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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LPF-SGD | LPF-SGD-master/codes/adversarial/utility/initialize.py | import random
import torch
def initialize(args, seed: int):
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
| 312 | 21.357143 | 46 | py |
LPF-SGD | LPF-SGD-master/codes/adversarial/utility/train_utils.py | import torch
from torchvision import transforms
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR10, CIFAR100, ImageNet, MNIST, ImageFolder
def get_loader(args, training, augment=False):
""" function to get data loader specific to different datasets
"""
if args.dtype == 'cifar... | 7,451 | 32.41704 | 80 | py |
LPF-SGD | LPF-SGD-master/codes/adversarial/utility/cutout.py | import torch
class Cutout:
def __init__(self, size=16, p=0.5):
self.size = size
self.half_size = size // 2
self.p = p
def __call__(self, image):
if torch.rand([1]).item() > self.p:
return image
left = torch.randint(-self.half_size, image.size(1) - self.hal... | 620 | 28.571429 | 89 | py |
LPF-SGD | LPF-SGD-master/codes/adversarial/utility/step_lr.py | import torch
import numpy as np
import logging
class StepLR:
def __init__(self, optimizer, learning_rate: float, total_epochs: int):
self.optimizer = optimizer
self.total_epochs = total_epochs
self.base = learning_rate
def step(self, epoch):
# let total_epochs = 200
... | 1,936 | 29.746032 | 118 | py |
LPF-SGD | LPF-SGD-master/codes/adversarial/utility/entropy_sgd.py | from torch.optim.optimizer import Optimizer, required
import copy
from copy import deepcopy
import torch
import numpy as np
# class EntropySGD(Optimizer):
# def __init__(self, params, wd, lr, momentum, gamma_0, gamma_1, L=5, eta_prime = 0.1, epsilon=0.0001, alpha=0.75, nesterov=False):
# defaults = dict(... | 9,680 | 38.194332 | 168 | py |
LPF-SGD | LPF-SGD-master/codes/adversarial/models/pyramidnet.py | import torch
import torch.nn as nn
import math
#from math import round
import torch.utils.model_zoo as model_zoo
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
clas... | 9,018 | 36.736402 | 168 | py |
LPF-SGD | LPF-SGD-master/codes/adversarial/models/shake_shake.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch
from torch.autograd import Function
class ShakeFunction(Function):
@staticmethod
def forward(ctx, x1, x2, alpha, beta):
ctx.save_for_backward(x1, x2, alpha, beta)
y = x1 * alpha + x2 * (1 - alpha)
return ... | 7,669 | 30.825726 | 78 | py |
LPF-SGD | LPF-SGD-master/codes/adversarial/models/wide_res_net.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu1 = nn.ReLU(inplace=True)
se... | 12,862 | 42.60339 | 116 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/sharp_dn.py | import torch
import torch.nn as nn
from models import ResNet18
import numpy as np
import random
from utils import get_loader
from flat_meas import fro_norm, entropy_one_direc, \
entropy_grad, eig_trace, eps_flatness, \
pac_bayes, entropy, low_pass, fim, shannon_entropy
from u... | 9,211 | 32.256318 | 117 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/sharp_dd.py | import torch
import torch.nn as nn
from models import resnet_dd
import numpy as np
import random
from utils import get_loader
from flat_meas import fro_norm, entropy_one_direc, \
entropy_grad, eig_trace, eps_flatness, \
pac_bayes, entropy, low_pass, fim, shannon_entropy
from ... | 9,035 | 32.466667 | 117 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/sharp_ln.py | import torch
import torch.nn as nn
from models import ResNet18
import numpy as np
import random
from utils import get_loader
from flat_meas import fro_norm, entropy_one_direc, \
entropy_grad, eig_trace, eps_flatness, \
pac_bayes, entropy, low_pass, fim, shannon_entropy
from u... | 9,252 | 32.046429 | 117 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/train_dd.py | import torch
import torch.nn as nn
import torch.optim as optim
from models import resnet_dd
import time
import logging
import numpy as np
import glob
import os
import random
from torch.utils.tensorboard import SummaryWriter
from utils import get_loader, class_model_run
import argparse
import shutil
def create_path(pa... | 4,127 | 32.836066 | 96 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/sharp.py | from args import get_args
import torch
import torch.nn as nn
from models import resnet18_narrow
import numpy as np
import random
from utils import get_loader
from flat_meas import fro_norm, entropy_one_direc, \
entropy_grad, eig_trace, eps_flatness, \
pac_bayes, entropy, low_... | 8,054 | 32.5625 | 117 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/train_ln.py | import torch
import torch.nn as nn
import torch.optim as optim
from models import ResNet18
import time
import logging
import numpy as np
import glob
import os
import random
from torch.utils.tensorboard import SummaryWriter
from utils import get_loader, class_model_run
import argparse
def create_path(path):
if os.... | 3,893 | 31.181818 | 97 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/train_dn.py | import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from models import ResNet18
import time
import logging
import numpy as np
import glob
import os
import random
from torch.utils.tensorboard import SummaryWriter
from utils import get_loader, class_model_run
def create_path(path):
if os.... | 4,096 | 32.308943 | 97 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/train.py | import torch
import torch.nn as nn
import torch.optim as optim
from models import resnet18_narrow
import time
import logging
import numpy as np
import glob
import os
import random
from torch.utils.tensorboard import SummaryWriter
from utils import get_loader, class_model_run
import argparse
def str2bool(v):
if is... | 4,654 | 29.227273 | 97 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/models/conv_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(256, 120)
self.fc2 = nn.Linear(120, 84)
... | 3,765 | 35.211538 | 116 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/models/resnet.py | '''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansi... | 8,437 | 32.752 | 129 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/models/resnet_narrow.py | '''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion... | 8,988 | 37.251064 | 137 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/models/resnet_dd.py | '''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansi... | 7,893 | 34.558559 | 129 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/flat_meas/low_pass.py | import torch
import numpy as np
import copy
from tqdm import tqdm
def _low_pass(func, theta_star, mcmc_itr, sigma):
out = 0.0
d = theta_star.shape[0]
for i in range(mcmc_itr):
theta = theta_star + torch.from_numpy(np.random.multivariate_normal(np.zeros((d)), (sigma**2)*np.eye(d))).type(torch.float... | 1,525 | 31.468085 | 147 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/flat_meas/eps_flatness.py | import torch
import numpy as np
import torch.optim as optim
from utils.train_utils import AverageMeter
import copy
import sys
import logging
def load_weights(model, params, grads):
for mp, p, g in zip(model.parameters(), params, grads):
mp.data = copy.deepcopy(p.data)
mp.grad.data = copy.deepcopy(... | 4,243 | 35.273504 | 148 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/flat_meas/fim.py | from tqdm import tqdm
import torch
def fim(model_func):
r'''
Fisher information matrix
Inputs:
func : model functional class
Outputs:
frobenius norm
'''
out = 0.0
theta_star = torch.cat([p.view(-1) for p in model_func.model.parameters()])
return (theta_st... | 618 | 21.925926 | 79 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/flat_meas/local_entropy.py | import torch
import numpy as np
import copy
from tqdm import tqdm
from torch.optim.optimizer import Optimizer, required
from copy import deepcopy
from scipy.integrate import quad
class EntropySGD(Optimizer):
def __init__(self, params, config={}):
defaults = dict(lr=0.01, momentum=0, damp=0,
... | 7,474 | 31.929515 | 141 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/flat_meas/lanczos.py | import numpy as np
import sys
sys.path.append('../')
from utils.spectral_utils import tridiag_to_eigv
from tqdm import tqdm
from scipy.sparse.linalg import LinearOperator as ScipyLinearOperator
from scipy.sparse.linalg import eigsh
from warnings import warn
import torch
import time
import logging
def lanczos(func, di... | 7,905 | 28.173432 | 95 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/flat_meas/shannon_entropy.py | # Shannon Entropy
import torch
def shannon_entropy(model_func):
res = 0.0
for inputs, targets in model_func.dataloader['train']:
if model_func.use_cuda:
inputs = inputs.cuda()
targets = targets.cuda()
with torch.no_grad():
outputs = model_func.model(inputs)
res += torch.sum(torch.nn.Softmax(1)(out... | 429 | 24.294118 | 83 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/flat_meas/pac_bayes.py | import torch
import numpy as np
import copy
from utils.train_utils import AverageMeter
from utils import vector_to_parameter_tuple
import time
import logging
def load_weights(model, params):
for mp, p in zip(model.parameters(), params):
mp.data = copy.deepcopy(p.data)
def pac_bayes(model_func, mcmc_itr,... | 3,282 | 33.557895 | 152 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/flat_meas/fro_norm.py | from tqdm import tqdm
import torch
def fro_norm(model_func, mcmc_itr):
r'''
Frobenium norm approximation based on [put paper here]
Inputs:
func : model functional class
mcmc_itr : mcmc_itr
Outputs:
frobenius norm
'''
out = 0.0
for i in tqdm(range... | 1,042 | 27.972222 | 122 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/utils/fit.py | import torch
import logging
import time
from utils.train_utils import AverageMeter, accuracy
import numpy as np
def class_model_run(phase, loader, model, criterion, optimizer, args):
"""
Function to forward pass through classification problem
"""
logger = logging.getLogger('my_log')
if phase ... | 1,918 | 28.984375 | 79 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/utils/spectral_utils.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
from tqdm import tqdm
import math
import numpy as np
def get_hessian(batch_loss, model):
grads = torch.autograd.grad(batch_loss, model.parameters(), create_graph=True)
grads = torch.cat([x.vie... | 9,806 | 38.071713 | 108 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/utils/train_utils.py | import sys
sys.path.append("../")
import torch
from torchvision import datasets, transforms
from sklearn.metrics import average_precision_score
from utils.data_loader_mnist import MNIST
from utils.data_loader_cifar import CIFAR10, CIFAR10_NOISY
def get_loader(args, training, data_load_fraction=1.0, label_noise=0.0):
... | 5,319 | 31.638037 | 91 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/utils/data_loader_cifar.py | from __future__ import print_function
from PIL import Image
import os
import os.path
import numpy as np
import sys
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
import torch.utils.data as data
from torchvision.datasets.utils import download_url, check_integrity
import torch
from tor... | 9,561 | 37.401606 | 120 | py |
LPF-SGD | LPF-SGD-master/codes/sharpness_v_generalization/utils/data_loader_mnist.py | from __future__ import print_function
from torchvision.datasets.vision import VisionDataset
import warnings
from PIL import Image
import os
import os.path
import numpy as np
import torch
import codecs
from sklearn.model_selection import train_test_split
from torchvision.datasets.utils import download_url, download_and_... | 9,832 | 36.10566 | 118 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/sgd_train.py | import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from models import cifar_resnet50, cifar_resnet18, cifar_resnet101, LeNet
from torchvision.models import resnet18 as imagenet_resnet18
from torchvision.models import resnet50 as imagenet_resnet50
from torchvision.models import resnet101 as i... | 8,590 | 33.922764 | 102 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/args.py | import argparse
import torch
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
... | 1,671 | 29.4 | 81 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/sam_train.py | import torch
import torch.nn as nn
import torch.optim as optim
from models import cifar_resnet50, cifar_resnet18, cifar_resnet101, LeNet
from torchvision.models import resnet18 as imagenet_resnet18
from torchvision.models import resnet50 as imagenet_resnet50
from torchvision.models import resnet101 as imagenet_resnet10... | 9,150 | 33.402256 | 102 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/entropy_train.py | import torch
import torch.nn as nn
import torch.optim as optim
from models import cifar_resnet50, cifar_resnet18, cifar_resnet101, LeNet
from torchvision.models import resnet18 as imagenet_resnet18
from torchvision.models import resnet50 as imagenet_resnet50
from torchvision.models import resnet101 as imagenet_resnet10... | 9,805 | 33.650177 | 100 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/lpf_train.py | import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from models import cifar_resnet50, cifar_resnet18, cifar_resnet101, LeNet
from torchvision.models import resnet18 as imagenet_resnet18
from torchvision.models import resnet50 as imagenet_resnet50
from torchvision.models import resnet101 as i... | 10,009 | 35.268116 | 112 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/smoothout_train.py | import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from models import cifar_resnet50, cifar_resnet18, cifar_resnet101, LeNet
from torchvision.models import resnet18 as imagenet_resnet18
from torchvision.models import resnet50 as imagenet_resnet50
from torchvision.models import resnet101 as i... | 9,312 | 34.410646 | 102 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/models/resnet.py | '''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansi... | 10,717 | 39.142322 | 121 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/models/wide_resnet.py | from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicUnit(nn.Module):
def __init__(self, channels: int, dropout: float):
super(BasicUnit, self).__init__()
self.block = nn.Sequential(OrderedDict([
("0_normalization", nn.Batch... | 4,134 | 40.35 | 114 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/models/fcnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class LeNet(nn.Module):
def __init__(self, num_classes):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout(0.25)
self.dropout2 ... | 2,917 | 33.738095 | 116 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/flat_meas/low_pass.py | import torch
import numpy as np
import copy
from tqdm import tqdm
import sys; sys.path.append('..')
from utils.train_utils import AverageMeter, accuracy
def _low_pass(func, theta_star, mcmc_itr, sigma):
out = 0.0
d = theta_star.shape[0]
for i in range(mcmc_itr):
theta = theta_star + torch.from_num... | 1,933 | 30.193548 | 147 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/flat_meas/check.py | import torch
from torchvision.models import resnet18
model = resnet18()
def new_func(a):
return a.data.add_(torch.empty_like(a, device=a.data.device).normal_(0,1))
new_list = list(map(lambda a: , model.parameters()))
print(model) | 235 | 22.6 | 75 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/flat_meas/eps_flatness.py | import torch
import numpy as np
import torch.optim as optim
from utils.train_utils import AverageMeter
import copy
import sys
import logging
def load_weights(model, params, grads):
for mp, p, g in zip(model.parameters(), params, grads):
mp.data = copy.deepcopy(p.data)
mp.grad.data = copy.deepcopy(... | 4,243 | 35.273504 | 148 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/flat_meas/fim.py | from tqdm import tqdm
import torch
def fim(model_func):
r'''
Fisher information matrix
Inputs:
func : model functional class
Outputs:
frobenius norm
'''
out = 0.0
theta_star = torch.cat([p.view(-1) for p in model_func.model.parameters()])
return (theta_st... | 618 | 21.925926 | 79 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/flat_meas/local_entropy.py | import torch
import numpy as np
import copy
from tqdm import tqdm
from torch.optim.optimizer import Optimizer, required
from copy import deepcopy
from scipy.integrate import quad
class EntropySGD(Optimizer):
def __init__(self, params, config={}):
defaults = dict(lr=0.01, momentum=0, damp=0,
... | 7,474 | 31.929515 | 141 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/flat_meas/lanczos.py | import numpy as np
import sys
sys.path.append('../')
from utils.spectral_utils import tridiag_to_eigv
from tqdm import tqdm
from scipy.sparse.linalg import LinearOperator as ScipyLinearOperator
from scipy.sparse.linalg import eigsh
from warnings import warn
import torch
import time
import logging
def lanczos(func, di... | 7,905 | 28.173432 | 95 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/flat_meas/shannon_entropy.py | # Shannon Entropy
import torch
def shannon_entropy(model_func):
res = 0.0
for inputs, targets in model_func.dataloader['train']:
if model_func.use_cuda:
inputs = inputs.cuda()
targets = targets.cuda()
with torch.no_grad():
outputs = model_func.model(inputs)
res += torch.sum(torch.nn.Softmax(1)(out... | 429 | 24.294118 | 83 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/flat_meas/pac_bayes.py | import torch
import numpy as np
import copy
from utils.train_utils import AverageMeter
from utils import vector_to_parameter_tuple
import time
import logging
def load_weights(model, params):
for mp, p in zip(model.parameters(), params):
mp.data = copy.deepcopy(p.data)
def pac_bayes(model_func, mcmc_itr,... | 3,282 | 33.557895 | 152 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/flat_meas/fro_norm.py | from tqdm import tqdm
import torch
def fro_norm(model_func, mcmc_itr):
r'''
Frobenium norm approximation based on [put paper here]
Inputs:
func : model functional class
mcmc_itr : mcmc_itr
Outputs:
frobenius norm
'''
out = 0.0
for i in tqdm(range... | 1,042 | 27.972222 | 122 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/utils/spectral_utils.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
from tqdm import tqdm
import math
import numpy as np
def get_hessian(batch_loss, model):
grads = torch.autograd.grad(batch_loss, model.parameters(), create_graph=True)
grads = torch.cat([x.vie... | 9,806 | 38.071713 | 108 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/utils/train_utils.py | import torch
from torchvision import transforms
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR10, CIFAR100, ImageNet, MNIST, ImageFolder
def get_loader(args, training, augment=False):
""" function to get data loader specific to different datasets
"""
if args.dtype == 'cifar... | 7,499 | 32.185841 | 80 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/utils/sam.py | import torch
class SAM(torch.optim.Optimizer):
def __init__(self, params, base_optimizer, rho=0.05, **kwargs):
assert rho >= 0.0, f"Invalid rho, should be non-negative: {rho}"
defaults = dict(rho=rho, **kwargs)
super(SAM, self).__init__(params, defaults)
self.base_optimizer = bas... | 1,829 | 34.192308 | 175 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/utils/entropy_sgd.py | from torch.optim.optimizer import Optimizer, required
import copy
from copy import deepcopy
import torch
import numpy as np
class EntropySGD(Optimizer):
def __init__(self, params, config = {}):
defaults = dict(lr=0.01, num_batches=0, gtime=1,
momentum=0, momentum_sgld=0, damp=0,
... | 5,636 | 33.796296 | 129 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/utils/data_loader_cifar.py | from __future__ import print_function
from PIL import Image
import os
import os.path
import numpy as np
import sys
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
import torch.utils.data as data
from torchvision.datasets.utils import download_url, check_integrity
import torch
from tor... | 9,561 | 37.401606 | 120 | py |
LPF-SGD | LPF-SGD-master/codes/resnets_nodataaug/utils/data_loader_mnist.py | from __future__ import print_function
from torchvision.datasets.vision import VisionDataset
import warnings
from PIL import Image
import os
import os.path
import numpy as np
import torch
import codecs
from sklearn.model_selection import train_test_split
from torchvision.datasets.utils import download_url, download_and_... | 9,832 | 36.10566 | 118 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/_lpfadamtrain.py | import yaml
import os
import re
import argparse
import time
import torch
import utils
import numpy as np
import youtokentome as yttm
from tqdm import tqdm
import argparse
from models import *
import _data as data
from utils import *
import glob
import shutil
import logging
from torch.utils.tensorboard import SummaryWri... | 7,492 | 29.835391 | 124 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/_data.py | import torch
import os
import youtokentome as yttm
import torch.nn.utils.rnn as rnn_utils
from torch.utils.data import Dataset, DataLoader
UNK_IDX, PAD_IDX, BOS_IDX, EOS_IDX = 0, 1, 2, 3
class Translation(Dataset):
def __init__(self, directory, split, bpe):
self.bpe = bpe
with open(os.path.join(directory, '%s.d... | 3,165 | 28.045872 | 113 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/utils.py | from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import Transformer
import math
import random
from torchtext.data.metrics import bleu_score
from tqdm import tqdm
UNK_IDX, PAD_IDX, BOS_IDX, EOS_IDX = 0, 1, 2, 3
SRC_LANGUAGE, TGT_LANGUAGE = 'de', 'en'
class AverageMeter(object):
"""Computes ... | 3,560 | 28.675 | 95 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/_train.py | import yaml
import os
import re
import argparse
import time
import torch
import utils
import numpy as np
import youtokentome as yttm
from tqdm import tqdm
import argparse
from models import *
import _data as data
from utils import *
import glob
import shutil
import logging
from torch.utils.tensorboard import SummaryWri... | 5,687 | 30.425414 | 127 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/data.py | from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from torchtext.datasets import Multi30k, IWSLT2016, IWSLT2017
from torchtext.experimental.datasets.raw import WMT14
from typing import Iterable, List
from torch.utils.data import DataLoader
import torch
from torch.nn.ut... | 2,789 | 32.214286 | 125 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/_adamtrain.py | import yaml
import os
import re
import argparse
import time
import torch
import utils
import numpy as np
import youtokentome as yttm
from tqdm import tqdm
import argparse
from models import *
import _data as data
from utils import *
import glob
import shutil
import logging
from torch.utils.tensorboard import SummaryWri... | 6,182 | 29.014563 | 123 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/_smoothouttrain.py | import yaml
import os
import re
import argparse
import time
import torch
import utils
import numpy as np
import youtokentome as yttm
from tqdm import tqdm
import argparse
from models import *
import _data as data
from utils import *
import glob
import shutil
import logging
from torch.utils.tensorboard import SummaryWri... | 7,000 | 29.572052 | 123 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/_samtrain.py | import yaml
import os
import re
import argparse
import time
import torch
import utils
import numpy as np
import youtokentome as yttm
from tqdm import tqdm
import argparse
from models import *
import _data as data
from utils import *
import glob
import shutil
import logging
from torch.utils.tensorboard import SummaryWri... | 6,565 | 29.682243 | 131 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/preprocess.py | from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
from torchtext.datasets import Multi30k, IWSLT2016, IWSLT2017
from torchtext.experimental.datasets.raw import WMT14
from typing import Iterable, List
from torch.utils.data import DataLoader
import torch
from torch.nn.ut... | 2,001 | 31.819672 | 102 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/_test.py | import yaml
import os
import re
import argparse
import time
import torch
import utils
import numpy as np
import youtokentome as yttm
from tqdm import tqdm
import argparse
from models import *
import _data as data
from utils import *
import glob
import shutil
import logging
from torch.utils.tensorboard import SummaryWri... | 2,033 | 21.6 | 94 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/_esgdtrain.py | import yaml
import os
import re
import argparse
import time
import torch
import utils
import numpy as np
import youtokentome as yttm
from tqdm import tqdm
import argparse
from models import *
import _data as data
from utils import *
import glob
import shutil
import logging
from torch.utils.tensorboard import SummaryWri... | 7,126 | 28.089796 | 123 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/models/seq2seq.py | from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import Transformer
import math
# helper Module that adds positional encoding to the token embedding to introduce a notion of word order.
class PositionalEncoding(nn.Module):
def __init__(self,
emb_size: int,
... | 3,608 | 40.482759 | 105 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/utils/lr_scheduler.py | # MIT License
#
# Copyright (c) 2021 Soohwan Kim
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, ... | 4,146 | 33.272727 | 89 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/utils/esgd.py | from torch.optim.optimizer import Optimizer, required
import copy
from copy import deepcopy
import torch
import numpy as np
import torch.optim._functional as F
class EntropyAdam(Optimizer):
def __init__(self, params, config = {}):
defaults = dict(lr=0.01, gtime=1,betas=(0.9, 0.999),eps=1e-8,
... | 7,831 | 33.808889 | 129 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/utils/train_utils.py | from torch import Tensor
import torch
import torch.nn as nn
from torch.nn import Transformer
import math
import random
from torchtext.data.metrics import bleu_score
UNK_IDX, PAD_IDX, BOS_IDX, EOS_IDX = 0, 1, 2, 3
SRC_LANGUAGE, TGT_LANGUAGE = 'de', 'en'
class AverageMeter(object):
"""Computes and stores the average ... | 4,336 | 25.937888 | 106 | py |
LPF-SGD | LPF-SGD-master/codes/machine_translation/utils/sam.py | import torch
class SAM(torch.optim.Optimizer):
def __init__(self, params, base_optimizer, rho=0.05, **kwargs):
assert rho >= 0.0, f"Invalid rho, should be non-negative: {rho}"
defaults = dict(rho=rho, **kwargs)
super(SAM, self).__init__(params, defaults)
self.base_optimizer = bas... | 2,128 | 33.901639 | 131 | py |
turkish-bert | turkish-bert-master/train_flert_model.py | from argparse import ArgumentParser
import torch, flair
# dataset, model and embedding imports
from flair.datasets import UniversalDependenciesCorpus, XTREME
from flair.embeddings import TransformerWordEmbeddings
from flair.models import SequenceTagger
from flair.trainers import ModelTrainer
if __name__ == "__main__"... | 3,259 | 34.053763 | 113 | py |
SRW | SRW-master/docs/source/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# srwpy documentation build configuration file, created by
# sphinx-quickstart on Thu Jun 28 12:35:56 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
# auto... | 7,151 | 30.095652 | 135 | py |
s2am | s2am-master/main.py | from __future__ import print_function, absolute_import
import argparse
import torch
from scripts.utils.misc import save_checkpoint, adjust_learning_rate
import scripts.utils.pytorch_ssim as pytorch_ssim
import scripts.datasets as datasets
import scripts.machines as machines
from options import Options
def main(args)... | 1,476 | 29.142857 | 114 | py |
s2am | s2am-master/scripts/models/backbone_unet.py |
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import functools
import math
from scripts.utils.model_init import *
from scripts.models.vgg import Vgg16
from scripts.models.rasc import *
from scripts.models.urasc import *
from scripts.models.unet import oUnetG... | 3,637 | 28.819672 | 119 | py |
s2am | s2am-master/scripts/models/unetseg.py | import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import functools
import math
import numbers
from scripts.utils.model_init import *
from scripts.models.vgg import Vgg16
from scripts.models.blocks import *
def calc_mean_std(feat, eps=1e-5):
# eps is a small ... | 21,209 | 40.916996 | 230 | py |
s2am | s2am-master/scripts/models/discriminator.py |
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import functools
import math
from scripts.utils.model_init import *
from scripts.models.unet import oUnetGenerator,UnetGenerator
# radhn with unet
__all__ = ['patchgan']
class Discriminator(nn.Module):
d... | 1,468 | 28.979592 | 81 | py |
s2am | s2am-master/scripts/models/dhn.py |
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
from scripts.utils.model_init import *
from scripts.models.vgg import Vgg16
__all__ = ['dhn','dhn256','dih256','dih256seg']
class DHN(nn.Module):
def __init__(self,):
super(DHN, self).... | 11,585 | 31.183333 | 79 | py |
s2am | s2am-master/scripts/models/vgg.py | from collections import namedtuple
import torch
from torchvision import models
class Vgg16(torch.nn.Module):
def __init__(self, requires_grad=False):
super(Vgg16, self).__init__()
vgg_pretrained_features = models.vgg16(pretrained=True).features
self.slice1 = torch.nn.Sequential()
... | 1,659 | 36.727273 | 104 | py |
s2am | s2am-master/scripts/models/urasc.py |
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from scripts.utils.model_init import *
from scripts.models.vgg import Vgg16
from scripts.models.blocks import *
class UNO(nn.Module):
def __init__(self,channel):
super(UNO, self).__init__()
def... | 2,181 | 32.569231 | 80 | py |
s2am | s2am-master/scripts/models/unet.py | import torch
import torch.nn as nn
from torch.nn import init
import functools
from scripts.models.blocks import *
from scripts.models.rasc import *
class MaskedMinimalUnetV2(nn.Module):
"""docstring for MinimalUnet"""
def __init__(self, down=None,up=None,submodule=None,attention=None,withoutskip=False,outermo... | 22,177 | 39.693578 | 252 | py |
s2am | s2am-master/scripts/models/can.py |
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
from scripts.utils.model_init import *
from scripts.models.blocks import *
__all__ = ['can','racan','msdgf']
class AdaptiveBatchNorm2d(nn.Module):
def __init__(self, num_features, eps=1e-5, mom... | 6,884 | 32.585366 | 156 | py |
s2am | s2am-master/scripts/models/radhn.py |
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import numpy
Filter
from scripts.utils.model_init import *
from scripts.models.vgg import Vgg16
from scripts.models.blocks import *
__all__ = ['rascseg','rasc','dihrasc']
class DIHRASC256(nn.Module):
def __init__(self,):
... | 9,909 | 30.762821 | 79 | py |
s2am | s2am-master/scripts/models/rasc.py |
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from scripts.utils.model_init import *
from scripts.models.vgg import Vgg16
from scripts.models.blocks import *
class PCBlock(nn.Module):
"""docstring for RegionalSkipConnect"""
def __init__(self, chann... | 8,201 | 39.205882 | 125 | py |
s2am | s2am-master/scripts/models/blocks.py | import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import functools
import math
import numbers
from scripts.utils.model_init import *
from scripts.models.vgg import Vgg16
from torch import nn, cuda
from torch.autograd import Variable
class _NonLocalBlockND(nn.Mod... | 30,348 | 38.059202 | 161 | py |
s2am | s2am-master/scripts/datasets/COCO.py | from __future__ import print_function, absolute_import
import os
import csv
import numpy as np
import json
import random
import math
from collections import namedtuple
from os import listdir
from os.path import isfile, join
import torch
import torch.utils.data as data
from scripts.utils.osutils import *
from scripts... | 2,637 | 27.989011 | 107 | py |
s2am | s2am-master/scripts/machines/BasicGAN.py | import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from progress.bar import Bar
import json
from tensorboardX import SummaryWriter
from scripts.utils.evaluation import accuracy, AverageMeter, final_preds
from scripts.utils.osutils import mkdir_p, isfile, isdir, join
import scripts.utils.pytorch_ssi... | 8,590 | 36.845815 | 193 | py |
s2am | s2am-master/scripts/machines/BasicMachine.py | import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from progress.bar import Bar
import json
from tensorboardX import SummaryWriter
from scripts.utils.evaluation import accuracy, AverageMeter, final_preds
from scripts.utils.osutils import mkdir_p, isfile, isdir, join
from scripts.utils.imutils impor... | 8,700 | 35.103734 | 191 | py |
s2am | s2am-master/scripts/machines/__init__.py | import torch
import torch.nn as nn
from .BasicMachine import BasicMachine
from .MMaskedRASCGAN import MMaskedRASCGAN
from .BasicGAN import BasicGAN
__all__ = ['basic','basicgan','mmaskedgan','maskedganplus']
def basic(**kwargs):
return BasicMachine(**kwargs)
def mmaskedgan(**kwargs):
return MMaskedRASCGAN(pixello... | 483 | 22.047619 | 59 | py |
s2am | s2am-master/scripts/machines/MMaskedRASCGANplus.py | import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from progress.bar import Bar
import json
from tensorboardX import SummaryWriter
from scripts.utils.evaluation import accuracy, AverageMeter, final_preds
from scripts.utils.osutils import mkdir_p, isfile, isdir, join
import scripts.utils.pytorch_ssi... | 11,546 | 40.092527 | 193 | py |
s2am | s2am-master/scripts/machines/MMaskedRASCGAN.py | import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from progress.bar import Bar
import json
from tensorboardX import SummaryWriter
from scripts.utils.evaluation import accuracy, AverageMeter, final_preds
from scripts.utils.osutils import mkdir_p, isfile, isdir, join
import scripts.utils.pytorch_ssi... | 11,541 | 40.221429 | 193 | py |
s2am | s2am-master/scripts/utils/misc.py | from __future__ import absolute_import
import os
import shutil
import torch
import math
import numpy as np
import scipy.io
def to_numpy(tensor):
if torch.is_tensor(tensor):
return tensor.cpu().numpy()
elif type(tensor).__module__ != 'numpy':
raise ValueError("Cannot convert {} to numpy array"... | 2,205 | 28.810811 | 117 | py |
s2am | s2am-master/scripts/utils/pytorch_modelsize.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
class SizeEstimator(object):
def __init__(self, model, input_size=(1,1,32,32), bits=32):
'''
Estimates the size of PyTorch models in memory
for a given input size
'''
self.model = mode... | 2,601 | 30.731707 | 78 | py |
s2am | s2am-master/scripts/utils/imutils.py | from __future__ import absolute_import
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import scipy.misc
from .misc import *
def im_to_numpy(img):
img = to_numpy(img)
img = np.transpose(img, (1, 2, 0)) # H*W*C
return img
def im_to_torch(img):
img = np.transpose(i... | 7,575 | 30.04918 | 108 | py |
s2am | s2am-master/scripts/utils/model_init.py |
from torch.nn import init
def weights_init_normal(m):
classname = m.__class__.__name__
# print(classname)
if classname.find('Conv') != -1:
init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('Linear') != -1:
init.normal_(m.weight.data, 0.0, 0.02)
elif classname.find('Batch... | 1,752 | 34.06 | 80 | py |
s2am | s2am-master/scripts/utils/__init__.py | from __future__ import absolute_import
from .evaluation import *
from .imutils import *
from .misc import *
from .osutils import *
from .transforms import *
from .pytorch_modelsize import *
| 191 | 20.333333 | 38 | py |
s2am | s2am-master/scripts/utils/evaluation.py | from __future__ import absolute_import
import math
import numpy as np
from random import randint
from .misc import *
from .transforms import transform, transform_preds
__all__ = ['accuracy', 'AverageMeter']
def get_preds(scores):
''' get predictions from score maps in torch Tensor
return type: torch.Lon... | 3,565 | 29.478632 | 109 | py |
s2am | s2am-master/scripts/utils/transforms.py | from __future__ import absolute_import
import os
import numpy as np
import scipy.misc
import torch
import torchvision
from .misc import *
from .imutils import *
def color_normalize(x, mean, std):
if x.size(0) == 1:
x = x.repeat(3, x.size(1), x.size(2))
for t, m, s in zip(x, mean, std):
t.su... | 5,193 | 26.336842 | 93 | py |
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