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
value |
|---|---|---|---|---|---|---|
herculens | herculens-main/herculens/Util/util.py | # Utility functions
#
# Copyright (c) 2021, herculens developers and contributors
# Copyright (c) 2018, Simon Birrer & lenstronomy contributors
# based on the Util module from lenstronomy (version 1.9.3)
__author__ = 'sibirrer', 'austinpeel', 'aymgal'
import math
import numpy as np
import jax.numpy as jnp
import js... | 11,904 | 38.290429 | 119 | py |
herculens | herculens-main/herculens/Util/model_util.py | # Utility functions
#
# Copyright (c) 2021, herculens developers and contributors
__author__ = 'aymgal'
import copy
import numpy as np
import jax.numpy as jnp
from scipy.ndimage import morphology
from scipy import ndimage
def mask_from_source_area(lens_image, parameters):
src_idx = lens_image.SourceModel.pixe... | 6,506 | 46.152174 | 146 | py |
herculens | herculens-main/herculens/Util/image_util.py | # Utility functions
#
# Copyright (c) 2021, herculens developers and contributors
# Copyright (c) 2018, Simon Birrer & lenstronomy contributors
# based on the Util module from lenstronomy (version 1.9.3)
__author__ = 'sibirrer', 'dangilman', 'aymgal'
import numpy as np
from scipy import interpolate #, ndimage
from s... | 6,392 | 38.708075 | 148 | py |
herculens | herculens-main/herculens/Util/jax_util.py | # Classes and functions to use with JAX
#
# Copyright (c) 2021, herculens developers and contributors
__author__ = 'austinpeel', 'aymgal', 'duxfrederic'
from functools import partial
from copy import deepcopy
import numpy as np
import jax.numpy as jnp
from jax import jit, vmap, lax
from jax.scipy.special import gam... | 3,482 | 33.485149 | 100 | py |
herculens | herculens-main/herculens/Inference/sensitivity_mapping.py | # Gradient-based sensitivity mapping
#
# Copyright (c) 2021, herculens developers and contributors
__author__ = 'aymgal'
import copy
import time
import numpy as np
from skimage import feature
from functools import partial
from jax import grad
from jax import jit
import jax.numpy as jnp
from herculens.LightModel.l... | 11,006 | 43.028 | 128 | py |
herculens | herculens-main/herculens/Inference/loss.py | # Defines the full loss function, from likelihood, prior and regularization terms
#
# Copyright (c) 2021, herculens developers and contributors
__author__ = 'aymgal', 'austinpeel'
import numpy as np
import jax.numpy as jnp
from jax import jit
import warnings
from herculens.Inference.base_differentiable import Diff... | 1,274 | 30.875 | 81 | py |
herculens | herculens-main/herculens/Inference/base_differentiable.py | # Defines a general fully differentiable scalar function
#
# Copyright (c) 2022, herculens developers and contributors
__author__ = 'aymgal'
from functools import partial
from jax import jit, grad, jacfwd, jacrev, jvp, value_and_grad
__all__ = ['Differentiable']
class Differentiable(object):
"""Abstract cl... | 1,536 | 29.137255 | 162 | py |
herculens | herculens-main/herculens/Inference/Sampling/sampling.py | # Handles different method to sample the posterior distribution of parameters
#
# Copyright (c) 2021, herculens developers and contributors
__author__ = 'aymgal', 'austinpeel'
import time
import numpy as np
from functools import partial
import jax
from herculens.Inference.Sampling.base_inference import Inference
... | 9,177 | 43.125 | 132 | py |
herculens | herculens-main/herculens/Inference/Sampling/base_inference.py | # Defines a general fully differentiable probability function for inference purposes
#
# Copyright (c) 2022, herculens developers and contributors
__author__ = 'aymgal'
from functools import partial
from jax import jit
__all__ = ['Inference']
class Inference(object):
"""Abstract class that defines wraps th... | 1,116 | 30.914286 | 107 | py |
herculens | herculens-main/herculens/Inference/Optimization/base_optim.py | # Defines a general fully differentiable probability function for inference purposes
#
# Copyright (c) 2022, herculens developers and contributors
__author__ = 'aymgal'
from functools import partial
import jax.numpy as jnp
from jax import jit, grad, value_and_grad
from tqdm import tqdm
__all__ = ['BaseOptimizer']... | 1,582 | 28.867925 | 110 | py |
herculens | herculens-main/herculens/Inference/Optimization/jaxopt.py | # Handles different method to optimize a loss function
#
# Copyright (c) 2021, herculens developers and contributors
__author__ = 'aymgal', 'austinpeel'
import time
import warnings
import numpy as np
from copy import deepcopy
import jax
import jaxopt
from herculens.Inference.Optimization.base_optim import BaseOpti... | 5,014 | 36.148148 | 123 | py |
herculens | herculens-main/herculens/Inference/Optimization/optax.py | # Handles different method to optimize a loss function
#
# Copyright (c) 2021, herculens developers and contributors
__author__ = 'aymgal', 'austinpeel'
import time
import numpy as np
from copy import deepcopy
from functools import partial
from jax import jit
import optax
from herculens.Inference.Optimization.base... | 4,633 | 40.747748 | 134 | py |
herculens | herculens-main/herculens/Inference/ProbModel/numpyro_util.py | # Set of utilities that extends some of numpyro functionalities
# In the future, these may be incorporated within numpyro
import jax.numpy as jnp
from functools import partial
import numpyro
from numpyro import handlers
from numpyro.distributions import transforms, constraints
from numpyro.distributions.util import... | 4,837 | 38.983471 | 106 | py |
herculens | herculens-main/herculens/Inference/ProbModel/numpyro.py | # Defines the model of a strong lens
#
# Copyright (c) 2022, herculens developers and contributors
__author__ = 'aymgal'
import jax
import jax.numpy as jnp
import numpyro
from numpyro import handlers
from numpyro.infer import util
from herculens.Inference.ProbModel.base_model import BaseProbModel
from herculens.I... | 2,638 | 33.272727 | 109 | py |
herculens | herculens-main/herculens/Inference/legacy/optimization.py | # Handles different method to optimize a loss function
#
# Copyright (c) 2021, herculens developers and contributors
__author__ = 'aymgal', 'austinpeel'
import time
import warnings
import numpy as np
import jax
import optax
from scipy import optimize
from scipy.optimize import Bounds
from tqdm import tqdm
from copy... | 13,140 | 44.787456 | 134 | py |
herculens | herculens-main/herculens/Inference/legacy/loss.py | # Defines the full loss function, from likelihood, prior and regularization terms
#
# Copyright (c) 2021, herculens developers and contributors
__author__ = 'aymgal', 'austinpeel'
import numpy as np
import jax.numpy as jnp
from jax import jit
from herculens.Inference.legacy.base_differentiable import Differentiabl... | 24,921 | 54.629464 | 158 | py |
herculens | herculens-main/herculens/Inference/legacy/sampling.py | # Handles different method to sample the posterior distribution of parameters
#
# Copyright (c) 2021, herculens developers and contributors
__author__ = 'aymgal', 'austinpeel'
import time
import numpy as np
from functools import partial
import jax
from herculens.Inference.legacy.base_inference import Inference
#... | 9,368 | 42.985915 | 132 | py |
herculens | herculens-main/herculens/Inference/legacy/base_differentiable.py | # Defines a general fully differentiable scalar function
#
# Copyright (c) 2022, herculens developers and contributors
__author__ = 'aymgal'
from functools import partial
from jax import jit, grad, jacfwd, jacrev, jvp, value_and_grad
__all__ = ['Differentiable']
class Differentiable(object):
"""Abstract cl... | 1,474 | 29.729167 | 162 | py |
herculens | herculens-main/herculens/Inference/legacy/parameters.py | # Estimation of model parameters storage and manipulation
#
# Copyright (c) 2021, herculens developers and contributors
__author__ = 'aymgal'
from copy import deepcopy
import numpy as np
import jax.numpy as jnp
from jax import lax, jit
from functools import partial
from herculens.MassModel.Profiles import pixelate... | 31,937 | 48.135385 | 186 | py |
herculens | herculens-main/herculens/Inference/legacy/base_inference.py | # Defines a general fully differentiable probability function for inference purposes
#
# Copyright (c) 2022, herculens developers and contributors
__author__ = 'aymgal'
from functools import partial
from jax import jit
__all__ = ['Inference']
class Inference(object):
"""Abstract class that defines wraps th... | 1,163 | 31.333333 | 107 | py |
herculens | herculens-main/herculens/Instrument/noise.py | # Defines the data noise model
#
# Copyright (c) 2021, herculens developers and contributors
# Copyright (c) 2018, Simon Birrer & lenstronomy contributors
# based on the Data module from lenstronomy (version 1.9.3)
__author__ = 'sibirrer', 'austinpeel', 'aymgal'
import numpy as np
import jax.numpy as jnp
from jax i... | 6,709 | 39.421687 | 129 | py |
herculens | herculens-main/herculens/LensImage/lens_image.py | # Defines the model of a strong lens
#
# Copyright (c) 2023, herculens developers and contributors
# Copyright (c) 2018, Simon Birrer & lenstronomy contributors
# based on the ImSim module from lenstronomy (version 1.9.3)
__author__ = 'sibirrer', 'austinpeel', 'aymgal'
import copy
import numpy as np
import jax.numpy... | 15,267 | 54.722628 | 156 | py |
herculens | herculens-main/herculens/LensImage/lensing_operator.py | # Defines the model of a strong lens
#
# Copyright (c) 2023, herculens developers and contributors
# Copyright (c) 2020, Austin Peel and Aymeric Galan
# based on the LensingOperator class from slitronomy (https://github.com/aymgal/SLITronomy)
__author__ = 'austinpeel', 'aymgal'
import copy
import numpy as np
import... | 13,789 | 37.093923 | 113 | py |
herculens | herculens-main/herculens/LensImage/Numerics/numerics.py | # Handles coordinate grids and convolutions
#
# Copyright (c) 2021, herculens developers and contributors
# Copyright (c) 2018, Simon Birrer & lenstronomy contributors
# based on the ImSim.Numerics module from lenstronomy (version 1.9.3)
__author__ = 'sibirrer', 'austinpeel', 'aymgal'
import numpy as np
from hercul... | 6,653 | 44.575342 | 135 | py |
herculens | herculens-main/herculens/LensImage/Numerics/convolution.py | # Handles different convolution methods
#
# Copyright (c) 2021, herculens developers and contributors
# Copyright (c) 2018, Simon Birrer & lenstronomy contributors
# based on the ImSim.Numerics module from lenstronomy (version 1.9.3)
__author__ = 'sibirrer', 'austinpeel', 'aymgal'
import numpy as np
import jax.nump... | 7,248 | 37.558511 | 118 | py |
herculens | herculens-main/herculens/LensImage/Numerics/grid.py | # Handles coordinate grid on which ray-tracing and convolution are performed
#
# Copyright (c) 2021, herculens developers and contributors
# Copyright (c) 2018, Simon Birrer & lenstronomy contributors
# based on the ImSim.Numerics module from lenstronomy (version 1.9.3)
__author__ = 'sibirrer', 'austinpeel', 'aymgal'... | 4,533 | 39.123894 | 122 | py |
herculens | herculens-main/herculens/MassModel/mass_model.py | # High-level interface to a mass model
#
# Copyright (c) 2021, herculens developers and contributors
# Copyright (c) 2018, Simon Birrer & lenstronomy contributors
# based on the LensModel module from lenstronomy (version 1.9.3)
__author__ = 'sibirrer', 'austinpeel', 'aymgal'
import numpy as np
import jax.numpy as jn... | 8,131 | 37.358491 | 121 | py |
herculens | herculens-main/herculens/MassModel/Profiles/point_mass.py | # Defines a point mass
#
# Copyright (c) 2021, herculens developers and contributors
__author__ = 'aymgal'
import numpy as np
import jax.numpy as jnp
__all__ = ['PointMass']
class PointMass(object):
"""
class to compute the physical deflection angle of a point mass, given as an Einstein radius
"""
... | 2,139 | 27.533333 | 95 | py |
herculens | herculens-main/herculens/MassModel/Profiles/nie.py | # Defines a non-singular isothermal ellipsoid
#
# Copyright (c) 2021, herculens developers and contributors
# Copyright (c) 2018, Simon Birrer & lenstronomy contributors
# based on the LensModel.Profiles module from lenstronomy (version 1.9.3)
__author__ = 'sibirrer', 'austinpeel', 'aymgal'
import numpy as np
impor... | 7,891 | 34.710407 | 114 | py |
herculens | herculens-main/herculens/MassModel/Profiles/pixelated.py | # Defines a pixelated profile in the lens potential
#
# Copyright (c) 2021, herculens developers and contributors
__author__ = 'austinpeel', 'aymgal'
import numpy as np
import jax
import jax.numpy as jnp
from jax import grad, jacfwd, jacrev, vmap
# from functools import partial
from utax.interpolation import Bicub... | 9,422 | 38.426778 | 113 | py |
herculens | herculens-main/herculens/MassModel/Profiles/multipole.py | # Defines a multipole in the potential
#
# Copyright (c) 2021, herculens developers and contributors
# Copyright (c) 2018, Simon Birrer & lenstronomy contributors
# based on the LensModel.Profiles module from lenstronomy (version 1.9.3)
__author__ = 'sibirrer', 'lynevdv', 'austinpeel', 'aymgal'
import numpy as np
i... | 3,589 | 42.253012 | 117 | py |
herculens | herculens-main/herculens/MassModel/Profiles/gaussian_potential.py | # Defines a gaussian profile
#
# Copyright (c) 2021, herculens developers and contributors
# Copyright (c) 2018, Simon Birrer & lenstronomy contributors
# based on the LensModel.Profiles module from lenstronomy (version 1.9.3)
__author__ = 'sibirrer', 'austinpeel', 'aymgal'
import numpy as np
import jax.numpy as jn... | 1,971 | 36.923077 | 114 | py |
herculens | herculens-main/herculens/MassModel/Profiles/epl.py | # Defines en elliptical power law profile
#
# Copyright (c) 2021, herculens developers and contributors
# Copyright (c) 2018, Simon Birrer & lenstronomy contributors
# based on the LensModel.Profiles module from lenstronomy (version 1.9.3)
__author__ = 'sibirrer', 'dangilman', 'ntessore', 'austinpeel', 'aymgal'
imp... | 7,348 | 32.404545 | 112 | py |
herculens | herculens-main/herculens/MassModel/Profiles/sersic_utils.py | # Utility methods for Sersic profiles
#
# Copyright (c) 2021, herculens developers and contributors
# Copyright (c) 2018, Simon Birrer & lenstronomy contributors
# based on the LensModel.Profiles module from lenstronomy (version 1.9.3)
__author__ = 'sibirrer', 'jiwoncpark', 'austinpeel', 'aymgal'
import scipy.speci... | 7,028 | 32.15566 | 150 | py |
herculens | herculens-main/herculens/Analysis/plot.py | # Class to plot a lens model
#
# Copyright (c) 2021, herculens developers and contributors
__author__ = 'aymgal'
import copy
import warnings
import numpy as np
import jax.numpy as jnp
import matplotlib.pyplot as plt
from scipy import ndimage
from matplotlib.colors import Normalize, LogNorm, TwoSlopeNorm
from hercu... | 20,805 | 46.940092 | 130 | py |
herculens | herculens-main/herculens/RegulModel/regul_util.py | # Utility functions for regularization methods
#
# Copyright (c) 2021, herculens developers and contributors
__author__ = 'aymgal'
import copy
import numpy as np
import time
from scipy import signal
from scipy.interpolate import griddata
import warnings
import jax
import jax.numpy as jnp
from jax import lax, jit, ... | 14,258 | 42.340426 | 118 | py |
herculens | herculens-main/herculens/RegulModel/Methods/sparsity.py | # Defines regularization choices
#
# Copyright (c) 2023, herculens developers and contributors
__author__ = 'aymgal'
import jax.numpy as jnp
from herculens.RegulModel.Methods.base import BaseRegularization
from herculens.RegulModel import regul_util
__all__ = [
'SparsityStarlet',
'SparsityBLWavelet',
]
... | 3,871 | 36.960784 | 110 | py |
herculens | herculens-main/herculens/RegulModel/Methods/constraints.py | # Defines regularization choices
#
# Copyright (c) 2023, herculens developers and contributors
__author__ = 'aymgal'
import jax.numpy as jnp
from herculens.RegulModel.Methods.base import BaseRegularization
__all__ = [
'Positivity',
'Negativity',
]
class Positivity(BaseRegularization):
param_names... | 1,600 | 24.822581 | 73 | py |
herculens | herculens-main/herculens/Standard/coolest_util.py | # Handles coordinate systems
#
# Copyright (c) 2022, herculens developers and contributors
__author__ = 'aymgal'
import os
import shutil
import math
import numpy as np
from astropy.io import fits
import logging
from herculens.Inference.legacy.parameters import Parameters as HerculensParameters
from herculens.Util ... | 26,903 | 42.533981 | 126 | py |
GFCS | GFCS-main/plot_results.py | # (LB plotting/stat code)
import configargparse
from pathlib import Path
from os import listdir
from os.path import isdir, isfile, join, splitext
import torch
import matplotlib.pyplot as plt
import json
from ours_util import stats_summary, bootstrap_sampling, MAX_QUERIES
def process_result_file(result, args):
in... | 4,796 | 36.771654 | 119 | py |
GFCS | GFCS-main/generate_results_summary.py | # (LB plotting/stat code)
# This consumes the results output by plot_results so that multiple PDF/CDFs can be plotted together.
import configargparse
from pathlib import Path
import json
import matplotlib
import matplotlib.pyplot as plt
from cycler import cycler
import torch
plt.rcParams.update({
"text.usetex": T... | 7,651 | 38.854167 | 120 | py |
GFCS | GFCS-main/gfcs_util.py | import torch
import torchvision.transforms as transforms
from random import randint
imagenet_mean = [0.485, 0.456, 0.406]
imagenet_std = [0.229, 0.224, 0.225]
class Interpolate(torch.nn.Module):
def __init__(self, size, mode):
super(Interpolate, self).__init__()
self.interp = torch.nn.functional... | 2,032 | 29.343284 | 109 | py |
GFCS | GFCS-main/rgf_variants_pytorch.py | # This is a PyTorch port of the main P-RGF attack file originally released and documented in this repo:
# https://github.com/thu-ml/Prior-Guided-RGF. This implements the method described in "Improving Black-box
# Adversarial Attacks with a Transfer-based Prior" (https://arxiv.org/abs/1906.06919). Note that this por... | 27,796 | 54.043564 | 120 | py |
GFCS | GFCS-main/GFCS_main.py |
# This implements the GFCS method of the paper "Attacking deep networks with surrogate-based adversarial black-box
# methods is easy" (https://arxiv.org/abs/2203.08725).
# The code is a heavily adapted version of the implementation of SimBA-ODS (https://github.com/ermongroup/ODS) from the
# paper "Diversity can be... | 14,015 | 49.057143 | 120 | py |
sgnmt | sgnmt-master/cam/sgnmt/ui.py | # -*- coding: utf-8 -*-
# coding=utf-8
# Copyright 2019 The SGNMT Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | 86,353 | 61.12518 | 88 | py |
sgnmt | sgnmt-master/cam/sgnmt/decode_utils.py | # -*- coding: utf-8 -*-
# coding=utf-8
# Copyright 2019 The SGNMT Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | 47,661 | 48.544699 | 84 | py |
sgnmt | sgnmt-master/cam/sgnmt/predictors/pytorch_fairseq.py | # -*- coding: utf-8 -*-
# coding=utf-8
# Copyright 2019 The SGNMT Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | 5,450 | 34.396104 | 75 | py |
BayesProcess | BayesProcess-master/JV_surrogate.py | """
JV surrogate model
filneame: JV_surrogate.py version: 1.0
Surrogate model for denoising experimental JV curves and predicting JV curves from materail descriptors
@authors: Danny Zekun Ren and Felipe Oviedo
MIT Photovoltaics Laboratory / Singapore and MIT Alliance for Research and Tehcnology
All code is under ... | 6,508 | 31.708543 | 110 | py |
BayesProcess | BayesProcess-master/Bayes.py | """
Baesian network
filneame: Bayes.py version: 1.0
Two step Bayesian inference(Bayesian network) to map process conditions to materaal properties
@authors: Danny Zekun Ren and Felipe Oviedo
MIT Photovoltaics Laboratory / Singapore and MIT Alliance for Research and Tehcnology
All code is under Apache 2.0 license, ... | 6,270 | 27.766055 | 94 | py |
pytorch-nested-unet | pytorch-nested-unet-master/losses.py | import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from LovaszSoftmax.pytorch.lovasz_losses import lovasz_hinge
except ImportError:
pass
__all__ = ['BCEDiceLoss', 'LovaszHingeLoss']
class BCEDiceLoss(nn.Module):
def __init__(self):
super().__init__()
def forward(self, i... | 1,036 | 24.925 | 92 | py |
pytorch-nested-unet | pytorch-nested-unet-master/val.py | import argparse
import os
from glob import glob
import cv2
import torch
import torch.backends.cudnn as cudnn
import yaml
from albumentations.augmentations import transforms
from albumentations.core.composition import Compose
from sklearn.model_selection import train_test_split
from tqdm import tqdm
import archs
from ... | 3,331 | 28.22807 | 108 | py |
pytorch-nested-unet | pytorch-nested-unet-master/archs.py | import torch
from torch import nn
__all__ = ['UNet', 'NestedUNet']
class VGGBlock(nn.Module):
def __init__(self, in_channels, middle_channels, out_channels):
super().__init__()
self.relu = nn.ReLU(inplace=True)
self.conv1 = nn.Conv2d(in_channels, middle_channels, 3, padding=1)
sel... | 5,702 | 40.627737 | 88 | py |
pytorch-nested-unet | pytorch-nested-unet-master/dataset.py | import os
import cv2
import numpy as np
import torch
import torch.utils.data
class Dataset(torch.utils.data.Dataset):
def __init__(self, img_ids, img_dir, mask_dir, img_ext, mask_ext, num_classes, transform=None):
"""
Args:
img_ids (list): Image ids.
img_dir: Image file di... | 2,361 | 29.675325 | 99 | py |
pytorch-nested-unet | pytorch-nested-unet-master/metrics.py | import numpy as np
import torch
import torch.nn.functional as F
def iou_score(output, target):
smooth = 1e-5
if torch.is_tensor(output):
output = torch.sigmoid(output).data.cpu().numpy()
if torch.is_tensor(target):
target = target.data.cpu().numpy()
output_ = output > 0.5
target_ ... | 771 | 24.733333 | 62 | py |
pytorch-nested-unet | pytorch-nested-unet-master/train.py | import argparse
import os
from collections import OrderedDict
from glob import glob
import pandas as pd
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.optim as optim
import yaml
from albumentations.augmentations import transforms
from albumentations.core.composition import Compose... | 12,595 | 34.48169 | 140 | py |
AGSR-Net | AGSR-Net-master/preprocessing.py | import torch
import numpy as np
import os
import scipy.io
path = 'drive/My Drive/BRAIN_DATASET'
roi_str = 'ROI_FC.mat'
def pad_HR_adj(label, split):
label = np.pad(label, ((split, split), (split, split)), mode="constant")
np.fill_diagonal(label, 1)
return label
def normalize_adj_torch(mx):
rowsum ... | 2,459 | 26.954545 | 76 | py |
AGSR-Net | AGSR-Net-master/model.py | import torch
import torch.nn as nn
from layers import *
from ops import *
from preprocessing import normalize_adj_torch
import torch.nn.functional as F
class AGSRNet(nn.Module):
def __init__(self, ks, args):
super(AGSRNet, self).__init__()
self.lr_dim = args.lr_dim
self.hr_dim = args.hr_... | 2,730 | 30.034091 | 79 | py |
AGSR-Net | AGSR-Net-master/layers.py | import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
import matplotlib.pyplot as plt
from initializations import *
from preprocessing import normalize_adj_torch
class GSRLayer(nn.Module):
def __init__(self, hr_dim):
super(GSRLayer, self).__init__()
self.weights =... | 2,087 | 29.26087 | 78 | py |
AGSR-Net | AGSR-Net-master/ops.py | import torch
import torch.nn as nn
import numpy as np
class GraphUnpool(nn.Module):
def __init__(self):
super(GraphUnpool, self).__init__()
def forward(self, A, X, idx):
new_X = torch.zeros([A.shape[0], X.shape[1]])
new_X[idx] = X
return A, new_X
class GraphPool(nn.Module):... | 2,688 | 25.623762 | 63 | py |
AGSR-Net | AGSR-Net-master/train.py | import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from preprocessing import *
from model import *
import torch.optim as optim
criterion = nn.MSELoss()
def train(model, subjects_adj, subjects_labels, args):
bce_loss = nn.BCELoss()
netD = Discriminator(args)
print(netD)... | 4,608 | 35.872 | 88 | py |
Q16 | Q16-main/main/clip_classifier/classify/utils.py | import torch
import clip
import pickle
class ClipWrapper(torch.nn.Module):
def __init__(self, device, model_name='ViT-B/16'):
super(ClipWrapper, self).__init__()
self.clip_model, self.preprocess = clip.load(model_name, device, jit=False)
self.clip_model.eval()
def forward(self, x):
... | 1,368 | 32.390244 | 86 | py |
Q16 | Q16-main/main/models/clip.py | import torch
import clip
class ClipVisionModel(torch.nn.Module):
def __init__(self, language_model, num_classes, device, fine_tune=False):
super(ClipVisionModel, self).__init__()
self.MMM, self.preprocess = clip.load(language_model.split('_')[1], device, jit=False)
self.MMM.to(device)
... | 4,122 | 40.23 | 114 | py |
Q16 | Q16-main/main/models/baseline.py | from __future__ import print_function
from __future__ import division
import torch
import torch.nn as nn
import torchvision
from torchvision import datasets, models, transforms
import clip
print("PyTorch Version: ", torch.__version__)
print("Torchvision Version: ", torchvision.__version__)
resnet_transforms = {
'... | 2,501 | 34.742857 | 108 | py |
Q16 | Q16-main/main/wordclouds/utils.py | import os
import matplotlib.pyplot as plt
from wordcloud import WordCloud
import re
import copy
import multidict as multidict
import scipy.io
import pickle
import pandas as pd
import numpy as np
from tqdm import tqdm
import torch
import PIL
import glob
from numpy import genfromtxt
import multiprocessing
import itertool... | 21,974 | 39.769944 | 119 | py |
Q16 | Q16-main/main/paper_experiments/experiments.py | import argparse
import torch
from PIL import Image
from matplotlib.colors import LinearSegmentedColormap
from torchvision.transforms import Normalize
import os
from tqdm import tqdm
import glob
import pickle
from main.models.clip import ClipSimModel_Infer
parser = argparse.ArgumentParser()
parser.add_argument('-g', '-... | 5,380 | 41.706349 | 120 | py |
Q16 | Q16-main/main/paper_experiments/smid_utils.py | import torch
import matplotlib.pyplot as plt
import seaborn as sns
import torchmetrics
sns.set_style("whitegrid")
def accuracy(y_pred, y_gt, cm):
y_pred_tag = torch.argmax(y_pred, dim=-1)
for i in range(len(cm)):
for j in range(len(cm)):
cm[i][j] += (y_pred_tag[y_gt == i] == j).sum().int(... | 1,290 | 25.895833 | 74 | py |
Q16 | Q16-main/main/paper_experiments/check_datasets/eval_openimages_train.py | from main.paper_experiments.experiments import torch, parser
from main.paper_experiments.experiments import run_model_imagefolder
torch.set_num_threads(6)
parser.add_argument('--dir_name', type=str, default='/workspace/datasets/openimagesv6/train')
args = parser.parse_args()
dir_name = args.dir_name
save_dir = 'openi... | 400 | 32.416667 | 93 | py |
Q16 | Q16-main/main/paper_experiments/check_datasets/eval_imagenet.py | from main.paper_experiments.experiments import torch, parser
from main.paper_experiments.experiments import run_model_imagefolder
torch.set_num_threads(6)
parser.add_argument('--dir_name', type=str, default='/workspace/datasets/imagenet1k/train')
args = parser.parse_args()
dir_name = args.dir_name
save_dir = 'imagen... | 381 | 28.384615 | 91 | py |
Q16 | Q16-main/main/paper_experiments/check_datasets/eval_openimages_test.py | from main.paper_experiments.experiments import torch, parser
from main.paper_experiments.experiments import run_model_imagefolder
torch.set_num_threads(6)
parser.add_argument('--dir_name', type=str, default='/workspace/datasets/openimagesv6/test')
args = parser.parse_args()
dir_name = args.dir_name
save_dir = 'openi... | 399 | 29.769231 | 92 | py |
Q16 | Q16-main/main/paper_experiments/check_datasets/eval_imagenet_val.py | from main.paper_experiments.experiments import torch, parser
from main.paper_experiments.experiments import run_model_imagefolder
torch.set_num_threads(6)
parser.add_argument('--dir_name', type=str, default='/workspace/datasets/imagenet1k/val')
args = parser.parse_args()
dir_name = args.dir_name
save_dir = 'imagenet1... | 376 | 30.416667 | 89 | py |
Q16 | Q16-main/main/paper_experiments/check_datasets/eval_openimages_val.py | from main.paper_experiments.experiments import torch, parser
from main.paper_experiments.experiments import run_model_imagefolder
torch.set_num_threads(6)
parser.add_argument('--dir_name', type=str, default='/workspace/datasets/openimagesv6/validation')
args = parser.parse_args()
dir_name = args.dir_name
save_dir = '... | 403 | 32.666667 | 98 | py |
nerfuser | nerfuser-main/nerfuser/view_blender.py | import shutil
from collections import defaultdict
from types import MethodType
import imageio
import numpy as np
import torch
from nerfstudio.cameras.cameras import Cameras
from nerfstudio.data.scene_box import SceneBox
from nerfstudio.models.nerfacto import NerfactoModelConfig
from tqdm import trange
from nerfuser.c... | 11,896 | 50.502165 | 182 | py |
nerfuser | nerfuser-main/nerfuser/components.py | import torch
from jaxtyping import Float
from nerfstudio.cameras.rays import RayBundle
from nerfstudio.field_components.field_heads import FieldHeadNames
from nerfstudio.model_components.renderers import RGBRenderer
from torch import Tensor
def get_nerfacto_outputs(self, ray_bundle: RayBundle):
ray_samples = self... | 2,785 | 39.970588 | 157 | py |
nerfuser | nerfuser-main/nerfuser/registration.py | import json
import re
import shutil
from collections import defaultdict
from dataclasses import dataclass, field
from datetime import datetime
from itertools import cycle
from pathlib import Path
from time import time
from typing import Literal, Optional, Union
import matplotlib.pyplot as plt
import numpy as np
import... | 14,861 | 50.248276 | 309 | py |
nerfuser | nerfuser-main/nerfuser/view_renderer.py | import torch
from nerfuser.view_blender import ViewBlender
class ViewRenderer(ViewBlender):
def __init__(self, model_method, model_name, load_dir, transform=None, load_step=None, chunk_size=None, device='cuda') -> None:
if transform is None:
transform = torch.eye(4)
super().__init__(m... | 651 | 45.571429 | 148 | py |
nerfuser | nerfuser-main/nerfuser/blending.py | import json
from collections import defaultdict
from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path
from time import time
from typing import Literal, Optional, Union
import cv2
import imageio
import numpy as np
import torch
import tyro
from torchmetrics import PeakSignalNois... | 11,822 | 52.017937 | 331 | py |
nerfuser | nerfuser-main/nerfuser/utils/utils.py | import json
import numpy as np
import torch
from nerfstudio.process_data.colmap_utils import qvec2rotmat
from scipy.spatial.transform import Rotation
def ch_pose_spec(T, src, tgt, pose_type='c2w'):
""" pose_spec:
0: x->right, y->front, z->up
1: x->right, y->down, z->front
2: x... | 4,947 | 33.124138 | 146 | py |
DALL-E | DALL-E-master/dall_e/utils.py | import attr
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
logit_laplace_eps: float = 0.1
@attr.s(eq=False)
class Conv2d(nn.Module):
n_in: int = attr.ib(validator=lambda i, a, x: x >= 1)
n_out: int = attr.ib(validator=lambda i, a, x: x >= 1)
kw: int = attr.ib(validator=lambda i... | 1,840 | 29.683333 | 81 | py |
DALL-E | DALL-E-master/dall_e/encoder.py | import attr
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from functools import partial
from dall_e.utils import Conv2d
@attr.s(eq=False, repr=False)
class EncoderBlock(nn.Module):
n_in: int = attr.ib(validator=lambda i, a, x: x >= ... | 3,775 | 39.170213 | 117 | py |
DALL-E | DALL-E-master/dall_e/decoder.py | import attr
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from functools import partial
from dall_e.utils import Conv2d
@attr.s(eq=False, repr=False)
class DecoderBlock(nn.Module):
n_in: int = attr.ib(validator=lambda i, a, x: x >= ... | 3,936 | 40.442105 | 117 | py |
DALL-E | DALL-E-master/dall_e/__init__.py | import io, requests
import torch
import torch.nn as nn
from dall_e.encoder import Encoder
from dall_e.decoder import Decoder
from dall_e.utils import map_pixels, unmap_pixels
def load_model(path: str, device: torch.device = None) -> nn.Module:
if path.startswith('http://') or path.startswith('https://'):
... | 595 | 30.368421 | 68 | py |
biomedical | biomedical-main/streamlit_demo/vis_data_card.py | # from matplotlib_venn import venn2, venn3
import json
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import plotly.io as pio
from datasets import load_dataset
from plotly.subplots import make_subplots
from rich import print as rprint
from collections import Counter
from ngram import get_tu... | 13,350 | 31.80344 | 110 | py |
Stark | Stark-main/external/AR/ltr/run_training.py | import os
import sys
import argparse
import importlib
import multiprocessing
import cv2 as cv
import torch.backends.cudnn
env_path = os.path.join(os.path.dirname(__file__), '..')
if env_path not in sys.path:
sys.path.append(env_path)
import ltr.admin.settings as ws_settings
def run_training(train_module, train_... | 1,817 | 31.464286 | 132 | py |
Stark | Stark-main/external/AR/ltr/train_settings/bbreg/atom_prob_ml.py | import torch.optim as optim
from ltr.dataset import Lasot, TrackingNet, MSCOCOSeq, Got10k
from ltr.data import processing, sampler, LTRLoader
import ltr.models.bbreg.atom as atom_models
import ltr.models.loss.kl_regression as klreg_losses
import ltr.actors.bbreg as bbreg_actors
from ltr.trainers import LTRTrainer
impor... | 5,470 | 54.826531 | 133 | py |
Stark | Stark-main/external/AR/ltr/train_settings/bbreg/atom.py | import torch.nn as nn
import torch.optim as optim
from ltr.dataset import Lasot, TrackingNet, MSCOCOSeq, Got10k
from ltr.data import processing, sampler, LTRLoader
import ltr.models.bbreg.atom as atom_models
from ltr import actors
from ltr.trainers import LTRTrainer
import ltr.data.transforms as tfm
def run(settings)... | 5,194 | 53.114583 | 133 | py |
Stark | Stark-main/external/AR/ltr/train_settings/bbreg/atom_paper.py | import torch.nn as nn
import torch.optim as optim
from ltr.dataset import Lasot, TrackingNet, MSCOCOSeq
from ltr.data import processing, sampler, LTRLoader
import ltr.models.bbreg.atom as atom_models
from ltr import actors
from ltr.trainers import LTRTrainer
import ltr.data.transforms as tfm
def run(settings):
# ... | 5,105 | 52.747368 | 133 | py |
Stark | Stark-main/external/AR/ltr/train_settings/bbreg/atom_gmm_sampl.py | import torch.nn as nn
import torch.optim as optim
from ltr.dataset import Lasot, TrackingNet, MSCOCOSeq, Got10k
from ltr.data import processing, sampler, LTRLoader
import ltr.models.bbreg.atom as atom_models
from ltr import actors
from ltr.trainers import LTRTrainer
import ltr.data.transforms as tfm
def run(settings)... | 5,339 | 54.051546 | 133 | py |
Stark | Stark-main/external/AR/ltr/train_settings/dimp/prdimp50.py | import torch.optim as optim
from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq
from ltr.data import processing, sampler, LTRLoader
from ltr.models.tracking import dimpnet
import ltr.models.loss as ltr_losses
import ltr.models.loss.kl_regression as klreg_losses
import ltr.actors.tracking as tracking_actors
fr... | 6,832 | 55.471074 | 151 | py |
Stark | Stark-main/external/AR/ltr/train_settings/dimp/dimp18.py | import torch.nn as nn
import torch.optim as optim
from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq
from ltr.data import processing, sampler, LTRLoader
from ltr.models.tracking import dimpnet
import ltr.models.loss as ltr_losses
from ltr import actors
from ltr.trainers import LTRTrainer
import ltr.data.tran... | 6,584 | 54.336134 | 133 | py |
Stark | Stark-main/external/AR/ltr/train_settings/dimp/dimp50.py | import torch.nn as nn
import torch.optim as optim
from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq
from ltr.data import processing, sampler, LTRLoader
from ltr.models.tracking import dimpnet
import ltr.models.loss as ltr_losses
from ltr import actors
from ltr.trainers import LTRTrainer
import ltr.data.tran... | 6,649 | 54.416667 | 133 | py |
Stark | Stark-main/external/AR/ltr/train_settings/dimp/prdimp18.py | import torch.optim as optim
from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq
from ltr.data import processing, sampler, LTRLoader
from ltr.models.tracking import dimpnet
import ltr.models.loss as ltr_losses
import ltr.models.loss.kl_regression as klreg_losses
import ltr.actors.tracking as tracking_actors
fr... | 6,740 | 55.175 | 151 | py |
Stark | Stark-main/external/AR/ltr/train_settings/dimp/super_dimp.py | import torch.optim as optim
from ltr.dataset import Lasot, Got10k, TrackingNet, MSCOCOSeq
from ltr.data import processing, sampler, LTRLoader
from ltr.models.tracking import dimpnet
import ltr.models.loss as ltr_losses
import ltr.models.loss.kl_regression as klreg_losses
import ltr.actors.tracking as tracking_actors
fr... | 7,949 | 58.774436 | 133 | py |
Stark | Stark-main/external/AR/ltr/dataset/base_image_dataset.py | import torch.utils.data
from ltr.data.image_loader import jpeg4py_loader
class BaseImageDataset(torch.utils.data.Dataset):
""" Base class for image datasets """
def __init__(self, name, root, image_loader=jpeg4py_loader):
"""
args:
root - The root path to the dataset
i... | 2,421 | 25.043011 | 121 | py |
Stark | Stark-main/external/AR/ltr/dataset/ecssd.py | import os
from .base_image_dataset import BaseImageDataset
from ltr.data.image_loader import jpeg4py_loader, opencv_loader, imread_indexed
import torch
from collections import OrderedDict
from ltr.admin.environment import env_settings
from ltr.data.bounding_box_utils import masks_to_bboxes
class ECSSD(BaseImageDatase... | 3,059 | 35 | 120 | py |
Stark | Stark-main/external/AR/ltr/dataset/tracking_net.py | import torch
import os
import os.path
import numpy as np
import pandas
import random
from collections import OrderedDict
from ltr.data.image_loader import jpeg4py_loader
from .base_video_dataset import BaseVideoDataset
from ltr.admin.environment import env_settings
def list_sequences(root, set_ids):
""" Lists al... | 5,878 | 37.677632 | 169 | py |
Stark | Stark-main/external/AR/ltr/dataset/imagenetvid.py | import os
from .base_video_dataset import BaseVideoDataset
from ltr.data.image_loader import default_image_loader
import xml.etree.ElementTree as ET
import json
import torch
import random
from collections import OrderedDict
from ltr.admin.environment import env_settings
def get_target_to_image_ratio(seq):
anno = ... | 7,174 | 43.290123 | 120 | py |
Stark | Stark-main/external/AR/ltr/dataset/vos_base.py | import torch
from pathlib import Path
from collections import OrderedDict, defaultdict
import json
import numpy as np
import os
from .base_video_dataset import BaseVideoDataset
from ltr.data.image_loader import jpeg4py_loader, imread_indexed
from ltr.data.bounding_box_utils import masks_to_bboxes
class VOSMeta:
... | 16,000 | 39.102757 | 137 | py |
Stark | Stark-main/external/AR/ltr/dataset/lasot.py | import os
import os.path
import torch
import numpy as np
import pandas
import csv
import random
from collections import OrderedDict
from .base_video_dataset import BaseVideoDataset
from ltr.data.image_loader import jpeg4py_loader
from ltr.admin.environment import env_settings
class Lasot(BaseVideoDataset):
""" La... | 6,537 | 37.686391 | 130 | py |
Stark | Stark-main/external/AR/ltr/dataset/synthetic_video_blend.py | from collections import OrderedDict
from .base_video_dataset import BaseVideoDataset
from ltr.data.bounding_box_utils import masks_to_bboxes
import random
import torch
class SyntheticVideoBlend(BaseVideoDataset):
"""
Create a synthetic video by applying random transformations to an object (foreground) and pas... | 6,943 | 41.601227 | 113 | py |
Stark | Stark-main/external/AR/ltr/dataset/hku_is.py | import os
from .base_image_dataset import BaseImageDataset
from ltr.data.image_loader import jpeg4py_loader, opencv_loader, imread_indexed
import torch
from collections import OrderedDict
from ltr.admin.environment import env_settings
from ltr.data.bounding_box_utils import masks_to_bboxes
class HKUIS(BaseImageDatase... | 3,080 | 32.857143 | 120 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.