repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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mmda | mmda-main/src/mmda/predictors/hf_predictors/bibentry_predictor/predictor.py | import os
import re
from typing import Dict, List, Optional, Tuple
from optimum.onnxruntime import ORTModelForTokenClassification
import torch
from transformers import AutoConfig, AutoTokenizer, AutoModelForTokenClassification
from unidecode import unidecode
from mmda.predictors.hf_predictors.base_hf_predictor import... | 9,946 | 43.806306 | 193 | py |
mmda | mmda-main/src/ai2_internal/vila/interface.py | """
This file contains the classes required by Semantic Scholar's
TIMO tooling.
You must provide a wrapper around your model, as well
as a definition of the objects it expects, and those it returns.
"""
import logging
from typing import List
import torch
from pydantic import BaseModel, BaseSettings, Field
from ai2_... | 3,302 | 27.721739 | 92 | py |
mmda | mmda-main/src/ai2_internal/layout_parser/interface.py | """
This file contains the classes required by Semantic Scholar's
TIMO tooling.
You must provide a wrapper around your model, as well
as a definition of the objects it expects, and those it returns.
"""
import logging
from typing import List
import torch
from pydantic import BaseModel, BaseSettings, Field
from ai2_... | 3,946 | 32.449153 | 85 | py |
mmda | mmda-main/tests/test_recipes/core_recipe_fixtures.py | FIRST_1000_SYMBOLS = """Field\nTask\nDataset\nSOTA\nB ERT -Base\nS CI B ERT\nFrozen\nFinetune\nFrozen\nFinetune\nBio\nNER\nBC5CDR (Li et al., 2016)\n88.85 7\n85.08\n86.72\n88.73\n90.01\nJNLPBA (Collier and Kim, 2004)\n78.58\n74.05\n76.09\n75.77\n77.28\nNCBI-disease (Dogan et al., 2014)\n89.36\n84.06\n86.88\n86.39\n88.5... | 249,118 | 509.489754 | 234,906 | py |
PRISim | PRISim-master/prisim/interferometry.py | from __future__ import division
import numpy as NP
import scipy.constants as FCNST
from scipy import interpolate, ndimage
import datetime as DT
import progressbar as PGB
import os, ast
import copy
import astropy
from astropy.io import fits, ascii
from astropy.coordinates import Galactic, SkyCoord, ICRS, FK5, AltAz, Ear... | 579,057 | 57.526177 | 587 | py |
PRISim | PRISim-master/scripts/altsim_interface.py | #!python
import yaml, argparse, ast, warnings
import numpy as NP
from astropy.io import ascii
from astropy.time import Time
import prisim
prisim_path = prisim.__path__[0]+'/'
def simparms_from_pyuvsim_to_prisim(pyuvsim_parms, prisim_parms):
if not isinstance(pyuvsim_parms, dict):
raise TypeError('Input p... | 8,667 | 49.988235 | 376 | py |
PRISim | PRISim-master/scripts/run_prisim.py | #!python
import os, shutil, subprocess, pwd, errno, warnings
from mpi4py import MPI
import yaml
import h5py
import argparse
import copy
import numpy as NP
from astropy.io import fits, ascii
from astropy.coordinates import Galactic, FK5, ICRS, SkyCoord, AltAz, EarthLocation
from astropy import units as U
from astropy.t... | 122,758 | 51.461111 | 537 | py |
dstqa | dstqa-master/multiwoz_format.py | // Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
// Licensed under the Amazon Software License http://aws.amazon.com/asl/
import sys
import os
import json
import pdb
import copy
import random
assert(len(sys.argv) == 4)
ontology_... | 15,023 | 35.914005 | 171 | py |
dstqa | dstqa-master/multiwoz_2.1_format.py | // Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
// Licensed under the Amazon Software License http://aws.amazon.com/asl/
import sys
import os
import json
import pdb
import copy
import random
assert(len(sys.argv) == 4)
ontology_... | 18,246 | 35.567134 | 171 | py |
dstqa | dstqa-master/dstqa/dstqa.py | // Copyright 2019 Amazon.com, Inc. or its affiliates. All Rights Reserved.
// SPDX-License-Identifier: LicenseRef-.amazon.com.-AmznSL-1.0
// Licensed under the Amazon Software License http://aws.amazon.com/asl/
import pdb
import math
import logging
import os.path
import pickle
import random
from typing import Any, Di... | 26,073 | 48.103578 | 193 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Lorenz/generate.py | from turtle import color
import numpy as np
import math
import torch
import timeit
import numpy as np
import matplotlib.pyplot as plt
# import matplotlib
# matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
# matplotlib.rcParams['text.usetex'] = True
colors = [
[233/256, 110/256, 236/256], # #e96e... | 12,969 | 35.432584 | 103 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Lorenz/ES_ICNN.py | import torch.nn.functional as F
import timeit
from hessian import hessian
from hessian import jacobian
# from gradient import hessian
# from gradient import jacobian
import torch
import random
import numpy as np
def setup_seed(seed):
torch.manual_seed(seed)
# torch.cuda.manual_seed_all(seed)
# torch.cuda.... | 4,181 | 31.169231 | 169 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Lorenz/ES_Quadratic.py | import torch.nn.functional as F
import timeit
from hessian import hessian
from hessian import jacobian
# from gradient import hessian
# from gradient import jacobian
import torch
import random
import math
import numpy as np
def setup_seed(seed):
torch.manual_seed(seed)
# torch.cuda.manual_seed_all(seed)
# ... | 5,582 | 30.016667 | 142 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Lorenz/Control_Nonlinear_Icnn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class ICNN(nn.Module):
def __init__(self, input_shape, layer_sizes, activation_fn):
super(ICNN, self).__init__()
self._input_shape = input_shape
self._layer_sizes = layer_sizes
self._activation_fn = activation_fn
... | 3,750 | 34.386792 | 122 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Energy/AS.py | import torch
import torch.nn.functional as F
import timeit
import math
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = torch.nn.Linear(n_h... | 1,720 | 21.064103 | 70 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Energy/functions.py | import numpy as np
import math
import torch
import timeit
from scipy import integrate
start = timeit.default_timer()
np.random.seed(1)
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.L... | 3,792 | 26.092857 | 129 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Energy/plot.py | import numpy as np
import matplotlib.pyplot as plt
import torch
import matplotlib
matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
matplotlib.rcParams['text.usetex'] = True
def plot_grid():
plt.grid(b=True, which='major', color='gray', alpha=0.6, linestyle='dashdot', lw=1.5)
# minor grid lin... | 3,641 | 34.359223 | 127 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/stuart/AS.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = torch.nn.Lin... | 1,843 | 22.341772 | 82 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/stuart/generate.py | import numpy as np
from scipy import integrate
import torch
import matplotlib.pyplot as plt
import math
import timeit
from scipy.integrate import odeint
import sys
sys.path.append('./neural_sde/stuart')
from AS import *
from functions import *
start = timeit.default_timer()
stuart_model = Net(D_in,H1,D_out)
# stuar... | 1,915 | 24.210526 | 96 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/stuart/functions.py | import torch
import numpy as np
import timeit
import matplotlib.pyplot as plt
'''
x = rho_1,rho_2,rho_n, w1,w2,wn-1
'''
#Transform \Tilde{\theta} to \theta
def theta(W):
W = torch.cat([W,torch.tensor([1.0])],0)
T = torch.eye(len(W))
for i in range(len(T)):
for k in range(len(T)):
... | 3,921 | 30.376 | 181 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/stuart/plot.py | from functions import *
import numpy as np
import torch
import matplotlib.pyplot as plt
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
# import matplotlib
# matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
# matplotlib.rcParams['text.usetex'] = True
font_size = 35
def plot_grid():
plt.grid(b... | 4,294 | 34.204918 | 184 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/inverted_pendulum/invert_pendulum_control_1227.py | import numpy as np
import math
import torch
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.gridspec as gridspec
from functions import *
from base_function import colors
alpha = 1.0
fontsize=35
fontsize_legend = 20
MarkerSize = 60
linewidth = 5
color_w = 0.15 #0.5
framealpha = 0.7
N_seg = ... | 6,416 | 33.315508 | 129 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/inverted_pendulum/algo2.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
import math
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = ... | 2,276 | 24.021978 | 141 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/inverted_pendulum/functions.py | import numpy as np
import math
import torch
import timeit
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.gridspec as gridspec
from scipy.integrate import odeint
import numpy as np
np.random.seed(10)
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
... | 4,192 | 29.830882 | 127 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/plot_trajectory.py | from statistics import mean
import sys
sys.path.append('./neural_sde')
import numpy as np
import math
import matplotlib.pyplot as plt
import torch
from mpl_toolkits.mplot3d import axes3d
from matplotlib import cm
import timeit
# import pylustrator
# pylustrator.start()
start = timeit.default_timer()
A = torch.load('.... | 816 | 26.233333 | 105 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/plot_loss.py | import numpy as np
import matplotlib.pyplot as plt
import torch
import pylustrator
pylustrator.start()
import seaborn as sns
sns.set_theme(style="white")
def plot_a(a):
L = np.load('./neural_sde/hyper_a/a_{}.npy'.format(a))
r_L = np.zeros(1000-len(L))
L = np.concatenate((L,r_L),axis=0)
# np.concaten... | 1,949 | 30.967213 | 110 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/AS.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = torch.nn.Lin... | 2,236 | 25.011628 | 141 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/test.py | import sys
sys.path.append('./neural_sde')
import numpy as np
import math
import matplotlib.pyplot as plt
import torch
from mpl_toolkits.mplot3d import axes3d
from matplotlib import cm
import timeit
A = torch.ones(2,100)
# B = torch.diagonal(A)
print(A[:,0:100:10].shape) | 273 | 20.076923 | 39 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/generate.py | import numpy as np
import math
import torch
import timeit
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(10)
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_in... | 2,698 | 28.021505 | 92 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/u_plot.py | import matplotlib.pyplot as plt
import torch
import numpy as np
from matplotlib import cm
import matplotlib as mpl
class ControlNet(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(ControlNet, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_i... | 1,330 | 26.729167 | 80 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/functions.py | from os import stat
import numpy as np
import math
import torch
import timeit
import random
import matplotlib.pyplot as plt
from matplotlib import cm
from scipy.integrate import odeint
import numpy as np
np.random.seed(10)
class ControlNet(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
... | 4,265 | 30.6 | 127 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/calculate.py | import matplotlib.pyplot as plt
import torch
import numpy as np
def plot_grid():
plt.grid(b=True, which='major', color='gray', alpha=0.5, linestyle='dashdot', lw=1.5)
# minor grid lines
plt.minorticks_on()
plt.grid(b=True, which='minor', color='beige', alpha=0.5, ls='-', lw=1)
'''
Calculate and plot t... | 1,901 | 40.347826 | 207 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_a/plot.py | import numpy as np
import matplotlib.pyplot as plt
from u_plot import *
from plot_trajectory import *
# import matplotlib
# matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
# matplotlib.rcParams['text.usetex'] = True
font_size = 15
'''
Pick trajectories data for corresponding $\alpha$
'''
A = torch.... | 2,666 | 27.98913 | 89 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hopf/AS.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = torch.nn.Lin... | 2,120 | 24.554217 | 143 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hopf/generate.py | import numpy as np
import math
import matplotlib.pyplot as plt
import torch
import timeit
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = t... | 3,077 | 27.766355 | 80 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hopf/functions.py | import numpy as np
import torch
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.gridspec as gridspec
#向量场
def f(y,t) :
#parameters
x1,x2 = y
dydt = [-25.0*x1-x2+x1*(x1**2+x2**2),x1-25*x2+x2*(x1**2+x2**2)]
return dydt
#绘制向量场
def Plotflow(Xd, Yd, t):
# Plot phase ... | 4,576 | 33.674242 | 92 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Echo/AS.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
import argparse
parser = argparse.ArgumentParser('ODE demo')
parser.add_argument('--N', type=float, default=5000)
parser.add_argument('--lr', type=float, default=0.03)
args = parser.parse_args()
class Net(torch.nn.Module):
def __i... | 2,257 | 24.954023 | 141 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/Echo/generate.py | import numpy as np
import torch
import math
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = torch.nn.Linear(n_hidden,n_hidden)
self.... | 2,073 | 26.653333 | 77 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_b/plot_trajectory.py | import numpy as np
import math
import matplotlib.pyplot as plt
import torch
from mpl_toolkits.mplot3d import axes3d
from matplotlib import cm
import timeit
start = timeit.default_timer()
def plot_trajec(L,b):
mean_data = torch.mean(L,0).detach().numpy()
std_data =torch.std(L,0).detach().numpy()
plt.fi... | 639 | 28.090909 | 105 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_b/V_plot.py | import matplotlib.pyplot as plt
import torch
import numpy as np
from matplotlib import cm
import matplotlib as mpl
# import matplotlib
# matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
# matplotlib.rcParams['text.usetex'] = True
colors = [
[233/256, 110/256, 236/256], # #e96eec
# [0.6, 0.6,... | 2,205 | 28.413333 | 83 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_b/generate.py | import numpy as np
import math
import torch
import timeit
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(10)
class ControlNet(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(ControlNet, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.... | 2,768 | 30.827586 | 96 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_b/u_plot.py | import matplotlib.pyplot as plt
import torch
import numpy as np
from matplotlib import cm
import matplotlib as mpl
class ControlNet(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(ControlNet, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_in... | 1,389 | 27.367347 | 81 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_b/calculate.py | import matplotlib.pyplot as plt
import torch
import numpy as np
# import pylustrator
# pylustrator.start()
def plot_grid():
plt.grid(b=True, which='major', color='gray', alpha=0.5, linestyle='dashdot', lw=1.5)
# minor grid lines
plt.minorticks_on()
plt.grid(b=True, which='minor', color='beige', alpha=0... | 1,872 | 43.595238 | 243 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_b/ES_Quadratic.py | import sys
sys.path.append('./neural_sde')
import torch
import torch.nn.functional as F
import numpy as np
import timeit
from hessian import hessian
from hessian import jacobian
# from gradient import hessian
# from gradient import jacobian
class ControlNet(torch.nn.Module):
def __init__(self,n_input,n_hidd... | 4,311 | 28.737931 | 142 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/hyper_b/plot.py | import numpy as np
import matplotlib.pyplot as plt
from V_plot import *
from u_plot import *
from plot_trajectory import *
# import matplotlib
# matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
# matplotlib.rcParams['text.usetex'] = True
font_size = 15
A = torch.load('./data/hyper_b/data.pt')[:,9:14,... | 2,394 | 26.848837 | 106 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/plot_loss.py | import numpy as np
import matplotlib.pyplot as plt
import torch
import pylustrator
pylustrator.start()
import seaborn as sns
sns.set_theme(style="whitegrid")
L1 = torch.load('./data/harmonic/loss_icnn.pt')[2:] # delete large first tow numbers
L2 = torch.load('./data/harmonic/loss_quad.pt')
L3 = torch.load('./data/har... | 3,889 | 45.86747 | 181 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/AS.py | import torch
import torch.nn.functional as F
import timeit
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = torch.nn.Linear(n_hidden,n_hidd... | 2,766 | 26.39604 | 143 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/generate.py | import numpy as np
import math
import torch
import numpy as np
import timeit
from AS import *
from Control_Nonlinear_Icnn import *
start = timeit.default_timer()
# Harmonic linear oscillator
model = Net(D_in,H1,D_out)
# Generate trajectory with nonlinaer AS control
def algo2(z,X,N,dt):
model = Net(D_in,H1,D_out... | 2,835 | 33.585366 | 113 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/ES_ICNN.py | import torch
import torch.nn.functional as F
import timeit
from hessian import hessian
from hessian import jacobian
from Control_Nonlinear_Icnn import *
# Drift function
def harmonic(x):
y = []
beta = 0.5
for i in range(0,len(x)):
f = [x[i,1],-x[i,0]-2*beta*x[i,1]]
y.append(f)
y = to... | 2,972 | 26.527778 | 142 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/ES_Quadratic.py | import torch
import torch.nn.functional as F
import timeit
from hessian import hessian
from hessian import jacobian
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hid... | 3,376 | 26.016 | 142 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/plot.py | import numpy as np
import matplotlib.pyplot as plt
import torch
import matplotlib
matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
matplotlib.rcParams['text.usetex'] = True
import sys
sys.path.append('./data/harmonic')
'''
Data is dictionary {'X'，'Y'，'Z'，'W'}，corresponds to 20 sample trajectories unde... | 11,933 | 39.317568 | 114 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/table1.py | import numpy as np
import torch
data = torch.load('./data/harmonic/data_long.pt')
# Calculate the data in table1
def L2_norm(st,a):
Y = data[st][torch.tensor([0,1,3,4,5,6,7,8,9,10,11,13,14,15,16,17,18,19]),:,:]
Y = Y.detach().numpy()
X = np.linalg.norm(Y,axis=2)
Z = np.mean(X,0)
index = np.where(Z... | 513 | 24.7 | 83 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/Neural Stochastic Control/harmonic/Control_Nonlinear_Icnn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class ICNN(nn.Module):
def __init__(self, input_shape, layer_sizes, activation_fn):
super(ICNN, self).__init__()
self._input_shape = input_shape
self._layer_sizes = layer_sizes
self._activation_fn = activation_fn
... | 3,754 | 34.424528 | 122 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/model_free/functions.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
import argparse
import matplotlib.pyplot as plt
colors = [
[233/256, 110/256, 236/256], # #e96eec
# [0.6, 0.6, 0.2], # olive
# [0.5333333333333333, 0.13333333333333333, 0.3333333333333333], # wine
[255/255, 165/255, 0],
... | 2,097 | 32.301587 | 89 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/model_free/run.py | import numpy as np
from scipy import integrate
import torch
import matplotlib.pyplot as plt
import math
import timeit
from scipy.integrate import odeint
from functions import *
def f(x,u=0):
a, b, c = 1, 1, 1
U2 = np.array([0.5, 0.74645887, 1.05370735, 0.38154169, 1.68833014, 0.83746371])
x1, x2, x3, x4, ... | 4,155 | 29.335766 | 127 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/model_free/NODE.py | # import sys
# sys.path.append('./neural_sde/NODE')
import argparse
import time
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
parser = argparse.ArgumentParser('ODE demo')
parser.add_argument('--method', type=str, choices=['dopri5', 'adams'], default='dopri5')
parser.add_argument('... | 4,670 | 31.213793 | 118 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/model_free/NSC_train.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
import argparse
parser = argparse.ArgumentParser('ODE demo')
parser.add_argument('--N', type=float, default=1000)
parser.add_argument('--num', type=float, default=6)
parser.add_argument('--lr', type=float, default=0.05)
args = parser.parse_a... | 4,004 | 31.04 | 153 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/multiple_k/AS.py | import torch
import torch.nn.functional as F
import timeit
import math
class Net(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(Net, self).__init__()
torch.manual_seed(2)
self.layer1 = torch.nn.Linear(n_input, n_hidden)
self.layer2 = torch.nn.Linear(n_h... | 1,716 | 21.012821 | 72 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/multiple_k/functions.py | import numpy as np
import math
import torch
import timeit
from scipy import integrate
import matplotlib.pyplot as plt
start = timeit.default_timer()
np.random.seed(1)
class Net(torch.nn.Module):
def __init__(self, n_input, n_hidden, n_output):
super(Net, self).__init__()
torch.manual_seed(2)
... | 6,281 | 26.432314 | 129 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/multiple_k/plot_appendix.py | import numpy as np
import matplotlib.pyplot as plt
import torch
# import matplotlib
# matplotlib.rcParams['font.sans-serif'] = 'NSimSun,Times New Roman'
# matplotlib.rcParams['text.usetex'] = True
def plot_grid():
plt.grid(b=True, which='major', color='gray', alpha=0.6, linestyle='dashdot', lw=1.5)
# minor gr... | 2,306 | 30.60274 | 102 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/mixed_control/functions.py | import numpy as np
from scipy import integrate
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import math
import timeit
from scipy.integrate import odeint
colors = [
[233/256, 110/256, 236/256], # #e96eec
# [0.6, 0.6, 0.2], # olive
# [0.5333333333333333, 0.13333333333333333, 0.3333333... | 2,307 | 31.507042 | 89 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/mixed_control/run.py | import numpy as np
from scipy import integrate
import torch
import matplotlib.pyplot as plt
import math
import timeit
from scipy.integrate import odeint
from functions import *
from cvxopt import solvers,matrix
def f(x,u=0):
u,v = x
G = 9.81 # gravity
L = 0.5 # length of the pole
m = 0.15 # ball m... | 4,956 | 29.598765 | 127 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/mixed_control/NSC_train.py | import torch
import torch.nn.functional as F
import numpy as np
import timeit
import argparse
parser = argparse.ArgumentParser('ODE demo')
parser.add_argument('--N', type=float, default=1000)
parser.add_argument('--num', type=float, default=2)
parser.add_argument('--lr', type=float, default=0.05)
args = parser.parse_a... | 3,379 | 28.137931 | 153 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/comparison/lqr.py | import numpy as np
from cvxopt import solvers,matrix
import matplotlib.pyplot as plt
import torch
def harmonic(n,dt):
x0 = np.array([2.0,2.0])
X = np.zeros([n,2])
X[0,:]=x0
z = np.random.normal(0, 1, n)
for i in range(n-1):
x1,x2 = X[i,:]
X[i+1,0] = x1 + (x2-4.45*x1-0.09*x2)*dt
... | 662 | 21.1 | 83 | py |
Neural-Stochastic-Control | Neural-Stochastic-Control-main/code_rebuttal/comparison/run.py | import numpy as np
from cvxopt import solvers,matrix
import matplotlib.pyplot as plt
import torch
import seaborn as sns
class ControlNet(torch.nn.Module):
def __init__(self,n_input,n_hidden,n_output):
super(ControlNet,self).__init__()
torch.manual_seed(2)
self.layer1=torch.nn.Linear(n_inp... | 10,101 | 40.572016 | 116 | py |
MixLacune | MixLacune-main/process-lacunes.py | # -*- coding: utf-8 -*-
import os
import torch
import torchvision
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import SimpleITK as sitk
import glob
import torch.nn as nn
import nibabel as nib
import shutil
device = torch.device('cuda' if torch.cud... | 21,262 | 36.173077 | 155 | py |
SimCSE | SimCSE-main/setup.py | import io
from setuptools import setup, find_packages
with io.open('./README.md', encoding='utf-8') as f:
readme = f.read()
setup(
name='simcse',
packages=['simcse'],
version='0.4',
license='MIT',
description='A sentence embedding tool based on SimCSE',
author='Tianyu Gao, Xingcheng Yao, D... | 767 | 26.428571 | 88 | py |
SimCSE | SimCSE-main/evaluation.py | import sys
import io, os
import numpy as np
import logging
import argparse
from prettytable import PrettyTable
import torch
import transformers
from transformers import AutoModel, AutoTokenizer
# Set up logger
logging.basicConfig(format='%(asctime)s : %(message)s', level=logging.DEBUG)
# Set PATHs
PATH_TO_SENTEVAL = ... | 8,127 | 38.456311 | 165 | py |
SimCSE | SimCSE-main/simcse_to_huggingface.py | """
Convert SimCSE's checkpoints to Huggingface style.
"""
import argparse
import torch
import os
import json
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--path", type=str, help="Path of SimCSE checkpoint folder")
args = parser.parse_args()
print("SimCSE checkpoint -> Hugging... | 1,327 | 29.181818 | 107 | py |
SimCSE | SimCSE-main/train.py | import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional, Union, List, Dict, Tuple
import torch
import collections
import random
from datasets import load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPP... | 24,040 | 39.955707 | 144 | py |
SimCSE | SimCSE-main/simcse/tool.py | import logging
from tqdm import tqdm
import numpy as np
from numpy import ndarray
import torch
from torch import Tensor, device
import transformers
from transformers import AutoModel, AutoTokenizer
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.preprocessing import normalize
from typing import List... | 12,092 | 41.135889 | 160 | py |
SimCSE | SimCSE-main/simcse/trainers.py | import collections
import inspect
import math
import sys
import os
import re
import json
import shutil
import time
import warnings
from pathlib import Path
import importlib.util
from packaging import version
from transformers import Trainer
from transformers.modeling_utils import PreTrainedModel
from transformers.train... | 25,360 | 44.368515 | 149 | py |
SimCSE | SimCSE-main/simcse/models.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist
import transformers
from transformers import RobertaTokenizer
from transformers.models.roberta.modeling_roberta import RobertaPreTrainedModel, RobertaModel, RobertaLMHead
from transformers.models.bert.modeling_bert impo... | 13,807 | 34.405128 | 161 | py |
SimCSE | SimCSE-main/demo/gradiodemo.py | import torch
from scipy.spatial.distance import cosine
from transformers import AutoModel, AutoTokenizer
import gradio as gr
# Import our models. The package will take care of downloading the models automatically
tokenizer = AutoTokenizer.from_pretrained("princeton-nlp/sup-simcse-bert-base-uncased")
model = AutoModel.... | 2,105 | 40.294118 | 219 | py |
SimCSE | SimCSE-main/demo/flaskdemo.py | import json
import argparse
import torch
import os
import random
import numpy as np
import requests
import logging
import math
import copy
import string
from tqdm import tqdm
from time import time
from flask import Flask, request, jsonify
from flask_cors import CORS
from tornado.wsgi import WSGIContainer
from tornado.... | 2,839 | 32.809524 | 113 | py |
SimCSE | SimCSE-main/SentEval/examples/infersent.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
"""
InferSent models. See https://github.com/facebookresearch/InferSent.
"""
from __future__ import absolute_import, division,... | 2,463 | 31 | 92 | py |
SimCSE | SimCSE-main/SentEval/examples/bow.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from __future__ import absolute_import, division, unicode_literals
import sys
import io
import numpy as np
import logging
# ... | 3,423 | 29.300885 | 82 | py |
SimCSE | SimCSE-main/SentEval/examples/googleuse.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from __future__ import absolute_import, division
import os
import sys
import logging
import tensorflow as tf
import tensorflow... | 2,205 | 31.441176 | 86 | py |
SimCSE | SimCSE-main/SentEval/examples/models.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
"""
This file contains the definition of encoders used in https://arxiv.org/pdf/1705.02364.pdf
"""
import numpy as np
import t... | 9,875 | 36.12782 | 94 | py |
SimCSE | SimCSE-main/SentEval/examples/gensen.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
"""
Clone GenSen repo here: https://github.com/Maluuba/gensen.git
And follow instructions for loading the model used in batcher... | 2,429 | 31.4 | 82 | py |
SimCSE | SimCSE-main/SentEval/examples/skipthought.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from __future__ import absolute_import, division, unicode_literals
"""
Example of file for SkipThought in SentEval
"""
import ... | 2,048 | 32.048387 | 97 | py |
SimCSE | SimCSE-main/SentEval/senteval/engine.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
'''
Generic sentence evaluation scripts wrapper
'''
from __future__ import absolute_import, division, unicode_literals
from ... | 6,525 | 49.2 | 139 | py |
SimCSE | SimCSE-main/SentEval/senteval/rank.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
'''
Image-Caption Retrieval with COCO dataset
'''
from __future__ import absolute_import, division, unicode_literals
import os... | 4,643 | 41.605505 | 129 | py |
SimCSE | SimCSE-main/SentEval/senteval/snli.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
'''
SNLI - Entailment
'''
from __future__ import absolute_import, division, unicode_literals
import codecs
import os
import io... | 4,577 | 39.157895 | 75 | py |
SimCSE | SimCSE-main/SentEval/senteval/utils.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
from __future__ import absolute_import, division, unicode_literals
import numpy as np
import re
import inspect
from torch impo... | 2,713 | 27.270833 | 79 | py |
SimCSE | SimCSE-main/SentEval/senteval/binary.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
'''
Binary classifier and corresponding datasets : MR, CR, SUBJ, MPQA
'''
from __future__ import absolute_import, division, uni... | 3,712 | 38.924731 | 79 | py |
SimCSE | SimCSE-main/SentEval/senteval/mrpc.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
'''
MRPC : Microsoft Research Paraphrase (detection) Corpus
'''
from __future__ import absolute_import, division, unicode_liter... | 4,202 | 39.028571 | 80 | py |
SimCSE | SimCSE-main/SentEval/senteval/probing.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
'''
probing tasks
'''
from __future__ import absolute_import, division, unicode_literals
import os
import io
import copy
impo... | 6,786 | 38.459302 | 120 | py |
SimCSE | SimCSE-main/SentEval/senteval/sick.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
'''
SICK Relatedness and Entailment
'''
from __future__ import absolute_import, division, unicode_literals
import os
import io... | 9,243 | 41.599078 | 80 | py |
SimCSE | SimCSE-main/SentEval/senteval/trec.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
'''
TREC question-type classification
'''
from __future__ import absolute_import, division, unicode_literals
import os
import... | 3,565 | 38.622222 | 79 | py |
SimCSE | SimCSE-main/SentEval/senteval/sst.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
'''
SST - binary classification
'''
from __future__ import absolute_import, division, unicode_literals
import os
import io
im... | 3,946 | 39.690722 | 94 | py |
SimCSE | SimCSE-main/SentEval/senteval/tools/relatedness.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
"""
Semantic Relatedness (supervised) with Pytorch
"""
from __future__ import absolute_import, division, unicode_literals
impo... | 4,552 | 32.725926 | 100 | py |
SimCSE | SimCSE-main/SentEval/senteval/tools/validation.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
"""
Validation and classification
(train) : inner-kfold classifier
(train, test) : kfold classifier
(train, d... | 10,358 | 40.939271 | 93 | py |
SimCSE | SimCSE-main/SentEval/senteval/tools/classifier.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
"""
Pytorch Classifier class in the style of scikit-learn
Classifiers include Logistic Regression and MLP
"""
from __future__ ... | 7,737 | 37.118227 | 94 | py |
SimCSE | SimCSE-main/SentEval/senteval/tools/ranking.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
"""
Image Annotation/Search for COCO with Pytorch
"""
from __future__ import absolute_import, division, unicode_literals
impor... | 15,275 | 41.433333 | 109 | py |
pytorch_conv4D | pytorch_conv4D-master/conv4d.py | import numpy as np
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv4d_broadcast(nn.Module):
def __init__(self, in_channels,
out_channels,
kernel_size,
padding,
stride=1,
padding_mode='circ... | 9,176 | 40.337838 | 116 | py |
pytorch_conv4D | pytorch_conv4D-master/test_conv4d.py | import pytest
import timeit
import numpy as np
import scipy.stats as sns
from functools import partial
import torch
import torch.nn as nn
from .conv4d import Conv4d_broadcast, Conv4d_groups
try:
import intel_extension_for_pytorch as ipex
device = torch.device("xpu" if torch.xpu.is_available() else "cpu")
... | 7,179 | 35.262626 | 106 | py |
phocnet | phocnet-master/install.py | import os
import shutil
import logging
import argparse
from subprocess import call
import sys
def main(cudnn_dir, no_caffe, opencv_dir, install_dir, install_caffe_dir):
# init logger
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger('install.py')
# init submodules
call(['git',... | 2,659 | 41.222222 | 174 | py |
phocnet | phocnet-master/tools/predict_phocs.py | #!/usr/bin/env python
'''
Script for predicting PHOCs for a number of images residing in a folder on disk.
'''
import argparse
import logging
import os
import caffe
import numpy as np
import cv2
from phocnet.evaluation.cnn import net_output_for_word_image_list
def main(img_dir, output_dir, pretrained_phocnet, deploy... | 2,673 | 42.836066 | 133 | py |
phocnet | phocnet-master/tools/save_deploy_proto.py | #!/usr/bin/env python
import argparse
import os
from phocnet.caffe.model_proto_generator import ModelProtoGenerator
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Save a PHOCNet deploy proto file to disk.')
parser.add_argument('--output_dir', '-od', action='store', type=str, default='... | 1,028 | 59.529412 | 118 | py |
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