repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
GBST | GBST-master/gbst_src/demo/rank/rank_sklearn.py | #!/usr/bin/python
import xgboost as xgb
from sklearn.datasets import load_svmlight_file
# This script demonstrate how to do ranking with XGBRanker
x_train, y_train = load_svmlight_file("mq2008.train")
x_valid, y_valid = load_svmlight_file("mq2008.vali")
x_test, y_test = load_svmlight_file("mq2008.test")
group_train... | 1,121 | 30.166667 | 66 | py |
GBST | GBST-master/gbst_src/demo/kaggle-higgs/higgs-pred.py | #!/usr/bin/python
# make prediction
import numpy as np
import xgboost as xgb
# path to where the data lies
dpath = 'data'
modelfile = 'higgs.model'
outfile = 'higgs.pred.csv'
# make top 15% as positive
threshold_ratio = 0.15
# load in training data, directly use numpy
dtest = np.loadtxt( dpath+'/test.csv', delimiter... | 1,166 | 21.882353 | 66 | py |
GBST | GBST-master/gbst_src/demo/kaggle-higgs/speedtest.py | #!/usr/bin/python
# this is the example script to use xgboost to train
import numpy as np
import xgboost as xgb
from sklearn.ensemble import GradientBoostingClassifier
import time
test_size = 550000
# path to where the data lies
dpath = 'data'
# load in training data, directly use numpy
dtrain = np.loadtxt( dpath+'/t... | 2,068 | 31.328125 | 111 | py |
GBST | GBST-master/gbst_src/demo/kaggle-higgs/higgs-cv.py | #!/usr/bin/python
import numpy as np
import xgboost as xgb
### load data in do training
train = np.loadtxt('./data/training.csv', delimiter=',', skiprows=1, converters={32: lambda x:int(x=='s'.encode('utf-8')) } )
label = train[:,32]
data = train[:,1:31]
weight = train[:,31]
dtrain = xgb.DMatrix( data, label=label,... | 1,447 | 37.105263 | 125 | py |
GBST | GBST-master/gbst_src/demo/kaggle-higgs/higgs-numpy.py | #!/usr/bin/python
# this is the example script to use xgboost to train
import numpy as np
import xgboost as xgb
test_size = 550000
# path to where the data lies
dpath = 'data'
# load in training data, directly use numpy
dtrain = np.loadtxt( dpath+'/training.csv', delimiter=',', skiprows=1, converters={32: lambda x:... | 1,734 | 30.545455 | 127 | py |
GBST | GBST-master/gbst_src/dev/query_contributors.py | """Query list of all contributors and reviewers in a release"""
from sh.contrib import git
import sys
import re
import requests
import json
if len(sys.argv) != 5:
print(f'Usage: {sys.argv[0]} [starting commit/tag] [ending commit/tag] [GitHub username] [GitHub password]')
sys.exit(1)
from_commit = sys.argv[1]... | 2,614 | 39.859375 | 149 | py |
GBST | GBST-master/gbst_src/dmlc-core/tracker/dmlc_tracker/opts.py | # pylint: disable=invalid-name
"""Command line options of job submission script."""
import os
import argparse
def get_cache_file_set(args):
"""Get the list of files to be cached.
Parameters
----------
args: ArgumentParser.Argument
The arguments returned by the parser.
Returns
-------
... | 9,424 | 51.071823 | 110 | py |
GBST | GBST-master/gbst_src/dmlc-core/doc/conf.py | # -*- coding: utf-8 -*-
#
# documentation build configuration file, created by
# sphinx-quickstart on Thu Jul 23 19:40:08 2015.
#
# 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 file.
#
# All confi... | 5,609 | 32.795181 | 88 | py |
GBST | GBST-master/gbst_package/setup.py | # pylint: disable=invalid-name, exec-used
"""Setup xgboost package."""
from __future__ import absolute_import
import io
import sys
import os
from setuptools import setup, find_packages
# import subprocess
sys.path.insert(0, '.')
CURRENT_DIR = os.path.dirname(__file__)
# We can not import `xgboost.libpath` in setup.p... | 2,468 | 36.409091 | 97 | py |
GBST | GBST-master/gbst_package/gbst/libpath.py | # coding: utf-8
"""Find the path to gbst dynamic library files."""
import os
import platform
import sys
class GBSTLibraryNotFound(Exception):
"""Error thrown by when xgboost is not found"""
def find_lib_path():
"""Find the path to xgboost dynamic library files.
Returns
-------
lib_path: list(s... | 2,108 | 40.352941 | 86 | py |
GBST | GBST-master/gbst_package/gbst/core.py | # coding: utf-8
# pylint: disable=too-many-arguments, too-many-branches, invalid-name
# pylint: disable=too-many-branches, too-many-lines, too-many-locals
# pylint: disable=too-many-public-methods
"""Core GBST Library."""
import collections
# pylint: disable=no-name-in-module,import-error
from collections.abc import Ma... | 69,341 | 36.02189 | 112 | py |
GBST | GBST-master/gbst_package/gbst/sklearn.py | # coding: utf-8
# pylint: disable=too-many-arguments, too-many-locals, invalid-name, fixme, E0012, R0912, C0302
"""Scikit-Learn Wrapper interface for GBST."""
import warnings
import json
import numpy as np
from .core import Booster, DMatrix, gbstError
from .training import train
from sklearn.metrics import roc_auc_scor... | 25,465 | 40.611111 | 99 | py |
universal-distillation | universal-distillation-master/setup.py | #!/usr/bin/env python
from setuptools import setup, find_packages
setup(
name='universal-distillation',
version='0.0.0',
description='Describe Your Cool Project',
author='',
author_email='',
# REPLACE WITH YOUR OWN GITHUB PROJECT LINK
url='https://github.com/PyTorchLightning/pytorch-lightn... | 610 | 26.772727 | 80 | py |
universal-distillation | universal-distillation-master/tests/test_classifier.py | from pytorch_lightning import Trainer, seed_everything
from universal_distillation.data.jit import JITDataModule
from universal_distillation.modules.base import BaseTransformer
from transformers import AutoTokenizer
import tempfile
import os
import pytorch_lightning as pl
SAMPLE = """Mr President, concerning the Minut... | 1,765 | 44.282051 | 214 | py |
universal-distillation | universal-distillation-master/universal_distillation/evaluation.py | from argparse import ArgumentParser
import pytorch_lightning as pl
from pytorch_lightning.loggers import TensorBoardLogger
import torch
from torch.nn import functional as F
from torch.utils.data import (
DataLoader,
random_split,
RandomSampler,
BatchSampler,
DistributedSampler,
)
import torch.nn as ... | 2,740 | 26.969388 | 104 | py |
universal-distillation | universal-distillation-master/universal_distillation/distillation.py | from argparse import ArgumentParser
import pytorch_lightning as pl
from pytorch_lightning.loggers import TensorBoardLogger
import torch
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from torch.nn import functional as F
from torch.utils.data import (
DataLoader,
random_split,
RandomSam... | 3,745 | 28.496063 | 148 | py |
universal-distillation | universal-distillation-master/universal_distillation/modules/base.py | from argparse import ArgumentParser
from math import exp
import pytorch_lightning as pl
from pytorch_lightning.loggers import TensorBoardLogger
import torch
from torch.nn import functional as F
from torch.utils.data import (
DataLoader,
random_split,
RandomSampler,
BatchSampler,
DistributedSampler,
... | 11,285 | 36.370861 | 186 | py |
universal-distillation | universal-distillation-master/universal_distillation/data/jit/dataloader.py | from torch.types import Number
from transformers import PreTrainedTokenizerBase
import torch
from torch.utils.data import Dataset
import logging
from transformers.tokenization_utils_base import BatchEncoding
import math
from typing import Collection, Optional
import itertools
import numpy as np
logger = logging.getLog... | 9,719 | 34.735294 | 144 | py |
universal-distillation | universal-distillation-master/universal_distillation/data/jit/datamodule.py | from pytorch_lightning import LightningDataModule
from torch.utils.data import DataLoader
from transformers import PreTrainedTokenizerBase
from typing import Optional
from .dataloader import JITTokenizedDataset
class JITDataModule(LightningDataModule):
"""Data module that uses the tokenizer directly on a file.""... | 1,826 | 28 | 70 | py |
LensIt | LensIt-master/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 ------------------------------------------------------------... | 6,121 | 29.157635 | 79 | py |
Continual-Learning-with-Invertible-Generative-Models | Continual-Learning-with-Invertible-Generative-Models-master/main.py | import argparse
import itertools
import logging
import numpy as np
import yaml
import os.path
import torch
from torch import optim
import dill as pickle
from tqdm import tqdm
from base import get_dataset, get_predictions, Encoder
from continual_ai.cl_settings import MultiHeadTaskSolver, SingleIncrementalTaskSolver, M... | 14,844 | 36.205514 | 120 | py |
Continual-Learning-with-Invertible-Generative-Models | Continual-Learning-with-Invertible-Generative-Models-master/base.py | from functools import reduce
from operator import mul
import numpy as np
import torch
from torch import nn
from continual_ai.cl_settings.base import ClassificationTask
from continual_ai.datasets import MNIST, SVHN, CIFAR10, CIFAR100
def get_dataset(dataset_name, device):
transformer = lambda x: (torch.tensor(n... | 7,951 | 31.457143 | 120 | py |
Continual-Learning-with-Invertible-Generative-Models | Continual-Learning-with-Invertible-Generative-Models-master/continual_ai/datasets.py | import codecs
import gzip
import pickle
import tarfile
from os.path import join
from typing import Callable
from urllib.request import urlretrieve
import numpy as np
import torch
from torch.utils.data import DataLoader
# from classification.base1 import AbstractBaseDataset
# from base import AbstractBaseDataset
import ... | 12,718 | 36.630178 | 121 | py |
Continual-Learning-with-Invertible-Generative-Models | Continual-Learning-with-Invertible-Generative-Models-master/continual_ai/cl_strategies/base.py | import torch
class Container(object):
def __init__(self):
self.encoder = None
self.solver = None
self.other_models = torch.nn.ModuleDict()
self.optimizer = None
self.current_loss = None
self.current_task = None
self.current_batch = None
self.curre... | 1,227 | 23.56 | 82 | py |
Continual-Learning-with-Invertible-Generative-Models | Continual-Learning-with-Invertible-Generative-Models-master/continual_ai/cl_strategies/multi_task/ewc/EWC.py | import logging
import itertools
from copy import deepcopy
import torch
from continual_ai.cl_strategies import NaiveMethod, Container
from continual_ai.utils import ExperimentConfig
class ElasticWeightConsolidation(NaiveMethod):
"""
@article{kirkpatrick2017overcoming,
title={Overcoming catastrophic for... | 5,832 | 33.111111 | 116 | py |
Continual-Learning-with-Invertible-Generative-Models | Continual-Learning-with-Invertible-Generative-Models-master/continual_ai/cl_strategies/multi_task/lwf/LWF.py | from collections import defaultdict
import numpy as np
import torch
from typing import Union
import logging
from continual_ai.cl_strategies import NaiveMethod, Container
from .utils import KnowledgeDistillationLoss
from continual_ai.utils import ExperimentConfig
class LearningWithoutForgetting(NaiveMethod):
""... | 2,978 | 31.380435 | 104 | py |
Continual-Learning-with-Invertible-Generative-Models | Continual-Learning-with-Invertible-Generative-Models-master/continual_ai/cl_strategies/multi_task/lwf/__LWF.py | import numpy as np
import torch
from typing import Union
import logging
from continual_ai.cl_strategies import NaiveMethod, Container
from .utils import KnowledgeDistillationLoss
from continual_ai.utils import ExperimentConfig
class LearningWithoutForgetting(NaiveMethod):
#TODO: citazione
"""
"""
d... | 2,821 | 31.436782 | 104 | py |
Continual-Learning-with-Invertible-Generative-Models | Continual-Learning-with-Invertible-Generative-Models-master/continual_ai/cl_strategies/multi_task/lwf/utils.py | import torch.nn.functional as F
class KnowledgeDistillationLoss:
def __init__(self, temperature):
self.t = temperature
def __call__(self, prediction, target):
soft_log_probs = F.log_softmax(prediction / self.t, dim=-1)
soft_targets = F.softmax(target / self.t, dim=-1)
distill... | 490 | 27.882353 | 89 | py |
Continual-Learning-with-Invertible-Generative-Models | Continual-Learning-with-Invertible-Generative-Models-master/continual_ai/cl_strategies/multi_task/gem/GEM.py | from typing import Union
import logging
import itertools
from copy import deepcopy
import numpy as np
import torch
from torch import nn
from continual_ai.cl_strategies import NaiveMethod, Container
from .utils import qp
from continual_ai.utils import ExperimentConfig
class GradientEpisodicMemory(NaiveMethod):
... | 13,561 | 35.953678 | 121 | py |
Continual-Learning-with-Invertible-Generative-Models | Continual-Learning-with-Invertible-Generative-Models-master/continual_ai/cl_strategies/multi_task/prer/utils.py | import numpy as np
from torch import nn
from torch.distributions import Categorical
from typing import Union
import torch
class SequentialFlow(torch.nn.Sequential):
def forward(self, x, y=None):
log_det = 0
for module in self:
x, _log_det = module(x, y=y)
log_det = log_det ... | 5,177 | 27.607735 | 95 | py |
Continual-Learning-with-Invertible-Generative-Models | Continual-Learning-with-Invertible-Generative-Models-master/continual_ai/cl_strategies/multi_task/prer/PRER.py | import itertools
from copy import deepcopy
import numpy as np
import os
import logging
import torch
import torchvision
from matplotlib import pyplot as plt
from torch import optim, autograd
from torch.distributions import Categorical
from torch.optim import Adam
from tqdm import tqdm
from base import Encoder, Decod... | 32,682 | 37.495878 | 114 | py |
Continual-Learning-with-Invertible-Generative-Models | Continual-Learning-with-Invertible-Generative-Models-master/continual_ai/cl_strategies/multi_task/prer/rnvp.py | from functools import reduce
from operator import mul
from typing import Callable
import torch
from continual_ai.cl_strategies.multi_task.prer.utils import SequentialFlow, \
EmbeddingPreprocessing
class SplitGaussianize(torch.nn.Module):
def __init__(self, input_dim, conditioning_size=0):
super().__i... | 14,556 | 29.51782 | 88 | py |
Continual-Learning-with-Invertible-Generative-Models | Continual-Learning-with-Invertible-Generative-Models-master/continual_ai/cl_strategies/multi_task/er/ER.py | import logging
import numpy as np
from typing import Union
import torch
import torch.nn.functional as F
# from continual_ai.continual_learning_strategies.base import NaiveMethod, Container
# from continual_ai.base import ExperimentConfig
# from continual_ai.utils import Sampler
from continual_ai.cl_strategies import... | 7,319 | 35.41791 | 120 | py |
Continual-Learning-with-Invertible-Generative-Models | Continual-Learning-with-Invertible-Generative-Models-master/continual_ai/cl_settings/base.py | from abc import ABC, abstractmethod
from typing import Union
import numpy as np
from torch.utils.data import BatchSampler, RandomSampler
from continual_ai.base import ClassificationDataset
class ClassificationTask(ClassificationDataset):
def __init__(self, base_dataset: ClassificationDataset, task_labels: np.ndar... | 6,109 | 32.571429 | 120 | py |
Continual-Learning-with-Invertible-Generative-Models | Continual-Learning-with-Invertible-Generative-Models-master/continual_ai/cl_settings/solvers.py | __all__ = ['MultiHeadTaskSolver', 'SingleIncrementalTaskSolver']
import itertools
from abc import ABC, abstractmethod
from operator import mul
from functools import reduce
import numpy as np
import torch
from torch import nn
class Solver(ABC):
@property
@abstractmethod
def current_task(self):
... | 6,514 | 28.346847 | 93 | py |
essl | essl-main/imagenet/barlowtwins/main.py | from pathlib import Path
import argparse
import json
import math
import os
import random
import signal
import subprocess
import sys
import time
from PIL import Image, ImageOps, ImageFilter
from torch import nn, optim
import torch
import torchvision
import torchvision.transforms as transforms
parser = argparse.Argumen... | 15,754 | 38.289277 | 123 | py |
essl | essl-main/imagenet/barlowtwins/linear_probe.py | from pathlib import Path
import argparse
import os
import sys
import signal
import time
import json
import urllib
import random
from torch import nn, optim
from torchvision import models, datasets, transforms
import torch
import torchvision
parser = argparse.ArgumentParser(description='Evaluate resnet50 features on I... | 8,629 | 38.227273 | 178 | py |
essl | essl-main/imagenet/simsiam/main.py | import argparse
import builtins
import math
import os
import random
import shutil
import time
import warnings
from PIL import Image, ImageOps, ImageFilter
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.mu... | 17,670 | 38.621076 | 123 | py |
essl | essl-main/imagenet/simsiam/linear_probe.py | import argparse
import builtins
import math
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
i... | 19,762 | 37.75098 | 94 | py |
essl | essl-main/imagenet/simsiam/simsiam/builder.py | import torch
import torch.nn as nn
class SimSiam(nn.Module):
"""
Build a SimSiam model.
"""
def __init__(self, base_encoder, dim=2048, pred_dim=512, rotation=False):
"""
dim: feature dimension (default: 2048)
pred_dim: hidden dimension of the predictor (default: 512)
""... | 3,212 | 40.727273 | 97 | py |
essl | essl-main/imagenet/simclr/main.py | from pathlib import Path
import argparse
import os
import sys
import random
import subprocess
import time
import json
import math
import numpy as np
from PIL import Image, ImageOps, ImageFilter
from torch import nn, optim
import torch
import torchvision
import torchvision.transforms as transforms
from utils import ga... | 14,544 | 38.099462 | 123 | py |
essl | essl-main/imagenet/simclr/linear_probe.py | from pathlib import Path
import argparse
import json
import os
import random
import sys
import time
import urllib
from torch import nn, optim
from torchvision import models, datasets, transforms
import torch
import torchvision
parser = argparse.ArgumentParser(description='Evaluate resnet50 features on ImageNet')
pars... | 8,218 | 37.768868 | 117 | py |
essl | essl-main/imagenet/simclr/utils.py | import torch
import torch.distributed as dist
from classy_vision.generic.distributed_util import (
convert_to_distributed_tensor,
convert_to_normal_tensor,
is_distributed_training_run,
)
import random
from PIL import Image, ImageOps, ImageFilter
class GatherLayer(torch.autograd.Function):
"""
Gath... | 2,099 | 27.767123 | 88 | py |
essl | essl-main/cifar10/main.py | import os
import math
import time
import copy
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.cuda.amp import autocast, GradScaler
import torchvision
import torchvision.transforms as T
from resnet import resnet18
from utils import knn_monitor, fix_see... | 16,463 | 33.661053 | 116 | py |
essl | essl-main/cifar10/resnet.py | # modified from https://github.com/pytorch/vision/blob/main/torchvision/models/resnet.py
from typing import Type, Any, Callable, Union, List, Optional
import torch
import torch.nn as nn
from torch import Tensor
__all__ = [
"ResNet",
"resnet18",
"resnet34",
"resnet50",
"resnet101",
"resnet152",... | 15,458 | 38.136709 | 118 | py |
essl | essl-main/cifar10/utils.py | import torch.nn.functional as F
import torch
import random
import numpy as np
def fix_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# modified from
# htt... | 2,926 | 40.225352 | 115 | py |
essl | essl-main/photonics/main.py | import torch
from torch.utils import data
from torch.utils.tensorboard import SummaryWriter
import math
import matplotlib.pyplot as plt
import numpy as np
import argparse
import os
import torch.nn as nn
import torch.nn.functional as F
import json
from utils import Cosine_Scheduler, DOSLoss
from ssl_mode import SimCLR,... | 13,601 | 40.343465 | 136 | py |
essl | essl-main/photonics/phc_models.py |
import torch.nn as nn
import torch
import math
import numpy as np
class Encoder(nn.Module):
def __init__(self, Lv, ks):
super(Encoder, self).__init__()
self.enc_block2d = nn.Sequential(
nn.Conv2d(1, Lv[0], kernel_size=ks,stride=1,padding=math.ceil((ks-1)/2)),
nn.BatchNorm2... | 4,211 | 29.521739 | 89 | py |
essl | essl-main/photonics/symop_utils.py | import numpy as np
import torch
import random
def translate_tensor(tensor, input_size=32, nt=2):
"""
Data augmentation function to enforce periodic boundary conditions.
Inputs are arbitrarily translated in each dimension
"""
ndim = len(tensor[0,0, :].shape)
t = input_size//nt
t_vec = np.li... | 6,808 | 39.772455 | 177 | py |
essl | essl-main/photonics/utils.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Cosine_Scheduler(object):
def __init__(self, optimizer, warmup_epochs=0, warmup_lr=0., num_epochs=800, \
base_lr=1e-2, final_lr=0., iter_per_epoch=100):
self.base_lr = base_lr
warmup_iter = i... | 4,142 | 38.836538 | 112 | py |
essl | essl-main/photonics/phc_data.py | import torch
from torch.utils import data
from torchvision.transforms import transforms
import numpy as np
import h5py
import os
import math
import matplotlib.pyplot as plt
class PhC2D(data.Dataset):
def __init__(self, path_to_h5_dir, trainsize, validsize=0, testsize=2000,
predict = 'DOS', mode = 'ELdiff'... | 4,754 | 36.148438 | 101 | py |
essl | essl-main/photonics/ssl_mode.py | import copy
import random
import torch
from torch import nn
import torch.nn.functional as F
from phc_models import Enc, Proj, Clas # model classes
from utils import NTXentLoss
class SimCLRwEE(nn.Module):
def __init__(self, Lv,Lvpj,ks,device,batchsize_ptxt,temperature,
use_projector = False, nrot=... | 2,663 | 33.597403 | 145 | py |
RSC | RSC-master/ImageNet/main.py | import argparse
import os
import random
import shutil
import time
import warnings
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing as mp
import torch.utils.data
import torch.utils.data.distr... | 16,627 | 37.580046 | 91 | py |
RSC | RSC-master/ImageNet/resnet.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy.random as npr
import numpy as np
import torch.nn.functional as F
import random
import cv2
# from .utils import load_state_dict_from_url
try:
from torch.hub import load_state_dict_from_url
except ImportError:
from torch.utils.mod... | 18,411 | 41.719258 | 118 | py |
RSC | RSC-master/Domain_Generalization/train.py | import argparse
import torch
#from IPython.core.debugger import set_trace
from torch import nn
#from torch.nn import functional as F
from data import data_helper
## from IPython.core.debugger import set_trace
from data.data_helper import available_datasets
from models import model_factory
from optimizer.optimizer_help... | 8,164 | 48.484848 | 154 | py |
RSC | RSC-master/Domain_Generalization/models/model_factory.py | from models import caffenet
from models import mnist
from models import patch_based
from models import alexnet
from models import resnet
nets_map = {
'caffenet': caffenet.caffenet,
'alexnet': alexnet.alexnet,
'resnet18': resnet.resnet18,
'resnet50': resnet.resnet50,
'lenet': mnist.lenet
}
def get... | 529 | 21.083333 | 61 | py |
RSC | RSC-master/Domain_Generalization/models/model_utils.py | from torch.autograd import Function
class GradientKillerLayer(Function):
@staticmethod
def forward(ctx, x, **kwargs):
return x.view_as(x)
@staticmethod
def backward(ctx, grad_output):
return None, None
class ReverseLayerF(Function):
@staticmethod
def forward(ctx, x, lambda_v... | 525 | 20.04 | 51 | py |
RSC | RSC-master/Domain_Generalization/models/resnet.py | from torch import nn
from torch.utils import model_zoo
from torchvision.models.resnet import BasicBlock, model_urls, Bottleneck
import torch
from torch import nn as nn
from torch.autograd import Variable
import numpy.random as npr
import numpy as np
import torch.nn.functional as F
import random
import math
class ResNe... | 8,124 | 45.164773 | 121 | py |
RSC | RSC-master/Domain_Generalization/data/JigsawLoader.py | import numpy as np
import torch
import torch.utils.data as data
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from random import sample, random
def get_random_subset(names, labels, percent):
"""
:param names: list of names
:param labels: list of labels
:param p... | 8,703 | 33.955823 | 137 | py |
RSC | RSC-master/Domain_Generalization/data/concat_dataset.py | import bisect
import warnings
from torch.utils.data import Dataset
# This is a small variant of the ConcatDataset class, which also returns dataset index
from data.JigsawLoader import JigsawTestDatasetMultiple
class ConcatDataset(Dataset):
"""
Dataset to concatenate multiple datasets.
Purpose: useful to... | 1,686 | 29.672727 | 86 | py |
RSC | RSC-master/Domain_Generalization/data/data_helper.py | from os.path import join, dirname
import torch
from torch.utils.data import DataLoader
from torchvision import transforms
from data import StandardDataset
from data.JigsawLoader import JigsawDataset, JigsawTestDataset, get_split_dataset_info, _dataset_info, JigsawTestDatasetMultiple
from data.concat_dataset import Co... | 6,496 | 49.364341 | 158 | py |
RSC | RSC-master/Domain_Generalization/data/StandardDataset.py | from torchvision import datasets
from torchvision import transforms
def get_dataset(path, mode, image_size):
if mode == "train":
img_transform = transforms.Compose([
transforms.RandomResizedCrop(image_size, scale=(0.7, 1.0)),
transforms.RandomHorizontalFlip(),
transform... | 865 | 40.238095 | 145 | py |
RSC | RSC-master/Domain_Generalization/optimizer/optimizer_helper.py | from torch import optim
def get_optim_and_scheduler(network, epochs, lr, train_all, nesterov=False):
if train_all:
params = network.parameters()
else:
params = network.get_params(lr)
optimizer = optim.SGD(params, weight_decay=.0005, momentum=.9, nesterov=nesterov, lr=lr)
#optimizer = o... | 523 | 33.933333 | 92 | py |
lineid_plot | lineid_plot-master/doc/conf.py | # -*- coding: utf-8 -*-
#
# Line Identification Plots documentation build configuration file, created by
# sphinx-quickstart on Sun Sep 18 11:26:57 2011.
#
# 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
# autogener... | 7,377 | 31.9375 | 86 | py |
NTIRE2018 | NTIRE2018-master/Model/CustomCallbacks.py | import numpy as np
import warnings
from keras import callbacks
class ModelCheckpointDetached(callbacks.Callback):
"""Save the model after every epoch.
`filepath` can contain named formatting options,
which will be filled the value of `epoch` and
keys in `logs` (passed in `on_epoch_end`).
For exam... | 4,677 | 41.917431 | 85 | py |
NTIRE2018 | NTIRE2018-master/Model/VisualizeArchitecture.py | import keras
import Model
Model = Model.GetModel(lr=0, gpus=2)
Model.summary()
keras.utils.plot_model(Model,to_file='Architecture.png',show_shapes=True,show_layer_names=False) | 176 | 28.5 | 96 | py |
NTIRE2018 | NTIRE2018-master/Model/Model.py | from keras import models, layers, optimizers
from keras.utils import multi_gpu_model
import tensorflow as tf
import functools
PatternSize = 159
def VGGConv(Input, FilterCount, Tag, Activation=True):
Conv = layers.Conv2D(FilterCount, (3, 3), activation=None, padding='valid', name='Conv' + Tag)(Input)
if Activa... | 8,555 | 54.921569 | 112 | py |
NTIRE2018 | NTIRE2018-master/Model/Train.py | import IO
import PatternManipulator
import Model
import CustomCallbacks
from keras import callbacks
GPU = 2
PatternSize = 159
Margin = PatternManipulator.GetMargin([3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3])
TargetSize = PatternSize - 2 * Margin
batch_size = 3 * GPU
Length = 995124 #2x:934776
G... | 940 | 41.772727 | 126 | py |
NTIRE2018 | NTIRE2018-master/Predict/test.py | import keras
Model = keras.models.load_model('Model_8th.h5')
#trained 300 + 300 iterations
Model.save_weights('w_8th.h5') | 125 | 14.75 | 47 | py |
NTIRE2018 | NTIRE2018-master/Predict/Run.py | from imageio import imread, imwrite
import numpy
import keras
import os
Model = keras.models.load_model('Model4x-003.h5')
def exec(addr):
img = imread("lr_r/"+addr)
Size=159
Margin=(159-95)//2
Step=Size - 2 * Margin
Height = img.shape[0]
Width = img.shape[1]
PatternList = []
for i in r... | 1,302 | 35.194444 | 128 | py |
NTIRE2018 | NTIRE2018-master/Dataset4x/Run.py | from imageio import imread, imwrite
import numpy
import keras
import os
import tensorflow as tf
from keras.utils import multi_gpu_model
with tf.device('/cpu:0'):
Model = keras.models.load_model('Model-002.h5')
Model = multi_gpu_model(Model, gpus=2)
def exec(addr):
img = imread("Y4x_LR/"+addr)
Size=159
... | 1,441 | 35.05 | 128 | py |
dcor | dcor-master/docs/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# dcor documentation build configuration file, created by
# sphinx-quickstart on Thu Sep 14 14:53:09 2017.
#
# 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
# autog... | 8,588 | 27.919192 | 99 | py |
DenseTeacher | DenseTeacher-main/coco-full/augmentations.py | from cvpods.data.transforms import ComposeTransform, ResizeShortestEdge, RandomFlip, NoOpTransform
import torchvision.transforms as transforms
from PIL import ImageFilter
import numpy as np
class GaussianBlur:
def __init__(self, rad_range=[0.1, 2.0]):
self.rad_range = rad_range
def __call__(self, x):... | 2,609 | 30.445783 | 98 | py |
DenseTeacher | DenseTeacher-main/coco-full/dataset.py | import contextlib
import copy
import io
import json
import os
from copy import deepcopy
import numpy as np
import torch
from cvpods.data.build import (SAMPLERS, Infinite, comm, logger,
trivial_batch_collator, worker_init_reset_seed)
from cvpods.data.datasets import COCODataset
# from cvp... | 9,393 | 38.805085 | 121 | py |
DenseTeacher | DenseTeacher-main/coco-full/runner.py | from cvpods.engine.runner import (
RUNNERS, torch, Infinite, hooks, comm, maybe_convert_module,
DistributedDataParallel, auto_scale_config, DefaultCheckpointer
)
import time
from cvpods.modeling.meta_arch.retinanet import permute_to_N_HWA_K
import torch.nn.functional as F
from ema import ModelEMA
from loguru i... | 14,705 | 37.598425 | 115 | py |
DenseTeacher | DenseTeacher-main/coco-full/fcos.py | import math
from typing import List
import torch
import torch.nn.functional as F
from torch import nn
from cvpods.layers import ShapeSpec, cat, generalized_batched_nms
from cvpods.modeling.box_regression import Shift2BoxTransform
from cvpods.modeling.losses import iou_loss, sigmoid_focal_loss_jit
from cvpods.modeling... | 27,042 | 41.721959 | 98 | py |
DenseTeacher | DenseTeacher-main/coco-full/ema.py | #!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
from copy import deepcopy
import torch
import torch.nn as nn
__all__ = ["ModelEMA", "is_parallel"]
def is_parallel(model):
"""check if model is in parallel mode."""
parallel_type = (
nn.parallel.... | 2,018 | 33.810345 | 93 | py |
DenseTeacher | DenseTeacher-main/coco-full-LSJ/augmentations.py | from cvpods.data.transforms import ComposeTransform, ResizeShortestEdge, RandomFlip, NoOpTransform
import torchvision.transforms as transforms
from PIL import ImageFilter
import numpy as np
class GaussianBlur:
def __init__(self, rad_range=[0.1, 2.0]):
self.rad_range = rad_range
def __call__(self, x):... | 2,609 | 30.445783 | 98 | py |
DenseTeacher | DenseTeacher-main/coco-full-LSJ/dataset.py | import contextlib
import copy
import io
import json
import os
from copy import deepcopy
import numpy as np
import torch
from cvpods.data.build import (SAMPLERS, Infinite, comm, logger,
trivial_batch_collator, worker_init_reset_seed)
from cvpods.data.datasets import COCODataset
# from cvp... | 9,393 | 38.805085 | 121 | py |
DenseTeacher | DenseTeacher-main/coco-full-LSJ/runner.py | from cvpods.engine.runner import (
RUNNERS, torch, Infinite, hooks, comm, maybe_convert_module,
DistributedDataParallel, auto_scale_config, DefaultCheckpointer
)
import time
from cvpods.modeling.meta_arch.retinanet import permute_to_N_HWA_K
import torch.nn.functional as F
from ema import ModelEMA
from loguru i... | 14,705 | 37.598425 | 115 | py |
DenseTeacher | DenseTeacher-main/coco-full-LSJ/fcos.py | import math
from typing import List
import torch
import torch.nn.functional as F
from torch import nn
from cvpods.layers import ShapeSpec, cat, generalized_batched_nms
from cvpods.modeling.box_regression import Shift2BoxTransform
from cvpods.modeling.losses import iou_loss, sigmoid_focal_loss_jit
from cvpods.modeling... | 27,042 | 41.721959 | 98 | py |
DenseTeacher | DenseTeacher-main/coco-full-LSJ/ema.py | #!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
from copy import deepcopy
import torch
import torch.nn as nn
__all__ = ["ModelEMA", "is_parallel"]
def is_parallel(model):
"""check if model is in parallel mode."""
parallel_type = (
nn.parallel.... | 2,018 | 33.810345 | 93 | py |
DenseTeacher | DenseTeacher-main/coco-p1/augmentations.py | from cvpods.data.transforms import ComposeTransform, ResizeShortestEdge, RandomFlip, NoOpTransform
import torchvision.transforms as transforms
from PIL import ImageFilter
import numpy as np
class GaussianBlur:
def __init__(self, rad_range=[0.1, 2.0]):
self.rad_range = rad_range
def __call__(self, x):... | 2,609 | 30.445783 | 98 | py |
DenseTeacher | DenseTeacher-main/coco-p1/dataset.py | import contextlib
import copy
import io
import json
import os
from copy import deepcopy
import numpy as np
import torch
from cvpods.data.build import (SAMPLERS, Infinite, comm, logger,
trivial_batch_collator, worker_init_reset_seed)
from cvpods.data.datasets import COCODataset
# from cvp... | 9,393 | 38.805085 | 121 | py |
DenseTeacher | DenseTeacher-main/coco-p1/runner.py | from cvpods.engine.runner import (
RUNNERS, torch, Infinite, hooks, comm, maybe_convert_module,
DistributedDataParallel, auto_scale_config, DefaultCheckpointer, get_bn_modules
)
import time
from cvpods.modeling.meta_arch.retinanet import permute_to_N_HWA_K
import torch.nn.functional as F
from ema import ModelE... | 15,218 | 37.82398 | 115 | py |
DenseTeacher | DenseTeacher-main/coco-p1/fcos.py | import math
from typing import List
import torch
import torch.nn.functional as F
from torch import nn
from cvpods.layers import ShapeSpec, cat, generalized_batched_nms
from cvpods.modeling.box_regression import Shift2BoxTransform
from cvpods.modeling.losses import iou_loss, sigmoid_focal_loss_jit
from cvpods.modeling... | 27,042 | 41.721959 | 98 | py |
DenseTeacher | DenseTeacher-main/coco-p1/ema.py | #!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
from copy import deepcopy
import torch
import torch.nn as nn
__all__ = ["ModelEMA", "is_parallel"]
def is_parallel(model):
"""check if model is in parallel mode."""
parallel_type = (
nn.parallel.... | 2,018 | 33.810345 | 93 | py |
DenseTeacher | DenseTeacher-main/coco-p10/augmentations.py | from cvpods.data.transforms import ComposeTransform, ResizeShortestEdge, RandomFlip, NoOpTransform
import torchvision.transforms as transforms
from PIL import ImageFilter
import numpy as np
class GaussianBlur:
def __init__(self, rad_range=[0.1, 2.0]):
self.rad_range = rad_range
def __call__(self, x):... | 2,609 | 30.445783 | 98 | py |
DenseTeacher | DenseTeacher-main/coco-p10/dataset.py | import contextlib
import copy
import io
import json
import os
from copy import deepcopy
import numpy as np
import torch
from cvpods.data.build import (SAMPLERS, Infinite, comm, logger,
trivial_batch_collator, worker_init_reset_seed)
from cvpods.data.datasets import COCODataset
# from cvp... | 9,394 | 38.64135 | 121 | py |
DenseTeacher | DenseTeacher-main/coco-p10/runner.py | from cvpods.engine.runner import (
RUNNERS, torch, Infinite, hooks, comm, maybe_convert_module,
DistributedDataParallel, auto_scale_config, DefaultCheckpointer, get_bn_modules
)
import time
from cvpods.modeling.meta_arch.retinanet import permute_to_N_HWA_K
import torch.nn.functional as F
from ema import ModelE... | 14,230 | 37.152815 | 115 | py |
DenseTeacher | DenseTeacher-main/coco-p10/fcos.py | import math
from typing import List
import torch
import torch.nn.functional as F
from torch import nn
from cvpods.layers import ShapeSpec, cat, generalized_batched_nms
from cvpods.modeling.box_regression import Shift2BoxTransform
from cvpods.modeling.losses import iou_loss, sigmoid_focal_loss_jit
from cvpods.modeling... | 27,042 | 41.721959 | 98 | py |
DenseTeacher | DenseTeacher-main/coco-p10/ema.py | #!/usr/bin/env python3
# -*- coding:utf-8 -*-
# Copyright (c) 2014-2021 Megvii Inc. All rights reserved.
from copy import deepcopy
import torch
import torch.nn as nn
__all__ = ["ModelEMA", "is_parallel"]
def is_parallel(model):
"""check if model is in parallel mode."""
parallel_type = (
nn.parallel.... | 2,018 | 33.810345 | 93 | py |
nbval | nbval-master/docs/source/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# nbval documentation build configuration file, created by
# sphinx-quickstart on Fri Sep 1 14:00:11 2017.
#
# 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... | 5,226 | 30.487952 | 79 | py |
politihop | politihop-master/Transformer-XH/main.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import torch
import argparse
import os
import json
import numpy as np
from model import Model_Hotpot, Model_FEVER
import data
import logging
import random
import torch.nn as nn
import torch.distributed as dist
from tqdm import tqdm
from pytorch... | 7,408 | 38.201058 | 157 | py |
politihop | politihop-master/Transformer-XH/setup.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from distutils.core import setup
from setuptools import setup
from setuptools import find_packages
# put package dependencies here
# this will make it easy to use
# i.e. run: ``python setup.py develop'' will automatically install depedencies sta... | 1,073 | 28.833333 | 153 | py |
politihop | politihop-master/Transformer-XH/transformer-xh/main.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import torch
import argparse
import os
import json
import numpy as np
from model import Model_Hotpot, Model_FEVER
import data
import logging
import random
import torch.nn as nn
import torch.distributed as dist
from tqdm import tqdm
from pytorch... | 10,899 | 43.489796 | 157 | py |
politihop | politihop-master/Transformer-XH/transformer-xh/Trainer.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from data import HotpotDataset, FEVERDataset, TransformerXHDataset
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.sampler import RandomSampler, SequentialSampler
from data import batcher_hotpot, batcher_fever
from Evaluat... | 6,137 | 34.686047 | 120 | py |
politihop | politihop-master/Transformer-XH/transformer-xh/Evaluator.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from data import HotpotDataset, FEVERDataset, TransformerXHDataset
import numpy as np
import pandas as pd
import seaborn as sn
import matplotlib.pyplot as plt
from sklearn.metrics import f1_score, confusion_matrix
from torch.utils.data import Da... | 6,091 | 32.108696 | 101 | py |
politihop | politihop-master/Transformer-XH/transformer-xh/model/model_fever.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from .model import Model, ModelHelper
import torch
import torch.nn as nn
class ModelHelper_FEVER(ModelHelper):
def __init__(self, node_encoder, args, bert_config, config_model):
super(ModelHelper_FEVER, self).__init__(node_encoder, ... | 1,889 | 35.346154 | 166 | py |
politihop | politihop-master/Transformer-XH/transformer-xh/model/model_hotpotqa.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from .model import Model, ModelHelper
import torch
import torch.nn as nn
class ModelHelper_Hotpot(ModelHelper):
def __init__(self, node_encoder, args, bert_config, config_model):
super(ModelHelper_Hotpot, self).__init__(node_encoder... | 1,563 | 34.545455 | 150 | py |
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