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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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/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/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/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/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/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 |
USCL | USCL-main/train_USCL/simclr.py | import torch
from models.resnet_simclr import ResNetSimCLR
# from torch.utils.tensorboard import SummaryWriter
import torch.nn.functional as F
from loss.nt_xent import NTXentLoss
import os
import shutil
import sys
import time
import torch.nn as nn
apex_support = False
try:
sys.path.append('./apex')
from apex i... | 8,478 | 37.716895 | 127 | py |
USCL | USCL-main/train_USCL/linear_eval.py | import os
import yaml
import pickle
import torch
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn import preprocessing
import importlib.util
##################################### 设定 ####################################
fold = 1
self_... | 4,071 | 26.890411 | 104 | py |
USCL | USCL-main/train_USCL/NMI_loss.py | # -*- coding:utf-8 -*-
'''
Created on 2017年10月28日
@summary: 利用Python实现NMI计算
@author: dreamhome
'''
import math
import numpy as np
from sklearn import metrics
import time
import random
import torch
def MILoss(TensorA=None, TensorB=None):
# TensorA, TensorB = range(112*512*7*7), range(112*512*7*7)
#
# Tens... | 2,477 | 29.219512 | 100 | py |
USCL | USCL-main/train_USCL/models/model_resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from torch.nn import init
from .cbam import *
from .bam import *
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet18_cbam', 'resnet34_cbam', 'resnet50_cbam', 'resnet101_cbam',
'resnet152_cbam']
model_urls =... | 10,063 | 32.658863 | 119 | py |
USCL | USCL-main/train_USCL/models/resnet_simclr.py | import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
from .model_resnet import resnet18_cbam, resnet50_cbam
class ResNetSimCLR(nn.Module):
''' The ResNet feature extractor + projection head for SimCLR '''
def __init__(self, base_model, out_dim, pretrained=False):
... | 2,664 | 31.108434 | 101 | py |
USCL | USCL-main/train_USCL/models/bam.py | import torch
import math
import torch.nn as nn
import torch.nn.functional as F
class Flatten(nn.Module):
def forward(self, x):
return x.view(x.size(0), -1)
class ChannelGate(nn.Module):
def __init__(self, gate_channel, reduction_ratio=16, num_layers=1):
super(ChannelGate, self).__init__()
... | 2,729 | 53.6 | 147 | py |
USCL | USCL-main/train_USCL/models/cbam.py | import torch
import math
import torch.nn as nn
import torch.nn.functional as F
class BasicConv(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True, bn=True, bias=False):
super(BasicConv, self).__init__()
self.out_channels = out_pla... | 4,038 | 37.836538 | 154 | py |
USCL | USCL-main/train_USCL/models/baseline_encoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
class Encoder(nn.Module):
''' The 4 layer convolutional network backbone + 2 layer fc projection head '''
def __init__(self, out_dim=64):
super(Encoder, self).__init__()
self.conv1 = nn.Conv... | 1,184 | 24.76087 | 83 | py |
USCL | USCL-main/train_USCL/loss/nt_xent.py | import torch
import numpy as np
class NTXentLoss(torch.nn.Module):
def __init__(self, device, batch_size, temperature, use_cosine_similarity):
super(NTXentLoss, self).__init__()
self.batch_size = batch_size
self.temperature = temperature
self.device = device
self.softmax =... | 3,708 | 41.147727 | 130 | py |
USCL | USCL-main/train_USCL/data_aug/outpainting.py | import torch
import numpy as np
import random
class Outpainting(object):
"""Randomly mask out one or more patches from an image, we only need mask regions.
Args:
n_holes (int): Number of patches to cut out of each image.
length (int): The length (in pixels) of each square patch.
"""
def... | 1,491 | 33.697674 | 100 | py |
USCL | USCL-main/train_USCL/data_aug/sharpen.py | import torch
import numpy as np
from PIL import Image
from PIL import ImageFilter
class Sharpen(object):
""" Sharpen an image before inputing it to networks
Args:
degree (int): The sharpen intensity, from -1 to 5.
0 represents original image.
"""
def __init__(self, degree=0... | 2,260 | 34.888889 | 80 | py |
USCL | USCL-main/train_USCL/data_aug/dataset_wrapper_Ultrasound_Video_Mixup.py | import os
import random
from PIL import Image
import numpy as np
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
import torchvision.transforms as transforms
from data_aug.gaussian_blur import GaussianBlur
from data_aug.cutout import ... | 13,707 | 45.310811 | 167 | py |
USCL | USCL-main/train_USCL/data_aug/cutout.py | import torch
import numpy as np
class Cutout(object):
"""Randomly mask out one or more patches from an image.
Args:
n_holes (int): Number of patches to cut out of each image.
length (int): The length (in pixels) of each square patch.
"""
def __init__(self, n_holes, length):
sel... | 1,213 | 26.590909 | 84 | py |
USCL | USCL-main/train_USCL/data_aug/nonlin_trans.py | from __future__ import print_function
import random
import numpy as np
import torch
try: # SciPy >= 0.19
from scipy.special import comb
except ImportError:
from scipy.misc import comb
def bernstein_poly(i, n, t):
"""
The Bernstein polynomial of n, i as a function of t
"""
return comb(n, i) ... | 2,817 | 25.584906 | 101 | py |
USCL | USCL-main/eval_pretrained_model/eval_pretrained_model.py | import os
import sys
import time
import random
import argparse
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
import torch.optim as optim
from tools.my_dataset import COVIDDataset
from resnet_uscl import ResNetUSCL
apex_support... | 12,878 | 42.218121 | 136 | py |
USCL | USCL-main/eval_pretrained_model/resnet_uscl.py | import torch.nn as nn
import torchvision.models as models
class ResNetUSCL(nn.Module):
''' The ResNet feature extractor + projection head + classifier for USCL '''
def __init__(self, base_model, out_dim, pretrained=False):
super(ResNetUSCL, self).__init__()
self.resnet_dict = {"resnet18": mod... | 1,383 | 30.454545 | 101 | py |
USCL | USCL-main/eval_pretrained_model/tools/my_dataset.py | import os
import random
import pickle
from PIL import Image
from torch.utils.data import Dataset
random.seed(1)
class COVIDDataset(Dataset):
def __init__(self, data_dir, train=True, transform=None):
"""
POCUS Dataset
param data_dir: str
param transform: torch.transform
... | 1,079 | 29 | 77 | py |
real-robot-challenge | real-robot-challenge-main/python/pybullet_planning/utils/transformations.py | # -*- coding: utf-8 -*-
# transformations.py
# Copyright (c) 2006, Christoph Gohlke
# Copyright (c) 2006-2009, The Regents of the University of California
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions a... | 58,641 | 35.355859 | 79 | py |
pyparrot | pyparrot-master/docs/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# pyparrot documentation build configuration file, created by
# sphinx-quickstart on Tue May 29 13:55:14 2018.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# a... | 5,536 | 29.761111 | 92 | py |
ocropy2 | ocropy2-master/ocropy2/ocrnet.py | #def _patched_view_4d(*tensors):
# output = []
# for t in tensors:
# assert t.dim() == 3
# size = list(t.size())
# size.insert(2, 1)
# output += [t.contiguous().view(*size)]
# return output
#
#import torch.nn._functions.conv
#
#torch.nn._functions.conv._view4d = _patched_view_4d
fr... | 17,908 | 34.53373 | 100 | py |
ocropy2 | ocropy2-master/ocropy2/layers.py | import sys
import numpy as np
import torch
from torch import nn
from torch.legacy import nn as legnn
from torch.autograd import Variable
sys.modules["layers"] = sys.modules["ocroseg.layers"]
BD = "BD"
LBD = "LBD"
LDB = "LDB"
BDL = "BDL"
BLD = "BLD"
BWHD = "BWHD"
BDWH = "BDWH"
BWH = "BWH"
def lbd2bdl(x):
assert... | 6,762 | 28.792952 | 105 | py |
ocropy2 | ocropy2-master/ocropy2/inputs.py | import os
import sqlite3
import math
import numpy as np
import ocrnet
from PIL import Image
from StringIO import StringIO
import glob
import os.path
import codecs
import random as pyr
import re
import pylab
import ocrcodecs
import scipy.ndimage as ndi
import lineest
verbose = True
def image(x, normalize=True, gray=Fa... | 8,389 | 30.541353 | 82 | py |
ocropy2 | ocropy2-master/ocropy2/psegutils.py | from __future__ import print_function
import os
# import sl,morph
import torch
import scipy.ndimage as ndi
from pylab import *
from scipy.ndimage import filters, morphology, interpolation
from torch.autograd import Variable
def sl_width(s):
return s.stop - s.start
def sl_area(s):
return sl_width(s[0]) * s... | 12,723 | 30.730673 | 111 | py |
ImageNetV2 | ImageNetV2-master/code/train_imagenet_dataset_discriminator.py | import json
import pathlib
import concurrent.futures as fs
import os
import time
import math
import argparse
import random
import click
import numpy as np
import torchvision.models as models
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from tqdm import tq... | 8,705 | 36.852174 | 135 | py |
ImageNetV2 | ImageNetV2-master/code/make_imagenet_folders.py | import json
import pathlib
import concurrent.futures as fs
import os
import time
import math
import argparse
import random
import click
import numpy as np
import torchvision.models as models
import torchvision.transforms as transforms
import torch.optim as optim
from torch.optim import lr_scheduler
from tqdm import tq... | 2,511 | 27.545455 | 127 | py |
ImageNetV2 | ImageNetV2-master/code/featurize_candidates.py | import argparse
import io
import os
import pickle
import tarfile
import time
from timeit import default_timer as timer
import json
import boto3
import numpy as np
import skimage.transform
import torch
import torchvision.models as models
from torch.autograd import Variable
from torch import nn
import candidate_data
imp... | 3,328 | 39.108434 | 129 | py |
ImageNetV2 | ImageNetV2-master/code/featurize_test.py | import argparse
import io
import pickle
import tarfile
import time
from timeit import default_timer as timer
import boto3
import numpy as np
import skimage.transform
import torch
import torchvision.models as models
from torch.autograd import Variable
from torch import nn
import candidate_data
import featurize
import ... | 2,873 | 35.379747 | 119 | py |
ImageNetV2 | ImageNetV2-master/code/featurize.py | import io
import pickle
import sys
import tarfile
import time
import boto3
import imageio
import numpy as np
import skimage.transform
import torch
from torch.autograd import Variable
from torch import nn
import torchvision.models as models
import utils
def vgg16_features(images, batch_size=60, use_gpu=True):
mode... | 4,394 | 35.932773 | 96 | py |
ImageNetV2 | ImageNetV2-master/code/eval.py | import json
import pathlib
import click
import numpy as np
import torchvision.models
from tqdm import tqdm
import candidate_data
import eval_utils
import image_loader
import imagenet
import pretrainedmodels
import pretrainedmodels.utils as pretrained_utils
import torch
import os
import time
torch.backends.cudnn.dete... | 7,435 | 41.735632 | 138 | py |
ImageNetV2 | ImageNetV2-master/code/eval_utils.py | import math
from timeit import default_timer as timer
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
import tqdm
import image_loader
class ImageLoaderDataset(torch.utils.data.Dataset):
def __init__(self, filenames, imgnet, cds, size, verbose=Fals... | 3,351 | 39.878049 | 134 | py |
COV19D_3rd | COV19D_3rd-main/Seg-Exct-Classif-Pipeline-Hybrid Method.py | # -*- KENAN MORANI - IZMIR DEMOCRACY UNIVERSITY -*-
#### COV19-CT DB Database #####
### part of IEEE ICASSP 2023: AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition (AI-MIA-COV19D)
### at https://mlearn.lincoln.ac.uk/icassp-2023-ai-mia/
#### B. 3rd COV19D Competition ---- I. Covid-19 Detectio... | 65,932 | 32.215617 | 171 | py |
COV19D_3rd | COV19D_3rd-main/loading_models/Loading-Models.py |
## Image Process + CNN Model - no slcie removal
h=w=224
def make_model():
model = models.Sequential()
# Convulotional Layer 1
model.add(layers.Conv2D(16,(3,3),input_shape=(h,w,1), padding="same"))
model.add(layers.BatchNormalization())
model.add(layers.ReLU())
model.add(layers.MaxPool... | 6,156 | 28.743961 | 109 | py |
MetaSAug | MetaSAug-main/MetaSAug_LDAM_train.py | import os
import time
import argparse
import random
import copy
import torch
import torchvision
import numpy as np
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision.transforms as transforms
from data_utils import *
from resnet import *
import shutil
from loss import *
parser = argp... | 12,114 | 32.559557 | 115 | py |
MetaSAug | MetaSAug-main/resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from torch.autograd import Variable
import torch.nn.init as init
def to_var(x, requires_grad=True):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x, requires_grad=requires_grad)
class MetaModule(nn.Module):
... | 10,031 | 34.828571 | 120 | py |
MetaSAug | MetaSAug-main/loss.py | import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import math
import torch.nn.functional as F
import pdb
class EstimatorCV():
def __init__(self, feature_num, class_num):
super(EstimatorCV, self).__init__()
self.class_num = class_num
self.CoVariance =... | 4,318 | 34.401639 | 128 | py |
MetaSAug | MetaSAug-main/data_utils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision
import numpy as np
import copy
np.random.seed(6)
def build_dataset(dataset,num_meta... | 3,070 | 33.897727 | 128 | py |
MetaSAug | MetaSAug-main/MetaSAug_test.py | import os
import time
import argparse
import random
import copy
import torch
import torchvision
import numpy as np
import torch.nn.functional as F
from torch.autograd import Variable
import torch.nn as nn
import torchvision.transforms as transforms
from data_utils import *
from resnet import *
import shutil
import gc
... | 4,032 | 23.295181 | 77 | py |
MetaSAug | MetaSAug-main/ImageNet_iNat/ResNet.py | from resnet_meta import *
from utils import *
from os import path
def create_model(use_selfatt=False, use_fc=False, dropout=None, stage1_weights=False, dataset=None, log_dir=None, test=False, *args):
print('Loading Scratch ResNet 50 Feature Model.')
if not use_fc:
resnet50 = FeatureMeta(Bottl... | 1,473 | 39.944444 | 133 | py |
MetaSAug | MetaSAug-main/ImageNet_iNat/test.py |
import os
import time
import argparse
import random
import copy
import torch
import torchvision
import numpy as np
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision.transforms as transforms
from data_utils import *
from dataloader import load_data_distributed
import shutil
from Res... | 7,842 | 33.70354 | 177 | py |
MetaSAug | MetaSAug-main/ImageNet_iNat/dataloader.py | import numpy as np
import torchvision
from torch.utils.data import Dataset, DataLoader, ConcatDataset
from torchvision import transforms
import os
from PIL import Image
import json
# Image statistics
RGB_statistics = {
'iNaturalist18': {
'mean': [0.466, 0.471, 0.380],
'std': [0.195, 0.194, 0.192]
... | 4,682 | 28.828025 | 129 | py |
MetaSAug | MetaSAug-main/ImageNet_iNat/loss.py | # -*- coding: utf-8 -*
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import math
import torch.nn.functional as F
import pdb
def MI(outputs_target):
batch_size = outputs_target.size(0)
softmax_outs_t = nn.Softmax(dim=1)(outputs_target)
avg_softmax_outs_t = torch.... | 5,772 | 36.245161 | 130 | py |
MetaSAug | MetaSAug-main/ImageNet_iNat/utils.py | import numpy as np
import matplotlib.pyplot as plt
import torch
from sklearn.metrics import f1_score
import torch.nn.functional as F
import importlib
def source_import(file_path):
"""This function imports python module directly from source code using importlib"""
spec = importlib.util.spec_from_file_location(... | 7,719 | 32.859649 | 109 | py |
MetaSAug | MetaSAug-main/ImageNet_iNat/data_utils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision
import numpy as np
import copy
np.random.seed(6)
#random.seed(2)
def build_dataset(d... | 3,467 | 31.111111 | 86 | py |
MetaSAug | MetaSAug-main/ImageNet_iNat/train.py |
import os
import time
import argparse
import random
import copy
import torch
import torchvision
import numpy as np
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision.transforms as transforms
from data_utils import *
# import resnet
from dataloader import load_data_distributed
import... | 13,612 | 38.005731 | 184 | py |
MetaSAug | MetaSAug-main/ImageNet_iNat/resnet_meta.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from torch.autograd import Variable
import torch.nn.init as init
def to_var(x, requires_grad=True):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x, requires_grad=requires_grad)
class MetaModule(nn.Module):
... | 18,152 | 35.306 | 120 | py |
coocmap | coocmap-main/match.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
from collections import Counter
import numpy as np
import embeddings
np.set_printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)})
MAX_SVD_DIM = 5000 # maximum SVD to avoid long compute time
### initialization methods ###
def vecmap_unsup(x, z, norm_proc... | 10,281 | 32.061093 | 111 | py |
MateriAppsInstaller | MateriAppsInstaller-master/docs/sphinx/en/source/conf.py | # -*- coding: utf-8 -*-
#
# MateriApps-Installer documentation build configuration file, created by
# sphinx-quickstart on Sun May 1 14:29:22 2020.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated... | 5,684 | 28.455959 | 79 | py |
MateriAppsInstaller | MateriAppsInstaller-master/docs/sphinx/ja/source/conf.py | # -*- coding: utf-8 -*-
#
# MateriApps-Installer documentation build configuration file, created by
# sphinx-quickstart on Sun May 1 14:29:22 2020.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated... | 5,686 | 28.466321 | 79 | py |
harmonic | harmonic-main/docs/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 6,728 | 29.586364 | 100 | py |
DeepGAR | DeepGAR-main/test.py | from common import utils
from collections import defaultdict
from datetime import datetime
from sklearn.metrics import roc_auc_score, confusion_matrix
from sklearn.metrics import precision_recall_curve, average_precision_score
import torch
USE_ORCA_FEATS = False # whether to use orca motif counts along with embeddings... | 8,369 | 46.828571 | 115 | py |
DeepGAR | DeepGAR-main/deepgar.py | HYPERPARAM_SEARCH = False
HYPERPARAM_SEARCH_N_TRIALS = None # how many grid search trials to run
# (set to None for exhaustive search)
import argparse
from itertools import permutations
import pickle
from queue import PriorityQueue
import os
import random
import time
import ne... | 31,768 | 39.31599 | 205 | py |
DeepGAR | DeepGAR-main/common/utils.py | from collections import defaultdict, Counter
from deepsnap.graph import Graph as DSGraph
from deepsnap.batch import Batch
from deepsnap.dataset import GraphDataset
import torch
import torch.optim as optim
import torch_geometric.utils as pyg_utils
from torch_geometric.data import DataLoader
import networkx as nx
import... | 11,535 | 39.477193 | 120 | py |
DeepGAR | DeepGAR-main/common/data.py | import os
import pickle
import random
from deepsnap.graph import Graph as DSGraph
from deepsnap.batch import Batch
from deepsnap.dataset import GraphDataset, Generator
import networkx as nx
import numpy as np
from sklearn.manifold import TSNE
import torch
import torch.multiprocessing as mp
import torch.nn.functional a... | 24,005 | 44.20904 | 159 | py |
DeepGAR | DeepGAR-main/common/models.py | """Defines all graph embedding models"""
from functools import reduce
import random
import networkx as nx
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_geometric.nn as pyg_nn
import torch_geometric.utils as pyg_utils
from common import utils
from common import feat... | 16,350 | 41.250646 | 191 | py |
nepali-ner | nepali-ner-master/app.py | """
Needs code structuring
Date - 08/14/2020
"""
import torch
import logging
import sys
from flask import Flask, render_template, request
from utils.dataloader2 import Dataloader
from models.models import LSTMTagger
from config.config import Configuration
app = Flask(__name__)
def get_logger():
logger =... | 2,497 | 25.294737 | 98 | py |
nepali-ner | nepali-ner-master/train.py | #!/usr/bin/env python3
'''
Trainer
Author: Oyesh Mann Singh
'''
import os
from utils.eval import Evaluator
from tqdm import tqdm, tqdm_notebook, tnrange
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.metrics import accuracy_score
torch.manual_seed(163)
tqdm.pandas(desc='Progress'... | 8,092 | 34.034632 | 146 | py |
nepali-ner | nepali-ner-master/models/models.py | '''
Models
Author: Oyesh Mann Singh
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
from uniseg.graphemecluster import grapheme_clusters
tqdm.pandas(desc='Progress')
class LSTMTagger(nn.Module):
def __init__(self, config, dataloader):
... | 9,650 | 37.146245 | 104 | py |
nepali-ner | nepali-ner-master/utils/dataloader2.py | #!/usr/bin/env python3
'''
NER Dataloader
Author: Oyesh Mann Singh
Date: 10/14/2019
Data format:
<WORD> <NER-tag>
'''
import os
import pickle
from torchtext import data, vocab
from torchtext.datasets import SequenceTaggingDataset
class Dataloader():
def __init__(self, config, k):
... | 2,467 | 27.367816 | 99 | py |
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