | arc-master/third_party/cqr/nonconformist/icp.py | #!/usr/bin/env python
"""
Inductive conformal predictors.
"""
# Authors: Henrik Linusson
from __future__ import division
from collections import defaultdict
from functools import partial
import numpy as np
from sklearn.base import BaseEstimator
from nonconformist.base import RegressorMixin, ClassifierMixin
from no... | 13,978 | 30.698413 | 79 | py |
arc | arc-master/third_party/cqr/nonconformist/cp.py | from nonconformist.icp import *
# TODO: move contents from nonconformist.icp here
# -----------------------------------------------------------------------------
# TcpClassifier
# -----------------------------------------------------------------------------
class TcpClassifier(BaseEstimator, ClassifierMixin):
"""Tra... | 5,299 | 29.813953 | 79 | py |
arc | arc-master/third_party/cqr/nonconformist/util.py | from __future__ import division
import numpy as np
def calc_p(ncal, ngt, neq, smoothing=False):
if smoothing:
return (ngt + (neq + 1) * np.random.uniform(0, 1)) / (ncal + 1)
else:
return (ngt + neq + 1) / (ncal + 1)
| 223 | 23.888889 | 65 | py |
arc | arc-master/third_party/cqr/nonconformist/__init__.py | #!/usr/bin/env python
"""
docstring
"""
# Authors: Henrik Linusson
# Yaniv Romano modified np.py file to include CQR
__version__ = '2.1.0'
__all__ = ['icp', 'nc', 'acp']
| 174 | 12.461538 | 49 | py |
arc | arc-master/third_party/cqr/nonconformist/evaluation.py | #!/usr/bin/env python
"""
Evaluation of conformal predictors.
"""
# Authors: Henrik Linusson
# TODO: cross_val_score/run_experiment should possibly allow multiple to be evaluated on identical folding
from __future__ import division
from nonconformist.base import RegressorMixin, ClassifierMixin
import sys
import n... | 14,339 | 30.378556 | 106 | py |
arc | arc-master/third_party/cqr/nonconformist/acp.py | #!/usr/bin/env python
"""
Aggregated conformal predictors
"""
# Authors: Henrik Linusson
import numpy as np
from sklearn.cross_validation import KFold, StratifiedKFold
from sklearn.cross_validation import ShuffleSplit, StratifiedShuffleSplit
from sklearn.base import clone
from nonconformist.base import BaseEstimator... | 10,138 | 26.402703 | 79 | py |
arc | arc-master/third_party/cqr_comparison/cqr.py | import sys
import numpy as np
import pdb
# CQR error function
class QR_errfun():
"""Calculates conformalized quantile regression error.
Conformity scores:
.. math::
max{\hat{q}_low - y, y - \hat{q}_high}
"""
def __init__(self):
super(QR_errfun, self).__init__()
def apply(sel... | 5,191 | 32.496774 | 109 | py |
arc | arc-master/third_party/cqr_comparison/qr_net.py | import numpy as np
import torch
from functools import partial
import pdb
import os, sys
sys.path.insert(0, os.path.abspath("../third_party/"))
from cqr import torch_models
from nonconformist.base import RegressorAdapter
if torch.cuda.is_available():
device = "cuda:0"
else:
device = "cpu"
class NeuralNetworkQ... | 3,909 | 36.961165 | 105 | py |
arc | arc-master/third_party/cqr_comparison/__init__.py | #!/usr/bin/env python
from cqr_comparison.cqr import ConformalizedQR
#from cqr_comparison.qr_forest import RandomForestQR # Note: skgarden has recent compatibility issues
#from cqr_comparison.qr_net import NeuralNetworkQR # Note: skgarden has recent compatibility issues
| 271 | 53.4 | 101 | py |
arc | arc-master/third_party/cqr_comparison/qr_forest.py | import os, sys
import pdb
import numpy as np
from skgarden import RandomForestQuantileRegressor as RF
from sklearn.model_selection import train_test_split
class RandomForestQR:
def __init__(self, params, quantiles, verbose=False):
self.regressor = RF(n_estimators = params['n_estimators'],
... | 3,151 | 37.439024 | 114 | py |
arc | arc-master/experiments_sim_data/run_experiments.py | import numpy as np
from sklearn.model_selection import train_test_split
import pandas as pd
from tqdm import tqdm
import os.path
from os import path
import sys
sys.path.insert(0, '..')
import arc
# Where to write results
out_dir = "~/Workspace/classification/experiments"
def assess_predictions(S, X, y):
# Margin... | 7,029 | 36.393617 | 111 | py |
arc | arc-master/experiments_real_data/all_real_data_experiments.py |
from run_experiment_real_data import run_experiment
alpha = 0.1
DATASET_LIST = ["mice", "fashion", "mnist", "cifar10"]
N_TRAIN_LIST = [500, 1000, 5000, 10000]
# Data sets directory
dataset_base_path = '~/mydata/classification_data/'
# Where to write results
out_dir = "./results"
for EXP_id in range(100):
... | 753 | 25 | 56 | py |
arc | arc-master/experiments_real_data/run_experiment_real_data.py | import numpy as np
from sklearn.model_selection import train_test_split
import pandas as pd
import os.path
from os import path
from datasets import GetDataset
import random
import torch
import sys
sys.path.insert(0, '..')
import arc
def assess_predictions(S, X, y):
# Marginal coverage
coverage = np.mean([y[i]... | 6,950 | 37.192308 | 109 | py |
arc | arc-master/experiments_real_data/datasets.py |
import numpy as np
import pandas as pd
import torch
from torchvision import transforms
import torchvision.datasets as datasets
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
def GetDataset(name, base_path):
""" Load a dataset
Parameters
----------
name... | 4,903 | 31.263158 | 116 | py |
arc | arc-master/arc/black_boxes.py | import numpy as np
from sklearn import svm
from sklearn import ensemble
from sklearn import calibration
from sklearn.neural_network import MLPClassifier
import copy
class Oracle:
def __init__(self, model):
self.model = model
def fit(self,X,y):
return self
def predict(self, X):
... | 5,552 | 32.251497 | 87 | py |
arc | arc-master/arc/classification.py | import numpy as np
class ProbabilityAccumulator:
def __init__(self, prob):
self.n, self.K = prob.shape
self.order = np.argsort(-prob, axis=1)
self.ranks = np.empty_like(self.order)
for i in range(self.n):
self.ranks[i, self.order[i]] = np.arange(len(self.order[i]))
... | 1,908 | 40.5 | 94 | py |
arc | arc-master/arc/others.py | import numpy as np
from sklearn.model_selection import train_test_split
from scipy.stats.mstats import mquantiles
# Note: skgarden has recent compatibility issues
#from skgarden import RandomForestQuantileRegressor
# Note: skgarden has recent compatibility issues
#import sys
#sys.path.insert(0, '../third_party')
#fr... | 9,005 | 40.311927 | 132 | py |
arc | arc-master/arc/coverage.py | import numpy as np
from sklearn.model_selection import train_test_split
from tqdm import tqdm
def wsc(X, y, S, delta=0.1, M=1000, random_state=2020, verbose=False):
rng = np.random.default_rng(random_state)
def wsc_v(X, y, S, delta, v):
n = len(y)
cover = np.array([y[i] in S[i] for i in range(... | 2,607 | 35.732394 | 131 | py |
arc | arc-master/arc/methods.py | import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.model_selection import KFold
from scipy.stats.mstats import mquantiles
import sys
from tqdm import tqdm
from arc.classification import ProbabilityAccumulator as ProbAccum
class CVPlus:
def __init__(self, X, Y, black_box, alpha, n... | 9,536 | 43.35814 | 111 | py |
arc | arc-master/arc/models.py | import numpy as np
from scipy.stats import norm
def sigmoid(x):
return(1/(1 + np.exp(-x)))
class Model_Ex1:
def __init__(self, K, p, magnitude=1):
self.K = K
self.p = p
self.magnitude = magnitude
# Generate model parameters
self.beta_Z = self.magnitude*np.random.randn(s... | 3,189 | 37.902439 | 100 | py |
arc | arc-master/arc/__init__.py | #!/usr/bin/env python
from arc import models
from arc import methods
from arc import black_boxes
from arc import others
from arc import coverage
| 145 | 19.857143 | 27 | py |
QC3_release | QC3_release-main/solution_2pt.py | ################################################################################
# Import relevant python modules
################################################################################
# Standard python modules
import numpy as np, sys, os
np.set_printoptions(precision=6)
pi = np.pi; sqrt=np.sqrt; LA=np.linalg... | 5,199 | 48.056604 | 105 | py |
QC3_release | QC3_release-main/solution.py | ######################################A##########################################
# Import relevant python modules
################################################################################
# Standard python modules
import numpy as np, sys, os
np.set_printoptions(precision=6)
pi = np.pi; sqrt=np.sqrt; LA=np.linal... | 5,304 | 53.690722 | 106 | py |
QC3_release | QC3_release-main/test.py | ################################################################################
# Import relevant python modules
################################################################################
# Standard python modules
import numpy as np, sys, os
np.set_printoptions(precision=3)
pi = np.pi; sqrt=np.sqrt; LA=np.linalg... | 4,729 | 52.75 | 92 | py |
QC3_release | QC3_release-main/solution_ND.py | ######################################A##########################################
# Import relevant python modules
################################################################################
# Standard python modules
import numpy as np, sys, os
np.set_printoptions(precision=6)
pi = np.pi; sqrt=np.sqrt; LA=np.linal... | 5,230 | 55.247312 | 102 | py |
QC3_release | QC3_release-main/solution_ID.py | ######################################A##########################################
# Import relevant python modules
################################################################################
# Standard python modules
import numpy as np, sys, os
np.set_printoptions(precision=6)
pi = np.pi; sqrt=np.sqrt; LA=np.linal... | 4,280 | 50.578313 | 102 | py |
QC3_release | QC3_release-main/base_code/defns.py | import numpy as np
pi=np.pi; LA=np.linalg
from itertools import permutations as perms
from constants import *
# from numba import jit,njit
####################################################################################
# This file defines several basic functions that get called multiple times
###################... | 13,598 | 28.182403 | 139 | py |
QC3_release | QC3_release-main/base_code/constants.py |
################################################################################
# Spectator cutoff constants
################################################################################
# Set xmin, xmax for J cutoff fn.
def get_xrange():
xmin = 0.02
xmax = 0.97
return xmin, xmax
# Set epsH for H cutoff fn.... | 781 | 26.928571 | 80 | py |
QC3_release | QC3_release-main/base_code/group_theory_defns.py | import numpy as np, sys
pi=np.pi; conj=np.conjugate; LA=np.linalg
from itertools import permutations as perms
import defns; sqrt=defns.sqrt
#from projections import l0_proj, l2_proj
from scipy.linalg import block_diag
####################################################################################
# Group theory... | 23,358 | 28.419395 | 198 | py |
QC3_release | QC3_release-main/base_code/projections.py | import numpy as np
sqrt=np.sqrt; pi=np.pi; LA=np.linalg
from scipy.linalg import block_diag
import defns, group_theory_defns as GT
import sys
################################################################################
''' Here we implement projections onto all little group irreps '''
############################... | 9,770 | 39.882845 | 117 | py |
QC3_release | QC3_release-main/base_code/F3/F3_mat.py | import numpy as np
sqrt=np.sqrt; pi=np.pi; LA=np.linalg
#from numba import jit,njit
import defns, F_mat, G_mat, K2i_mat
##############################################################
# Compute full matrix F3 for 2+1 systems
##############################################################
def F3mat_2plus1(E,L,nnP, f_qcot_... | 2,254 | 45.979167 | 119 | py |
QC3_release | QC3_release-main/base_code/F3/qcot_fits.py | import numpy as np, sys
sqrt = np.sqrt; pi=np.pi
# s-wave phase shift model (for K2)
#@njit(fastmath=True)
def qcot_fit_s(q2,par_vec,ERE=True): # Use effective range expansion by default
if ERE==True:
a0 = par_vec[0]
qcot = -1/a0
if len(par_vec)==2:
r = par_vec[1]
qcot += 1/2*r*q... | 762 | 25.310345 | 93 | py |
QC3_release | QC3_release-main/base_code/F3/K2i_mat.py | import numpy as np
sqrt=np.sqrt; pi=np.pi; LA=np.linalg
from scipy.linalg import block_diag
import defns
# from numba import jit,njit
################################################################################
# Calculate matrix element of K2i_inv/(2*omega), no L^3
################################################... | 5,743 | 44.587302 | 130 | py |
QC3_release | QC3_release-main/base_code/F3/G_mat.py | import numpy as np
sqrt=np.sqrt; pi=np.pi; LA=np.linalg
# import sums_mov as sums
import defns
# from numba import jit,njit
################################################################################
# Compute individual matrix element of Gtilde^{ij} = G^{ij}/(2*omega*L^3)
########################################... | 6,725 | 34.776596 | 132 | py |
QC3_release | QC3_release-main/base_code/F3/F_mat.py | import numpy as np, sys
pi=np.pi; LA=np.linalg; exp=np.exp
from scipy.linalg import block_diag
from scipy.special import erfi,erfc
from scipy.optimize import fsolve
import defns
from constants import *
sqrt = defns.sqrt
################################################################################
# Find maximum n ... | 8,364 | 36.511211 | 119 | py |
QC3_release | QC3_release-main/base_code/Kdf3/K3E.py | import numpy as np
#sqrt=np.sqrt
pi=np.pi; conj=np.conjugate; LA=np.linalg;
import defns
sqrt=defns.sqrt
#################################################################
# Want to compute K3E term in Kdf3 for 2+1 systems
#################################################################
# Compute 4x4 block of K3E for g... | 3,800 | 35.902913 | 209 | py |
QC3_release | QC3_release-main/base_code/Kdf3/K3B.py | import defns
import numpy as np
sqrt=np.sqrt; pi=np.pi; LA=np.linalg
#################################################################
# Want to compute K3B term in Kdf3 for 2+1 systems
#################################################################
# Compute contributions to K3B for given (i,pvec; j,kvec)
def K3B_... | 2,306 | 35.619048 | 117 | py |
QC3_release | QC3_release-main/base_code/Kdf3/K3main.py | import numpy as np
sqrt=np.sqrt; pi=np.pi; LA=np.linalg;
import K3B, K3E
import defns
#################################################################
# Full linear-order threshold expansion of Kdf3 for 2+1 systems
#################################################################
# Note: input order for all functio... | 4,279 | 36.54386 | 117 | py |
signed-oracle | signed-oracle-main/implementation/main.py | import Eval
Eval.runAllSyntheticExperiments()
Eval.runAllRealWorldExperiments()
| 82 | 12.833333 | 33 | py |
signed-oracle | signed-oracle-main/implementation/GraphStats.py | import numpy
import Graph
def printGraphStats(G):
print(f'n:\t\t {G.numVertices}')
print(f'|E|:\t\t {G.numEdges}')
print(f'|E+|:\t\t {G.numPositiveEdges}')
print(f'|E-|:\t\t {G.numNegativeEdges}')
print(f'|E-|/|E|:\t {G.numNegativeEdges / G.numEdges}')
print(' ')
degrees = []
for u in G.edges.keys():
degre... | 537 | 23.454545 | 56 | py |
signed-oracle | signed-oracle-main/implementation/Graph.py | import random
import numpy
import networkx as nx
import time
class Graph:
'''
The graph is stored as a dict over unordered lists. Because of how the
graph is stored, the vertex ids start at 1 (and not at 0).
For positive edges, the list contains the index of the vertex, and for
negative edges, the list cont... | 4,086 | 22.624277 | 84 | py |
signed-oracle | signed-oracle-main/implementation/Eval.py | import numpy
import random
import os
import sys
import time
import networkx as nx
from networkx.algorithms import bipartite
import GraphReader
import GraphStats
import SyntheticData
import OracleModule
sys.path.insert(1, 'include/signed-local-community-master')
from core import query_graph_using_sparse_linear_solv... | 17,372 | 29.532513 | 210 | py |
signed-oracle | signed-oracle-main/implementation/OracleModule.py | import os
def getInitCommand(inputfile,
numSteps,
numWalks,
seedClusters=None,
k=None,
s=None,
numEstNorm=1,
unsigned=False,
biclustering=False):
command = './oracle '
command += f'{inputfile} '
command += f'{str(numSteps)} '
command += f'{str(numWalks)}... | 2,934 | 23.057377 | 134 | py |
signed-oracle | signed-oracle-main/implementation/SyntheticData.py | import numpy
import Graph
'''
Generates a signed SBM with n vertices and 2*k equally sized bi-clusters.
Intra-cluster (+,+)-edges are inserted with probability pIntra and
intra-cluster (+,-)-edges are inserted with probability pCross. The edges
have the ``correct'' sign with probability pSign. Inter-cluster edges... | 2,948 | 29.091837 | 102 | py |
signed-oracle | signed-oracle-main/implementation/GraphReader.py | import numpy
import Graph
'''
Reads a sparse csv file and returns a Graph object.
Assumes that the csv-file has the following format:
u,v,edgeWeight
where u and v are integers and edgeWeight is a float
'''
def graphFromSparseCSV(inputfile, separator=',', skipHeader=False, inputIsZeroIndexed=False):
'''
since ... | 1,414 | 21.822581 | 93 | py |
RefVAE | RefVAE-main/main_GAN.py | from __future__ import print_function
import argparse
from math import log10
import os
import torch
import torch.nn as nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
from laploss import LapLoss
from torch.utils.data import DataLoader
import torch.nn.functional as F
from model import *
from network... | 10,971 | 37.633803 | 147 | py |
RefVAE | RefVAE-main/test.py | from __future__ import print_function
import argparse
import os
import torch
import cv2
from model import *
import torchvision.transforms as transforms
from collections import OrderedDict
import numpy as np
from os.path import join
import time
from network import encoder4, decoder4
import numpy
from dataset import is_... | 8,446 | 35.5671 | 102 | py |
RefVAE | RefVAE-main/image_utils.py | import torch
import numpy as np
from PIL import Image
import math
import cv2
class TVLoss(torch.nn.Module):
def __init__(self):
super(TVLoss,self).__init__()
def forward(self,x):
batch_size = x.size()[0]
h_x = x.size()[2]
w_x = x.size()[3]
count_h = self._tensor_size(x[... | 9,144 | 37.104167 | 184 | py |
RefVAE | RefVAE-main/network.py | import torch
import torch.nn as nn
class encoder3(nn.Module):
def __init__(self):
super(encoder3,self).__init__()
# vgg
# 224 x 224
self.conv1 = nn.Conv2d(3,3,1,1,0)
self.reflecPad1 = nn.ReflectionPad2d((1,1,1,1))
# 226 x 226
self.conv2 = nn.Conv2d(3,64,3,1,... | 31,991 | 31.611621 | 125 | py |
RefVAE | RefVAE-main/model.py | import torch
import torch.nn as nn
from torch.nn import functional as F
import math
from torchvision import models
class ncc_test(nn.Module):
"""Residual Channel Attention Networks.
Paper: Image Super-Resolution Using Very Deep Residual Channel Attention
Networks
Ref git repo: https://github.com/... | 47,234 | 33.129335 | 116 | py |
RefVAE | RefVAE-main/dataset.py | import torch.utils.data as data
import torch
import numpy as np
import os
from os import listdir
from os.path import join
from PIL import Image, ImageOps, ImageEnhance
import random
from torchvision import transforms
from glob import glob
from imresize import imresize
def is_image_file(filename):
return any(filen... | 8,504 | 32.093385 | 108 | py |
RefVAE | RefVAE-main/data.py | from os.path import join
from torchvision import transforms
from dataset import DatasetFromFolderEval, DatasetFromFolder
def transform():
return transforms.Compose([
transforms.ToTensor(),
# Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
# def transform(fineSize):
# return transforms.Comp... | 887 | 26.75 | 84 | py |
RefVAE | RefVAE-main/laploss.py | import numpy as np
from PIL import Image
import torch
from torch import nn
import torch.nn.functional as fnn
from torch.autograd import Variable
def build_gauss_kernel(size=5, sigma=1.0, n_channels=1, cuda=False):
if size % 2 != 1:
raise ValueError("kernel size must be uneven")
grid = np.float32(np.m... | 3,025 | 35.457831 | 91 | py |
RefVAE | RefVAE-main/eval_4x.py | from __future__ import print_function
import argparse
import os
import torch
import cv2
from model import *
import torchvision.transforms as transforms
from collections import OrderedDict
import numpy as np
from os.path import join
import time
from network import encoder4, decoder4
import numpy
from dataset import is_... | 7,250 | 33.528571 | 109 | py |
RefVAE | RefVAE-main/eval_8x.py | from __future__ import print_function
import argparse
import os
import torch
import cv2
from model import *
import torchvision.transforms as transforms
from collections import OrderedDict
import numpy as np
from os.path import join
import time
from network import encoder4, decoder4
import numpy
from dataset import is_... | 7,250 | 33.528571 | 109 | py |
1L-3NErrors | 1L-3NErrors-main/main_aberr.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""NLTE corrections calculator
This code takes stellar parameters and line parameters for a specific line
and calculates the NLTE corrections, based on results by Amarsi+2022.
A neural network is used for interpolation. For more information about
how to prepare the input ... | 4,257 | 29.414286 | 105 | py |
1L-3NErrors | 1L-3NErrors-main/function_aberr.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""NLTE corrections functions
Functions to calculate the NLTE corrections aberr, used in the main_aberr.py program.
For more information about how to prepare input files and run the main program, or
how to use the functions provided in this script see the README files.
Vi... | 3,479 | 37.666667 | 288 | py |
TrianFlow | TrianFlow-master/test.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from core.dataset import KITTI_2012, KITTI_2015
from core.evaluation import eval_flow_avg, load_gt_flow_kitti
from core.evaluation import eval_depth
from core.visualize import Visualizer_debug
from core.networks import Model_depth_pose, Model_fl... | 10,749 | 40.030534 | 144 | py |
TrianFlow | TrianFlow-master/train.py | import os, sys
import yaml
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from core.dataset import KITTI_RAW, KITTI_Prepared, NYU_Prepare, NYU_v2, KITTI_Odo
from core.networks import get_model
from core.config import generate_loss_weights_dict
from core.visualize import Visualizer
from core.evaluation impo... | 10,881 | 49.37963 | 169 | py |
TrianFlow | TrianFlow-master/infer_vo.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from core.networks.model_depth_pose import Model_depth_pose
from core.networks.model_flow import Model_flow
from visualizer import *
from profiler import Profiler
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as... | 13,467 | 38.964392 | 223 | py |
TrianFlow | TrianFlow-master/core/evaluation/evaluate_mask.py | import os
import numpy as np
import cv2
import functools
import matplotlib.pyplot as plt
import multiprocessing
"""
Adopted from https://github.com/martinkersner/py_img_seg_eval
"""
class EvalSegErr(Exception):
def __init__(self, value):
self.value = value
def __str__(self):
return repr(self.... | 6,269 | 23.782609 | 87 | py |
TrianFlow | TrianFlow-master/core/evaluation/flowlib.py | #!/usr/bin/python
"""
Adopted from https://github.com/liruoteng/OpticalFlowToolkit
# ==============================
# flowlib.py
# library for optical flow processing
# Author: Ruoteng Li
# Date: 6th Aug 2016
# ==============================
"""
import png
import scipy
import numpy as np
import matplotlib.colors as cl
... | 14,420 | 25.656192 | 90 | py |
TrianFlow | TrianFlow-master/core/evaluation/evaluate_flow.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import numpy as np
from flowlib import read_flow_png, flow_to_image
import cv2
import multiprocessing
import functools
def get_scaled_intrinsic_matrix(calib_file, zoom_x, zoom_y):
intrinsics = load_intrinsics_raw(calib_file)
intrinsics ... | 6,496 | 36.125714 | 109 | py |
TrianFlow | TrianFlow-master/core/evaluation/eval_odom.py | import copy
from matplotlib import pyplot as plt
import numpy as np
import os
from glob import glob
import pdb
def scale_lse_solver(X, Y):
"""Least-sqaure-error solver
Compute optimal scaling factor so that s(X)-Y is minimum
Args:
X (KxN array): current data
Y (KxN array): reference data
... | 13,822 | 36.975275 | 121 | py |
TrianFlow | TrianFlow-master/core/evaluation/evaluation_utils.py | import numpy as np
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import cv2, skimage
import skimage.io
#import scipy.misc as sm
import imageio as sm
# Adopted from https://github.com/mrharicot/monodepth
def compute_errors(gt, pred, nyu=False):
thresh = np.maximum((gt / pred), (pred / ... | 870 | 24.617647 | 60 | py |
TrianFlow | TrianFlow-master/core/evaluation/__init__.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from evaluate_flow import eval_flow_avg, load_gt_flow_kitti
from evaluate_mask import load_gt_mask
from evaluate_depth import eval_depth
| 212 | 34.5 | 59 | py |
TrianFlow | TrianFlow-master/core/evaluation/evaluate_depth.py | from evaluation_utils import *
def process_depth(gt_depth, pred_depth, min_depth, max_depth):
mask = gt_depth > 0
pred_depth[pred_depth < min_depth] = min_depth
pred_depth[pred_depth > max_depth] = max_depth
gt_depth[gt_depth < min_depth] = min_depth
gt_depth[gt_depth > max_depth] = max_depth
... | 1,977 | 35.62963 | 103 | py |
TrianFlow | TrianFlow-master/core/networks/model_flow.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from structures import *
from pytorch_ssim import SSIM
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import pdb
import cv2
def transformerFwd(U,
flo,
out_size,
... | 18,005 | 46.384211 | 201 | py |
TrianFlow | TrianFlow-master/core/networks/model_flowposenet.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from structures import *
from pytorch_ssim import SSIM
from model_flow import Model_flow
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'visualize'))
from visualizer import *
from profiler import Profiler
import t... | 6,517 | 35.824859 | 178 | py |
TrianFlow | TrianFlow-master/core/networks/model_triangulate_pose.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import torch
import torch.nn as nn
import numpy as np
from structures import *
from model_flow import Model_flow
import pdb
import cv2
class Model_triangulate_pose(nn.Module):
def __init__(self, cfg):
super(Model_triangulate_pose, s... | 5,969 | 47.536585 | 182 | py |
TrianFlow | TrianFlow-master/core/networks/__init__.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from model_flow import Model_flow
from model_triangulate_pose import Model_triangulate_pose
from model_depth_pose import Model_depth_pose
from model_flowposenet import Model_flowposenet
def get_model(mode):
if mode == 'flow':
return... | 635 | 32.473684 | 59 | py |
TrianFlow | TrianFlow-master/core/networks/model_depth_pose.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from structures import *
from model_triangulate_pose import Model_triangulate_pose
from pytorch_ssim import SSIM
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'visualize'))
from visualizer import *
import torch
i... | 30,237 | 53.978182 | 188 | py |
TrianFlow | TrianFlow-master/core/networks/pytorch_ssim/ssim.py | import torch
import torch.nn as nn
def SSIM(x, y):
C1 = 0.01 ** 2
C2 = 0.03 ** 2
mu_x = nn.AvgPool2d(3, 1, padding=1)(x)
mu_y = nn.AvgPool2d(3, 1, padding=1)(y)
sigma_x = nn.AvgPool2d(3, 1, padding=1)(x**2) - mu_x**2
sigma_y = nn.AvgPool2d(3, 1, padding=1)(y**2) - mu_y**2
sigma_xy = nn.Av... | 535 | 24.52381 | 65 | py |
TrianFlow | TrianFlow-master/core/networks/pytorch_ssim/__init__.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from ssim import SSIM
| 98 | 18.8 | 59 | py |
TrianFlow | TrianFlow-master/core/networks/structures/ransac.py | import torch
import numpy as np
import os, sys
import torch.nn as nn
import pdb
import cv2
class reduced_ransac(nn.Module):
def __init__(self, check_num, thres, dataset):
super(reduced_ransac, self).__init__()
self.check_num = check_num
self.thres = thres
self.dataset = dataset
... | 3,145 | 45.955224 | 189 | py |
TrianFlow | TrianFlow-master/core/networks/structures/depth_model.py | '''
This code was ported from existing repos
[LINK] https://github.com/nianticlabs/monodepth2
'''
from __future__ import absolute_import, division, print_function
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional a... | 7,964 | 36.394366 | 92 | py |
TrianFlow | TrianFlow-master/core/networks/structures/flowposenet.py | import torch
import torch.nn as nn
from torch import sigmoid
from torch.nn.init import xavier_uniform_, zeros_
def conv(in_planes, out_planes, kernel_size=3):
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, padding=(kernel_size-1)//2, stride=2),
nn.ReLU(inplace=True... | 1,951 | 30.483871 | 104 | py |
TrianFlow | TrianFlow-master/core/networks/structures/feature_pyramid.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from net_utils import conv
import torch
import torch.nn as nn
class FeaturePyramid(nn.Module):
def __init__(self):
super(FeaturePyramid, self).__init__()
self.conv1 = conv(3, 16, kernel_size=3, stride=2)
self.conv2... | 1,586 | 40.763158 | 77 | py |
TrianFlow | TrianFlow-master/core/networks/structures/inverse_warp.py | from __future__ import division
import torch
import torch.nn.functional as F
pixel_coords = None
def set_id_grid(depth):
global pixel_coords
b, h, w = depth.size()
i_range = torch.arange(0, h).view(1, h, 1).expand(
1, h, w).type_as(depth) # [1, H, W]
j_range = torch.arange(0, w).view(1, 1, w... | 10,018 | 36.107407 | 119 | py |
TrianFlow | TrianFlow-master/core/networks/structures/__init__.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from feature_pyramid import FeaturePyramid
from pwc_tf import PWC_tf
from ransac import reduced_ransac
from depth_model import Depth_Model
from net_utils import conv, deconv, warp_flow
from flowposenet import FlowPoseNet
from inverse_warp import... | 335 | 32.6 | 59 | py |
TrianFlow | TrianFlow-master/core/networks/structures/pwc_tf.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from net_utils import conv, deconv, warp_flow
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', '..', 'external'))
# from correlation_package.correlation import Correlation
# from spatial_correlation_sampler import S... | 8,423 | 45.541436 | 97 | py |
TrianFlow | TrianFlow-master/core/networks/structures/net_utils.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import pdb
import numpy as np
def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1):
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
pa... | 2,088 | 32.693548 | 119 | py |
TrianFlow | TrianFlow-master/core/config/config_utils.py | import os, sys
def generate_loss_weights_dict(cfg):
weight_dict = {}
weight_dict['loss_pixel'] = 1 - cfg.w_ssim
weight_dict['loss_ssim'] = cfg.w_ssim
weight_dict['loss_flow_smooth'] = cfg.w_flow_smooth
weight_dict['loss_flow_consis'] = cfg.w_flow_consis
weight_dict['geo_loss'] = cfg.w_geo
w... | 546 | 33.1875 | 57 | py |
TrianFlow | TrianFlow-master/core/config/__init__.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from config_utils import generate_loss_weights_dict
| 128 | 24.8 | 59 | py |
TrianFlow | TrianFlow-master/core/dataset/kitti_raw.py | import os, sys
import numpy as np
import imageio
from tqdm import tqdm
import torch.multiprocessing as mp
import pdb
def process_folder(q, static_frames, test_scenes, data_dir, output_dir, stride=1):
while True:
if q.empty():
break
folder = q.get()
if folder in static_frames.key... | 8,568 | 40.8 | 150 | py |
TrianFlow | TrianFlow-master/core/dataset/__init__.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from kitti_raw import KITTI_RAW
from kitti_prepared import KITTI_Prepared
from kitti_2012 import KITTI_2012
from kitti_2015 import KITTI_2015
from nyu_v2 import NYU_Prepare, NYU_v2
from kitti_odo import KITTI_Odo | 287 | 35 | 59 | py |
TrianFlow | TrianFlow-master/core/dataset/nyu_v2.py | import os, sys
import numpy as np
import imageio
import cv2
import copy
import h5py
import scipy.io as sio
import torch
import torch.utils.data
import pdb
from tqdm import tqdm
import torch.multiprocessing as mp
def collect_image_list(path):
# Get ppm images list of a folder.
files = os.listdir(path)
sorte... | 13,298 | 36.997143 | 150 | py |
TrianFlow | TrianFlow-master/core/dataset/kitti_2015.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from kitti_2012 import KITTI_2012
class KITTI_2015(KITTI_2012):
def __init__(self, data_dir, img_hw=(256, 832)):
super(KITTI_2015, self).__init__(data_dir, img_hw, init=False)
self.num_total = 200
self.data_list = s... | 378 | 24.266667 | 70 | py |
TrianFlow | TrianFlow-master/core/dataset/kitti_2012.py | import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
from kitti_prepared import KITTI_Prepared
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'evaluation'))
from evaluate_flow import get_scaled_intrinsic_matrix, eval_flow_avg
import numpy as np
import cv2
import cop... | 2,288 | 35.333333 | 110 | py |
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