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
value |
|---|---|---|---|---|---|---|
fairness-comparison | fairness-comparison-master/fairness/metrics/EqOppo_fn_ratio.py | """Equal opportunity - Protected and unprotected False negative ratio"""
import math
import sys
import numpy as np
from fairness.metrics.utils import calc_fp_fn
from fairness.metrics.Metric import Metric
class EqOppo_fn_ratio(Metric):
def __init__(self):
Metric.__init__(self)
self.name = 'EqOppo_f... | 1,288 | 35.828571 | 85 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/Ratio.py | from fairness.metrics.Metric import Metric
class Ratio(Metric):
def __init__(self, metric_numerator, metric_denominator):
Metric.__init__(self)
self.numerator = metric_numerator
self.denominator = metric_denominator
self.name = self.numerator.get_name() + '_over_' + self.de... | 1,335 | 39.484848 | 117 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/DemParWelf.py | import sys
import numpy as np
import math
from fairness.metrics.UtilityMetric import UtilityMetric
class DemParWelf(UtilityMetric):
def __init__(self, welfare_fn, cost_fn, tau=None, transform_welf=None, name=None):
UtilityMetric.__init__(self, welfare_fn, cost_fn)
self.tau = tau
self.tran... | 1,837 | 37.291667 | 86 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/CV.py | import sys
import numpy
import math
from fairness.metrics.utils import calc_pos_protected_percents
from fairness.metrics.Metric import Metric
class CV(Metric):
def __init__(self):
Metric.__init__(self)
self.name = 'CV'
def calc(self, actual, predicted, dict_of_sensitive_lists, single_sensitiv... | 854 | 33.2 | 94 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/MCC.py | from fairness.metrics.Metric import Metric
from sklearn.metrics import matthews_corrcoef
class MCC(Metric):
def __init__(self):
Metric.__init__(self)
self.name = 'MCC'
def calc(self, actual, predicted, dict_of_sensitive_lists, single_sensitive_name,
unprotected_vals, positive_pred... | 399 | 32.333333 | 85 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/Diff.py | from fairness.metrics.Metric import Metric
class Diff(Metric):
def __init__(self, metric1, metric2):
Metric.__init__(self)
self.metric1 = metric1
self.metric2 = metric2
self.name = "diff:" + self.metric1.get_name() + 'to' + self.metric2.get_name()
def calc(self, actua... | 1,179 | 38.333333 | 112 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/__init__.py | 0 | 0 | 0 | py | |
fairness-comparison | fairness-comparison-master/fairness/metrics/FPR.py | from fairness.metrics.Metric import Metric
from fairness.metrics.TNR import TNR
class FPR(Metric):
def __init__(self):
Metric.__init__(self)
self.name = 'FPR'
def calc(self, actual, predicted, dict_of_sensitive_lists, single_sensitive_name,
unprotected_vals, positive_pred, dict_of... | 563 | 36.6 | 93 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/Accuracy.py | from fairness.metrics.Metric import Metric
from sklearn.metrics import accuracy_score
class Accuracy(Metric):
def __init__(self):
Metric.__init__(self)
self.name = 'accuracy'
def calc(self, actual, predicted, dict_of_sensitive_lists, single_sensitive_name,
unprotected_vals, positi... | 403 | 32.666667 | 85 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/EqOppo_tp_rate_ratio.py | """Equal opportunity - Protected and unprotected True Positive Rate ratio"""
import math
import sys
import numpy as np
from fairness.metrics.utils import calc_fp_fn, calc_tp_tn
from fairness.metrics.Metric import Metric
class EqOppo_tp_rate_ratio(Metric):
def __init__(self):
Metric.__init__(self)
... | 1,283 | 36.764706 | 85 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/DIBinary.py | import math
from fairness.metrics.utils import calc_pos_protected_percents
from fairness.metrics.Metric import Metric
class DIBinary(Metric):
"""
This metric calculates disparate imapct in the sense of the 80% rule before the 80%
threshold is applied. This is described as DI in: https://arxiv.org/abs/141... | 1,411 | 39.342857 | 99 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/EqOppo_fn_diff.py | """ Equal opportunity - Protected and unprotected False negative difference"""
import math
import sys
import numpy as np
from fairness.metrics.utils import calc_fp_fn
from fairness.metrics.Metric import Metric
class EqOppo_fn_diff(Metric):
def __init__(self):
Metric.__init__(self)
self.name = 'EqO... | 797 | 33.695652 | 85 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/EqOppo_fp_rate_ratio.py | """ Equal opportunity - Protected and unprotected False postives rate ratio"""
import math
import sys
import numpy as np
from fairness.metrics.utils import calc_fp_fn, calc_tp_tn
from fairness.metrics.Metric import Metric
class EqOppo_fp_rate_ratio(Metric):
def __init__(self):
Metric.__init__(self)
... | 1,268 | 36.323529 | 85 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/UtilityMetric.py | import numpy as np
from fairness.metrics.Metric import Metric
class UtilityMetric(Metric):
def __init__(self, welfare_fn, cost_fn):
"""
Parameters
----------
welfare_fn : function
Welfare function: (nonclass_vals, class_vals, predicted_vals, prot_index, unprot_index )... | 11,678 | 38.456081 | 109 | py |
fairness-comparison | fairness-comparison-master/fairness/metrics/list.py | import numpy
from fairness.metrics.Accuracy import Accuracy
from fairness.metrics.BCR import BCR
from fairness.metrics.CalibrationNeg import CalibrationNeg
from fairness.metrics.CalibrationPos import CalibrationPos
from fairness.metrics.CV import CV
from fairness.metrics.CVWelf import CVWelf
from fairness.metrics.DemP... | 3,604 | 44.0625 | 153 | py |
fairness-comparison | fairness-comparison-master/fairness/analysis/analysis-numerical-vs-binsensitive.py | import fire
import pandas as pd
from ggplot import *
import sys
##############################################################################
import pathlib
def main():
o = pathlib.Path(sys.argv[1]).parts[-1].split('.')[0]
f1 = pd.read_csv(sys.argv[1] + '_numerical-binsensitive.csv')
f2 = pd.read_csv(sy... | 3,294 | 48.179104 | 136 | py |
fairness-comparison | fairness-comparison-master/fairness/data/__init__.py | 0 | 0 | 0 | py | |
fairness-comparison | fairness-comparison-master/fairness/data/objects/German.py | import pandas as pd
from fairness.data.objects.Data import Data
class German(Data):
def __init__(self):
Data.__init__(self)
self.dataset_name = 'german'
self.class_attr = 'credit'
self.positive_class_val = 1
self.sensitive_attrs = ['sex', 'age']
self.privileged_cla... | 2,153 | 50.285714 | 100 | py |
fairness-comparison | fairness-comparison-master/fairness/data/objects/TwoGaussians.py | from fairness.data.objects.Data import Data
import numpy as np
import pandas as pd
import math
##############################################################################
TOTAL_ITEMS = 200
class TwoGaussians(Data):
def __init__(self, percent_pos):
Data.__init__(self)
self.dataset_name = 'two-... | 1,643 | 36.363636 | 79 | py |
fairness-comparison | fairness-comparison-master/fairness/data/objects/Data.py | import numpy as np
import pandas as pd
import pathlib
from fairness.results import local_results_path
BASE_DIR = local_results_path()
PACKAGE_DIR = pathlib.Path(__file__).parents[2]
RAW_DATA_DIR = PACKAGE_DIR / 'data' / 'raw'
PROCESSED_DATA_DIR = PACKAGE_DIR / 'data' / 'preprocessed'
RESULT_DIR = BASE_DIR / "results"
... | 6,526 | 38.557576 | 105 | py |
fairness-comparison | fairness-comparison-master/fairness/data/objects/PropublicaViolentRecidivism.py | from fairness.data.objects.Data import Data
class PropublicaViolentRecidivism(Data):
def __init__(self):
Data.__init__(self)
self.dataset_name = 'propublica-violent-recidivism'
self.class_attr = 'two_year_recid'
self.positive_class_val = 1
self.sensitive_attrs = ['sex', 'ra... | 1,979 | 54 | 100 | py |
fairness-comparison | fairness-comparison-master/fairness/data/objects/Sample.py | import math
import pandas as pd
from fairness.data.objects.Data import Data
class Sample(Data):
"""
A way to sample from a dataset for testing purposes.
num: the number of total items to sample (uniform with replacement)
prob_pos_class: the probability [0,1] that an item has a positive class value
... | 3,734 | 50.875 | 99 | py |
fairness-comparison | fairness-comparison-master/fairness/data/objects/__init__.py | 0 | 0 | 0 | py | |
fairness-comparison | fairness-comparison-master/fairness/data/objects/Adult.py | from fairness.data.objects.Data import Data
class Adult(Data):
def __init__(self):
Data.__init__(self)
self.dataset_name = 'adult'
self.class_attr = 'income-per-year'
self.positive_class_val = '>50K'
self.sensitive_attrs = ['race', 'sex']
self.privileged_class_names ... | 890 | 48.5 | 101 | py |
fairness-comparison | fairness-comparison-master/fairness/data/objects/Ricci.py | import pandas as pd
from fairness.data.objects.Data import Data
class Ricci(Data):
def __init__(self):
Data.__init__(self)
self.dataset_name = 'ricci'
# Class attribute will not be created until data_specific_processing is run.
self.class_attr = 'Class'
self.positive_class_... | 1,058 | 30.147059 | 88 | py |
fairness-comparison | fairness-comparison-master/fairness/data/objects/ProcessedData.py | import pandas as pd
import numpy
import numpy.random
TAGS = ["original", "numerical", "numerical-binsensitive", "categorical-binsensitive"]
TRAINING_PERCENT = 2.0 / 3.0
class ProcessedData():
def __init__(self, data_obj):
self.data = data_obj
self.dfs = dict((k, pd.read_csv(self.data.get_filename(... | 1,829 | 30.551724 | 95 | py |
fairness-comparison | fairness-comparison-master/fairness/data/objects/PropublicaRecidivism.py | from fairness.data.objects.Data import Data
class PropublicaRecidivism(Data):
def __init__(self):
Data.__init__(self)
self.dataset_name = 'propublica-recidivism'
self.class_attr = 'two_year_recid'
self.positive_class_val = 1
self.sensitive_attrs = ['sex', 'race']
se... | 1,692 | 51.90625 | 100 | py |
fairness-comparison | fairness-comparison-master/fairness/data/objects/list.py | from fairness.data.objects.Sample import Sample
from fairness.data.objects.Ricci import Ricci
from fairness.data.objects.Adult import Adult
from fairness.data.objects.German import German
from fairness.data.objects.PropublicaRecidivism import PropublicaRecidivism
from fairness.data.objects.PropublicaViolentRecidivism i... | 1,845 | 36.673469 | 106 | py |
fairness-comparison | fairness-comparison-master/analysis/correlation-vis/combine_results_files.py | import sys
import pandas as pd
ATTRS_TO_SAVE = [
"algorithm",
"params",
"run-id",
"DemParWelf-tpr-tpr",
"DIbinary",
"DIavgall",
"CV",
"comparative-sensitive-TPR", # e.g. name in data: race-TPRDiff
"accuracy",
"0-accuracy",
"1-accuracy",
"sensitive-accuracy", # e.g. name ... | 2,568 | 31.518987 | 99 | py |
julia | julia-master/test/llvmpasses/lit.cfg.py | import os
import sys
import re
import platform
import lit.util
import lit.formats
config.name = 'Julia'
config.suffixes = ['.ll','.jl']
config.test_source_root = os.path.dirname(__file__)
config.test_format = lit.formats.ShTest(True)
config.substitutions.append(('%shlibext', '.dylib' if platform.system() == 'Darwin' ... | 500 | 24.05 | 98 | py |
julia | julia-master/test/clangsa/lit.cfg.py | import os
import sys
import re
import platform
import lit.util
import lit.formats
config.name = 'Julia-GCChecker'
config.suffixes = ['.c','.cpp']
config.test_source_root = os.path.dirname(__file__)
config.test_format = lit.formats.ShTest(True)
config.substitutions.append(('%shlibext', '.dylib' if platform.system() ==... | 947 | 35.461538 | 98 | py |
julia | julia-master/contrib/normalize_triplet.py | #!/usr/bin/env python
import re, sys
# This script designed to mimic `src/PlatformNames.jl` in `BinaryProvider.jl`, which has
# a method `platform_key_abi()` to parse uname-like output into something standardized.
if len(sys.argv) < 2:
print("Usage: {} <host triplet> [<gcc version>] [<cxxabi11>]".format(sys.argv... | 4,497 | 28.986667 | 157 | py |
julia | julia-master/contrib/relative_path.py | import sys, os
if len(sys.argv) != 3:
sys.stderr.write("\nrelative_path.py - incomplete arguments: %s\n"%(sys.argv))
sys.exit(1)
# We always use `/` as the path separator, no matter what OS we're running on, since our
# shells and whatnot during the build are all POSIX shells/cygwin. We rely on the build
# sy... | 537 | 47.909091 | 89 | py |
MST | MST-main/real/train_code/template.py | def set_template(args):
# Set the templates here
if args.template.find('mst') >= 0:
args.input_setting = 'Y'
args.input_mask = None
if args.template.find('gap_net') >= 0 or args.template.find('admm_net') >= 0:
args.input_setting = 'Y'
args.input_mask = 'Phi_PhiPhiT'
... | 2,220 | 32.651515 | 124 | py |
MST | MST-main/real/train_code/utils.py | import numpy as np
import scipy.io as sio
import os
import glob
import re
import torch
import torch.nn as nn
import math
import random
def _as_floats(im1, im2):
float_type = np.result_type(im1.dtype, im2.dtype, np.float32)
im1 = np.asarray(im1, dtype=float_type)
im2 = np.asarray(im2, dtype=float_type)
... | 5,293 | 27.771739 | 93 | py |
MST | MST-main/real/train_code/dataset.py | import torch.utils.data as tud
import random
import torch
import numpy as np
import scipy.io as sio
class dataset(tud.Dataset):
def __init__(self, opt, CAVE, KAIST):
super(dataset, self).__init__()
self.isTrain = opt.isTrain
self.size = opt.size
# self.path = opt.data_path
... | 3,450 | 34.57732 | 83 | py |
MST | MST-main/real/train_code/option.py | import argparse
import template
parser = argparse.ArgumentParser(description="HyperSpectral Image Reconstruction Toolbox")
parser.add_argument('--template', default='mst',
help='You can set various templates in option.py')
# Hardware specifications
parser.add_argument("--gpu_id", type=str, default... | 2,663 | 49.264151 | 121 | py |
MST | MST-main/real/train_code/train.py | from architecture import *
from utils import *
from dataset import dataset
import torch.utils.data as tud
import torch
import torch.nn.functional as F
import time
import datetime
from torch.autograd import Variable
import os
from option import opt
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE... | 3,493 | 35.395833 | 115 | py |
MST | MST-main/real/train_code/architecture/MST_Plus_Plus.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
def norm_cdf(x):
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (m... | 10,188 | 30.544892 | 116 | py |
MST | MST-main/real/train_code/architecture/DGSMP.py | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class Resblock(nn.Module):
def __init__(self, HBW):
super(Resblock, self).__init__()
self.block1 = nn.Sequential(nn.Conv2d(HBW, HBW, kernel_size=3, stride=1, padding=1),
... | 15,283 | 46.318885 | 148 | py |
MST | MST-main/real/train_code/architecture/DAUHST.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
import math
import warnings
from torch import einsum
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
def norm_cdf(x):
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (mean < a - 2 * std) or (mean > b... | 13,343 | 35.26087 | 133 | py |
MST | MST-main/real/train_code/architecture/CST.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import einsum
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
from collections import defaultdict, Counter
import numpy as np
from tqdm import tqdm
import random
def uniform(a,... | 20,061 | 32.381032 | 129 | py |
MST | MST-main/real/train_code/architecture/MST.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
def norm_cdf(x):
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (m... | 8,814 | 28.881356 | 116 | py |
MST | MST-main/real/train_code/architecture/BIRNAT.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class self_attention(nn.Module):
def __init__(self, ch):
super(self_attention, self).__init__()
self.conv1 = nn.Conv2d(ch, ch // 8, 1)
self.conv2 = nn.Conv2d(ch, ch // 8, 1)
self.conv3 = nn.Conv2d(ch, ch, 1)
... | 13,326 | 35.412568 | 119 | py |
MST | MST-main/real/train_code/architecture/GAP_Net.py | import torch.nn.functional as F
import torch
import torch.nn as nn
def A(x,Phi):
temp = x*Phi
y = torch.sum(temp,1)
return y
def At(y,Phi):
temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1)
x = temp*Phi
return x
def shift_3d(inputs,step=2):
[bs, nC, row, col] = inputs.shape
for ... | 5,524 | 28.232804 | 81 | py |
MST | MST-main/real/train_code/architecture/Lambda_Net.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import einsum
class LambdaNetAttention(nn.Module):
def __init__(
self,
dim,
):
super().__init__()
self.dim = dim
self.to_q = nn.Linear(dim, dim//8, bias=Fa... | 5,680 | 30.38674 | 95 | py |
MST | MST-main/real/train_code/architecture/ADMM_Net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def A(x,Phi):
temp = x*Phi
y = torch.sum(temp,1)
return y
def At(y,Phi):
temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1)
x = temp*Phi
return x
class double_conv(nn.Module):
def __init__(self, in_channels, out_chann... | 6,191 | 29.653465 | 81 | py |
MST | MST-main/real/train_code/architecture/TSA_Net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
_NORM_BONE = False
def conv_block(in_planes, out_planes, the_kernel=3, the_stride=1, the_padding=1, flag_norm=False, flag_norm_act=True):
conv = nn.Conv2d(in_planes, out_planes, kernel_size=the_kernel, stride=the_stride, padding... | 14,086 | 41.687879 | 118 | py |
MST | MST-main/real/train_code/architecture/__init__.py | import torch
from .MST import MST
from .GAP_Net import GAP_net
from .ADMM_Net import ADMM_net
from .TSA_Net import TSA_Net
from .HDNet import HDNet, FDL
from .DGSMP import HSI_CS
from .BIRNAT import BIRNAT
from .MST_Plus_Plus import MST_Plus_Plus
from .Lambda_Net import Lambda_Net
from .CST import CST
from .DAUHST impo... | 2,403 | 36.5625 | 91 | py |
MST | MST-main/real/train_code/architecture/HDNet.py | import torch
import torch.nn as nn
import math
def default_conv(in_channels, out_channels, kernel_size, bias=True):
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=(kernel_size//2), bias=bias)
class MeanShift(nn.Conv2d):
def __init__(
self, rgb_range,
rgb_mean... | 12,665 | 33.048387 | 132 | py |
MST | MST-main/real/test_code/test.py | import torch
import os
import argparse
from utils import dataparallel
import scipy.io as sio
import numpy as np
from torch.autograd import Variable
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
parser = argparse.ArgumentParser(description="PyTorch HSIFUSION")
parser.add_argum... | 3,319 | 40.5 | 126 | py |
MST | MST-main/real/test_code/template.py | def set_template(args):
# Set the templates here
if args.template.find('mst') >= 0:
args.input_setting = 'Y'
args.input_mask = None
if args.template.find('cst') >= 0:
args.input_setting = 'Y'
args.input_mask = None
if args.template.find('gap_net') >= 0 or args.template.... | 1,876 | 33.127273 | 124 | py |
MST | MST-main/real/test_code/utils.py | import numpy as np
import scipy.io as sio
import os
import glob
import re
import torch
import torch.nn as nn
import math
import random
def _as_floats(im1, im2):
float_type = np.result_type(im1.dtype, im2.dtype, np.float32)
im1 = np.asarray(im1, dtype=float_type)
im2 = np.asarray(im2, dtype=float_type)
... | 5,293 | 27.771739 | 93 | py |
MST | MST-main/real/test_code/dataset.py | import torch.utils.data as tud
import random
import torch
import numpy as np
import scipy.io as sio
class dataset(tud.Dataset):
def __init__(self, opt, CAVE, KAIST):
super(dataset, self).__init__()
self.isTrain = opt.isTrain
self.size = opt.size
# self.path = opt.data_path
... | 3,450 | 34.57732 | 83 | py |
MST | MST-main/real/test_code/option.py | import argparse
import template
parser = argparse.ArgumentParser(description="HyperSpectral Image Reconstruction Toolbox")
parser.add_argument('--template', default='mst',
help='You can set various templates in option.py')
# Hardware specifications
parser.add_argument("--gpu_id", type=str, default... | 2,663 | 49.264151 | 121 | py |
MST | MST-main/real/test_code/architecture/MST_Plus_Plus.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
def norm_cdf(x):
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (m... | 10,153 | 30.534161 | 116 | py |
MST | MST-main/real/test_code/architecture/DGSMP.py | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class Resblock(nn.Module):
def __init__(self, HBW):
super(Resblock, self).__init__()
self.block1 = nn.Sequential(nn.Conv2d(HBW, HBW, kernel_size=3, stride=1, padding=1),
... | 15,283 | 46.318885 | 148 | py |
MST | MST-main/real/test_code/architecture/DAUHST.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
import math
import warnings
from torch import einsum
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
def norm_cdf(x):
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (mean < a - 2 * std) or (mean > b... | 13,343 | 35.26087 | 133 | py |
MST | MST-main/real/test_code/architecture/CST.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import einsum
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
from collections import defaultdict, Counter
import numpy as np
from tqdm import tqdm
import random
def uniform(a,... | 20,008 | 32.404007 | 129 | py |
MST | MST-main/real/test_code/architecture/MST.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
def norm_cdf(x):
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (m... | 8,814 | 28.881356 | 116 | py |
MST | MST-main/real/test_code/architecture/BIRNAT.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class self_attention(nn.Module):
def __init__(self, ch):
super(self_attention, self).__init__()
self.conv1 = nn.Conv2d(ch, ch // 8, 1)
self.conv2 = nn.Conv2d(ch, ch // 8, 1)
self.conv3 = nn.Conv2d(ch, ch, 1)
... | 13,326 | 35.412568 | 119 | py |
MST | MST-main/real/test_code/architecture/GAP_Net.py | import torch.nn.functional as F
import torch
import torch.nn as nn
def A(x,Phi):
temp = x*Phi
y = torch.sum(temp,1)
return y
def At(y,Phi):
temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1)
x = temp*Phi
return x
def shift_3d(inputs,step=2):
[bs, nC, row, col] = inputs.shape
for ... | 5,524 | 28.232804 | 81 | py |
MST | MST-main/real/test_code/architecture/Lambda_Net.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import einsum
class LambdaNetAttention(nn.Module):
def __init__(
self,
dim,
):
super().__init__()
self.dim = dim
self.to_q = nn.Linear(dim, dim//8, bias=Fa... | 5,680 | 30.38674 | 95 | py |
MST | MST-main/real/test_code/architecture/ADMM_Net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def A(x,Phi):
temp = x*Phi
y = torch.sum(temp,1)
return y
def At(y,Phi):
temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1)
x = temp*Phi
return x
class double_conv(nn.Module):
def __init__(self, in_channels, out_chann... | 6,191 | 29.653465 | 81 | py |
MST | MST-main/real/test_code/architecture/TSA_Net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
_NORM_BONE = False
def conv_block(in_planes, out_planes, the_kernel=3, the_stride=1, the_padding=1, flag_norm=False, flag_norm_act=True):
conv = nn.Conv2d(in_planes, out_planes, kernel_size=the_kernel, stride=the_stride, padding... | 14,086 | 41.687879 | 118 | py |
MST | MST-main/real/test_code/architecture/__init__.py | import torch
from .MST import MST
from .GAP_Net import GAP_net
from .ADMM_Net import ADMM_net
from .TSA_Net import TSA_Net
from .HDNet import HDNet, FDL
from .DGSMP import HSI_CS
from .BIRNAT import BIRNAT
from .MST_Plus_Plus import MST_Plus_Plus
from .Lambda_Net import Lambda_Net
from .CST import CST
from .DAUHST impo... | 2,403 | 36.5625 | 91 | py |
MST | MST-main/real/test_code/architecture/HDNet.py | import torch
import torch.nn as nn
import math
def default_conv(in_channels, out_channels, kernel_size, bias=True):
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=(kernel_size//2), bias=bias)
class MeanShift(nn.Conv2d):
def __init__(
self, rgb_range,
rgb_mean... | 12,665 | 33.048387 | 132 | py |
MST | MST-main/simulation/train_code/ssim_torch.py | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
return gauss / gauss.sum()
def create_windo... | 2,621 | 32.615385 | 114 | py |
MST | MST-main/simulation/train_code/template.py | def set_template(args):
# Set the templates here
if args.template.find('mst') >= 0:
args.input_setting = 'H'
args.input_mask = 'Phi'
if args.template.find('gap_net') >= 0 or args.template.find('admm_net') >= 0:
args.input_setting = 'Y'
args.input_mask = 'Phi_PhiPhiT'
... | 2,225 | 32.727273 | 124 | py |
MST | MST-main/simulation/train_code/utils.py | import scipy.io as sio
import os
import numpy as np
import torch
import logging
import random
from ssim_torch import ssim
def generate_masks(mask_path, batch_size):
mask = sio.loadmat(mask_path + '/mask.mat')
mask = mask['mask']
mask3d = np.tile(mask[:, :, np.newaxis], (1, 1, 28))
mask3d = np.transpose... | 8,889 | 36.510549 | 123 | py |
MST | MST-main/simulation/train_code/option.py | import argparse
import template
parser = argparse.ArgumentParser(description="HyperSpectral Image Reconstruction Toolbox")
parser.add_argument('--template', default='mst',
help='You can set various templates in option.py')
# Hardware specifications
parser.add_argument("--gpu_id", type=str, default... | 2,148 | 45.717391 | 121 | py |
MST | MST-main/simulation/train_code/train.py | from architecture import *
from utils import *
import torch
import scipy.io as scio
import time
import os
import numpy as np
from torch.autograd import Variable
import datetime
from option import opt
import torch.nn.functional as F
os.environ["CUDA_DEVICE_ORDER"] = 'PCI_BUS_ID'
os.environ["CUDA_VISIBLE_DEVICES"] = opt... | 5,046 | 37.526718 | 112 | py |
MST | MST-main/simulation/train_code/architecture/MST_Plus_Plus.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
def norm_cdf(x):
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (m... | 10,068 | 30.367601 | 116 | py |
MST | MST-main/simulation/train_code/architecture/DGSMP.py | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class Resblock(nn.Module):
def __init__(self, HBW):
super(Resblock, self).__init__()
self.block1 = nn.Sequential(nn.Conv2d(HBW, HBW, kernel_size=3, stride=1, padding=1),
... | 15,284 | 46.175926 | 148 | py |
MST | MST-main/simulation/train_code/architecture/DAUHST.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
import math
import warnings
from torch import einsum
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
def norm_cdf(x):
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (mean < a - 2 * std) or (mean > b... | 13,343 | 35.26087 | 133 | py |
MST | MST-main/simulation/train_code/architecture/CST.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import einsum
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
from collections import defaultdict, Counter
import numpy as np
from tqdm import tqdm
import random
def uniform(a,... | 19,782 | 32.41723 | 129 | py |
MST | MST-main/simulation/train_code/architecture/MST.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
def norm_cdf(x):
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (m... | 9,703 | 30.102564 | 116 | py |
MST | MST-main/simulation/train_code/architecture/BIRNAT.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class self_attention(nn.Module):
def __init__(self, ch):
super(self_attention, self).__init__()
self.conv1 = nn.Conv2d(ch, ch // 8, 1)
self.conv2 = nn.Conv2d(ch, ch // 8, 1)
self.conv3 = nn.Conv2d(ch, ch, 1)
... | 13,326 | 35.412568 | 119 | py |
MST | MST-main/simulation/train_code/architecture/GAP_Net.py | import torch.nn.functional as F
import torch
import torch.nn as nn
def A(x,Phi):
temp = x*Phi
y = torch.sum(temp,1)
return y
def At(y,Phi):
temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1)
x = temp*Phi
return x
def shift_3d(inputs,step=2):
[bs, nC, row, col] = inputs.shape
for ... | 5,525 | 28.084211 | 81 | py |
MST | MST-main/simulation/train_code/architecture/Lambda_Net.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import einsum
class LambdaNetAttention(nn.Module):
def __init__(
self,
dim,
):
super().__init__()
self.dim = dim
self.to_q = nn.Linear(dim, dim//8, bias=Fa... | 5,679 | 30.555556 | 95 | py |
MST | MST-main/simulation/train_code/architecture/ADMM_Net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def A(x,Phi):
temp = x*Phi
y = torch.sum(temp,1)
return y
def At(y,Phi):
temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1)
x = temp*Phi
return x
class double_conv(nn.Module):
def __init__(self, in_channels, out_chann... | 6,191 | 29.653465 | 81 | py |
MST | MST-main/simulation/train_code/architecture/TSA_Net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
_NORM_BONE = False
def conv_block(in_planes, out_planes, the_kernel=3, the_stride=1, the_padding=1, flag_norm=False, flag_norm_act=True):
conv = nn.Conv2d(in_planes, out_planes, kernel_size=the_kernel, stride=the_stride, padding... | 14,086 | 41.687879 | 118 | py |
MST | MST-main/simulation/train_code/architecture/__init__.py | import torch
from .MST import MST
from .GAP_Net import GAP_net
from .ADMM_Net import ADMM_net
from .TSA_Net import TSA_Net
from .HDNet import HDNet, FDL
from .DGSMP import HSI_CS
from .BIRNAT import BIRNAT
from .MST_Plus_Plus import MST_Plus_Plus
from .Lambda_Net import Lambda_Net
from .CST import CST
from .DAUHST impo... | 2,403 | 36.5625 | 91 | py |
MST | MST-main/simulation/train_code/architecture/HDNet.py | import torch
import torch.nn as nn
import math
def default_conv(in_channels, out_channels, kernel_size, bias=True):
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=(kernel_size//2), bias=bias)
class MeanShift(nn.Conv2d):
def __init__(
self, rgb_range,
rgb_mean... | 12,665 | 33.048387 | 132 | py |
MST | MST-main/simulation/test_code/test.py | from architecture import *
from utils import *
import scipy.io as scio
import torch
import os
import numpy as np
from option import opt
os.environ["CUDA_DEVICE_ORDER"] = 'PCI_BUS_ID'
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_id
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
if not torch.c... | 1,458 | 30.042553 | 90 | py |
MST | MST-main/simulation/test_code/template.py | def set_template(args):
# Set the templates here
if args.template.find('mst') >= 0:
args.input_setting = 'H'
args.input_mask = 'Phi'
if args.template.find('gap_net') >= 0 or args.template.find('admm_net') >= 0 or args.template.find('dnu')>= 0 \
or args.template.find('dauhst')>= ... | 1,163 | 28.846154 | 115 | py |
MST | MST-main/simulation/test_code/utils.py | import scipy.io as sio
import os
import numpy as np
import torch
import logging
from fvcore.nn import FlopCountAnalysis
def generate_masks(mask_path, batch_size):
mask = sio.loadmat(mask_path + '/mask.mat')
mask = mask['mask']
mask3d = np.tile(mask[:, :, np.newaxis], (1, 1, 28))
mask3d = np.transpose(... | 5,335 | 35.547945 | 118 | py |
MST | MST-main/simulation/test_code/option.py | import argparse
import template
parser = argparse.ArgumentParser(description="HyperSpectral Image Reconstruction Toolbox")
parser.add_argument('--template', default='mst',
help='You can set various templates in option.py')
# Hardware specifications
parser.add_argument("--gpu_id", type=str, default... | 1,362 | 37.942857 | 105 | py |
MST | MST-main/simulation/test_code/architecture/MST_Plus_Plus.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
def norm_cdf(x):
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (m... | 10,068 | 30.367601 | 116 | py |
MST | MST-main/simulation/test_code/architecture/DGSMP.py | import torch
import torch.nn as nn
from torch.nn.parameter import Parameter
import torch.nn.functional as F
class Resblock(nn.Module):
def __init__(self, HBW):
super(Resblock, self).__init__()
self.block1 = nn.Sequential(nn.Conv2d(HBW, HBW, kernel_size=3, stride=1, padding=1),
... | 15,284 | 46.175926 | 148 | py |
MST | MST-main/simulation/test_code/architecture/DAUHST.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
import math
import warnings
from torch import einsum
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
def norm_cdf(x):
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (mean < a - 2 * std) or (mean > b... | 13,343 | 35.26087 | 133 | py |
MST | MST-main/simulation/test_code/architecture/CST.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import einsum
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
from collections import defaultdict, Counter
import numpy as np
from tqdm import tqdm
import random
def uniform(a,... | 19,711 | 32.466893 | 129 | py |
MST | MST-main/simulation/test_code/architecture/MST.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
import math
import warnings
from torch.nn.init import _calculate_fan_in_and_fan_out
def _no_grad_trunc_normal_(tensor, mean, std, a, b):
def norm_cdf(x):
return (1. + math.erf(x / math.sqrt(2.))) / 2.
if (m... | 9,703 | 30.102564 | 116 | py |
MST | MST-main/simulation/test_code/architecture/BIRNAT.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class self_attention(nn.Module):
def __init__(self, ch):
super(self_attention, self).__init__()
self.conv1 = nn.Conv2d(ch, ch // 8, 1)
self.conv2 = nn.Conv2d(ch, ch // 8, 1)
self.conv3 = nn.Conv2d(ch, ch, 1)
... | 13,326 | 35.412568 | 119 | py |
MST | MST-main/simulation/test_code/architecture/GAP_Net.py | import torch.nn.functional as F
import torch
import torch.nn as nn
def A(x,Phi):
temp = x*Phi
y = torch.sum(temp,1)
return y
def At(y,Phi):
temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1)
x = temp*Phi
return x
def shift_3d(inputs,step=2):
[bs, nC, row, col] = inputs.shape
for ... | 5,525 | 28.084211 | 81 | py |
MST | MST-main/simulation/test_code/architecture/Lambda_Net.py | import torch.nn as nn
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import einsum
class LambdaNetAttention(nn.Module):
def __init__(
self,
dim,
):
super().__init__()
self.dim = dim
self.to_q = nn.Linear(dim, dim//8, bias=Fa... | 5,679 | 30.555556 | 95 | py |
MST | MST-main/simulation/test_code/architecture/ADMM_Net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def A(x,Phi):
temp = x*Phi
y = torch.sum(temp,1)
return y
def At(y,Phi):
temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1)
x = temp*Phi
return x
class double_conv(nn.Module):
def __init__(self, in_channels, out_chann... | 6,191 | 29.653465 | 81 | py |
MST | MST-main/simulation/test_code/architecture/TSA_Net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
_NORM_BONE = False
def conv_block(in_planes, out_planes, the_kernel=3, the_stride=1, the_padding=1, flag_norm=False, flag_norm_act=True):
conv = nn.Conv2d(in_planes, out_planes, kernel_size=the_kernel, stride=the_stride, padding... | 14,086 | 41.687879 | 118 | py |
MST | MST-main/simulation/test_code/architecture/__init__.py | import torch
from .MST import MST
from .GAP_Net import GAP_net
from .ADMM_Net import ADMM_net
from .TSA_Net import TSA_Net
from .HDNet import HDNet, FDL
from .DGSMP import HSI_CS
from .BIRNAT import BIRNAT
from .MST_Plus_Plus import MST_Plus_Plus
from .Lambda_Net import Lambda_Net
from .CST import CST
from .DAUHST impo... | 2,403 | 36.5625 | 91 | py |
MST | MST-main/simulation/test_code/architecture/HDNet.py | import torch
import torch.nn as nn
import math
def default_conv(in_channels, out_channels, kernel_size, bias=True):
return nn.Conv2d(
in_channels, out_channels, kernel_size,
padding=(kernel_size//2), bias=bias)
class MeanShift(nn.Conv2d):
def __init__(
self, rgb_range,
rgb_mean... | 12,665 | 33.048387 | 132 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.