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FewRel | FewRel-master/models/metanet.py | import sys
sys.path.append('..')
import fewshot_re_kit
from fewshot_re_kit.network.embedding import Embedding
from fewshot_re_kit.network.encoder import Encoder
import torch
from torch import autograd, optim, nn
from torch.autograd import Variable
from torch.nn import functional as F
import numpy as np
def log_and_sig... | 8,016 | 37.729469 | 187 | py |
meta-demodulator | meta-demodulator-master/main_offline.py | from __future__ import print_function
import torch
import numpy
from nets.deeper_linear import deeper_linear_net
from nets.deeper_linear import deeper_linear_net_prime
from data_gen.data_set import generating_training_set
from data_gen.data_set import generating_test_set
from training.train import meta_train
from train... | 64,128 | 58.105069 | 329 | py |
meta-demodulator | meta-demodulator-master/main_online.py | from __future__ import print_function
import torch
import numpy
from nets.deeper_linear import deeper_linear_net
from nets.deeper_linear import deeper_linear_net_prime
from data_gen.data_set import generating_online_training_set
from data_gen.data_set import generating_test_set
from training.train import meta_train_onl... | 59,507 | 58.448551 | 308 | py |
meta-demodulator | meta-demodulator-master/nets/meta_net.py | from __future__ import print_function
import torch.nn as nn
from torch.nn import functional as F
class meta_Net(nn.Module):
def __init__(self, if_relu): # it only gets paramters from other network's parameters
super(meta_Net, self).__init__()
self.vars = nn.ParameterList()
self.softmax = nn... | 1,484 | 32 | 89 | py |
meta-demodulator | meta-demodulator-master/nets/deeper_linear.py | from __future__ import print_function
import torch.nn as nn
class deeper_Net(nn.Module):
def __init__(self, args, m_ary, num_neurons_first, num_neurons_second, num_neurons_third, if_bias, if_relu):
if m_ary == 5:
m_ary = 4
super(deeper_Net, self).__init__()
self.layer_list = []
... | 1,932 | 36.173077 | 112 | py |
meta-demodulator | meta-demodulator-master/training/train.py | from __future__ import print_function
import torch
import numpy
from loss.cross_entropy_loss import cross_entropy_loss
from loss.cross_entropy_loss import cross_entropy_loss_test
from nets.meta_net import meta_net
import math
import os
from numpy.linalg import inv
import scipy.io as sio
from data_gen.data_set import iq... | 101,937 | 53.164718 | 427 | py |
meta-demodulator | meta-demodulator-master/loss/cross_entropy_loss.py | from __future__ import print_function
import torch
def cross_entropy_loss(loss, error_rate, M, s, out):
## loss function
K, _= s.size()
success = 0
loss = 0
for i in range(K):
if M == 2:
if s[i, 0] == -1:
loss = loss - out[i][0]
if torch.argmax... | 10,797 | 37.841727 | 75 | py |
meta-demodulator | meta-demodulator-master/data_gen/data_set.py | from __future__ import print_function
import torch
def generating_symbol(M, device_for_data):
device = device_for_data
Bern = torch.distributions.bernoulli.Bernoulli(torch.tensor([0.5])) # equal prob.
if M == 2: # BPSK
symb = Bern.sample()
symb = symb.to(device)
if symb == 0:
... | 38,518 | 50.912399 | 146 | py |
BiGI | BiGI-main/BiGI_src/train_rec.py | import os
import sys
from datetime import datetime
import time
import numpy as np
import random
import argparse
from shutil import copyfile
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from model.trainer import DGITrainer
from utils.loader import DataLoader
from uti... | 12,721 | 43.020761 | 171 | py |
BiGI | BiGI-main/BiGI_src/train_lp.py | import os
import sys
from datetime import datetime
import time
import numpy as np
import random
import argparse
from shutil import copyfile
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from model.trainer import DGITrainer
from utils.loader import DataLoader,wikiData... | 11,728 | 42.280443 | 170 | py |
BiGI | BiGI-main/BiGI_src/utils/torch_utils.py | """
Utility functions for torch.
"""
import torch
from torch import nn, optim
from torch.optim.optimizer import Optimizer
### class
class MyAdagrad(Optimizer):
"""My modification of the Adagrad optimizer that allows to specify an initial
accumulater value. This mimics the behavior of the default Adagrad imple... | 5,703 | 32.751479 | 106 | py |
BiGI | BiGI-main/BiGI_src/utils/loader.py | """
Data loader for TACRED json files.
"""
import json
import random
import torch
import numpy as np
class DataLoader(object):
"""
Load data from json files, preprocess and prepare batches.
"""
def __init__(self, filename, batch_size, opt, user_real_dict, user_fake_dict, item_real_dict, item_fake_dic... | 12,021 | 43.03663 | 373 | py |
BiGI | BiGI-main/BiGI_src/utils/GraphMaker.py | import numpy as np
import random
import scipy.sparse as sp
import torch
import codecs
import json
import copy
def normalize(mx):
"""Row-normalize sparse matrix"""
rowsum = np.array(mx.sum(1))
r_inv = np.power(rowsum, -1).flatten()
r_inv[np.isinf(r_inv)] = 0.
r_mat_inv = sp.diags(r_inv)
mx = r_m... | 6,173 | 37.5875 | 200 | py |
BiGI | BiGI-main/BiGI_src/model/myDGI.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from model.GAT import GAT
class AvgReadout(nn.Module):
def __init__(self):
super(AvgReadout, self).__init__()
def forward(self, seq, msk=None):
if msk is None:
return torch.mean(seq, 0)
else:
... | 3,453 | 38.25 | 180 | py |
BiGI | BiGI-main/BiGI_src/model/GCN.py | import torch.nn as nn
import torch.nn.functional as F
import math
import torch
import torch.nn as nn
from torch.nn.modules.module import Module
class GCN(nn.Module):
def __init__(self, nfeat, nhid, dropout, alpha):
super(GCN, self).__init__()
self.gc1 = GraphConvolution(nfeat, nhid)
self.d... | 1,586 | 29.519231 | 80 | py |
BiGI | BiGI-main/BiGI_src/model/AttDGI.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class AvgReadout(nn.Module):
def __init__(self):
super(AvgReadout, self).__init__()
def forward(self, seq, msk=None):
if msk is None:
return torch.mean(seq, 0)
else:
msk = torch.unsqu... | 4,036 | 36.036697 | 182 | py |
BiGI | BiGI-main/BiGI_src/model/GNN.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from model.GCN import GCN
from torch.autograd import Variable
class GNN(nn.Module):
"""
GNN Module layer
"""
def __init__(self, opt):
super(GNN, self).__init__()
self.opt=opt
se... | 2,670 | 32.3875 | 96 | py |
BiGI | BiGI-main/BiGI_src/model/GNN2.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from model.GCN import GCN
from torch.autograd import Variable
class GNN2(nn.Module):
"""
DGCN Module layer
"""
def __init__(self, opt):
super(GNN2, self).__init__()
self.opt=opt
... | 3,755 | 31.66087 | 118 | py |
BiGI | BiGI-main/BiGI_src/model/BiGI.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from model.GNN import GNN
from model.GNN2 import GNN2
from model.AttDGI import AttDGI
from model.myDGI import myDGI
class BiGI(nn.Module):
def __init__(self, opt):
super(BiGI, self).__init__()
self.opt=opt
... | 1,702 | 36.844444 | 84 | py |
BiGI | BiGI-main/BiGI_src/model/GAT.py | import torch.nn as nn
import torch.nn.functional as F
import math
import torch
import torch.nn as nn
from torch.nn.modules.module import Module
class GAT(nn.Module):
def __init__(self, opt):
super(GAT, self).__init__()
self.att = Attention(opt)
self.dropout = opt["dropout"]
self.le... | 1,907 | 30.8 | 81 | py |
BiGI | BiGI-main/BiGI_src/model/trainer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from utils import torch_utils
from model.BiGI import BiGI
class Trainer(object):
def __init__(self, opt):
raise NotImplementedError
def update(self, batch):
raise NotImplementedError
def... | 9,418 | 44.066986 | 290 | py |
ATN | ATN-main/custom_transforms.py | # Basic library of transforms not included in torch.torchvision
# By Ashkan Pakzad (ashkanpakzad.github.io) 2022
import torch
import torchvision.transforms.functional as TF
import numpy as np
import numbers
from typing import Tuple, List, Optional
from collections.abc import Sequence
class GaussianNoise(object):
... | 10,751 | 31.581818 | 114 | py |
ATN | ATN-main/engine.py | # By Ashkan Pakzad (ashkanpakzad.github.io) 2022
import matplotlib
import matplotlib.pyplot as plt
import torch
import enum
from tqdm import tqdm
import numpy as np
import util
from sklearn import metrics
from dataset import prepare_cnr_batch
from pathlib import Path
import wandb
class Action(enum.Enum):
TRAIN =... | 13,015 | 27.4814 | 100 | py |
ATN | ATN-main/simgan.py | # By Ashkan Pakzad (ashkanpakzad.github.io) 2022
import os
import wandb
from tqdm import tqdm
import util
from imagehistorybuffer import ImageHistoryBuffer
from engine import setmodel, ModelAction, prer_train, pred_train, adv_train
from model import getmodels
from loss import VGGPerceptualLoss
from dataset import Decla... | 15,039 | 31.982456 | 101 | py |
ATN | ATN-main/imagehistorybuffer.py | # Adapted from https://github.com/mjdietzx/SimGAN/, under MIT license
import numpy as np
import torch
class ImageHistoryBuffer(object):
def __init__(self, shape, max_size, batch_size, device):
"""
Initialize the class's state.
:param shape: Shape of the data to be stored in the image histo... | 2,520 | 42.465517 | 119 | py |
ATN | ATN-main/loss.py | # By Ashkan Pakzad (ashkanpakzad.github.io) 2022
# Adapted from https://gist.github.com/alper111/8233cdb0414b4cb5853f2f730ab95a49
import torch
import torchvision
from torchvision.models import vgg16, VGG16_Weights
class VGGPerceptualLoss(torch.nn.Module):
def __init__(
self, resize=True, feature_layers=[... | 3,829 | 34.462963 | 80 | py |
ATN | ATN-main/model.py | # By Ashkan Pakzad (ashkanpakzad.github.io) 2022
from torch import nn
import util
def parsemode(mode):
if mode == 'ellipse':
outn = 8
elif mode == 'circle':
outn = 2
else:
raise(ValueError, 'argument mode invalid')
return outn
##==========================CNR=================... | 4,838 | 29.055901 | 76 | py |
ATN | ATN-main/dataset.py | # By Ashkan Pakzad (ashkanpakzad.github.io) 2022
import torch
from pathlib import Path
import tifffile
import numpy as np
from torchvision import transforms
import custom_transforms
import pandas as pd
import copy
from math import pi
class DeclareTransforms:
def __init__(self, inputsize):
self.inputsize ... | 6,518 | 29.895735 | 112 | py |
ATN | ATN-main/cnr.py | # By Ashkan Pakzad (ashkanpakzad.github.io) 2022
import wandb
import os
from pathlib import Path
import torch
from torch.utils.data import DataLoader, Subset
import random
import argparse
import engine
import dataset
import model
import util
torch.backends.cudnn.benchmark = True
os.environ["KMP_DUPLICATE_LIB_OK"] =... | 7,070 | 27.39759 | 101 | py |
ATN | ATN-main/util.py | # By Ashkan Pakzad (ashkanpakzad.github.io) 2022
import torch
import json
import matplotlib.pyplot as plt
from matplotlib.patches import Circle, Ellipse
import numpy as np
from dataset import da_vector_2_angle
def calc_acc(output, type):
assert type in [0, 1]
# TARGET 0 FOR REAL AND 1 FOR REFINED
target ... | 4,803 | 27.258824 | 99 | py |
ATN | ATN-main/atn.py | # By Ashkan Pakzad (ashkanpakzad.github.io) 2022
import os
from pathlib import Path
import wandb
import torch
import torch.nn as nn
import torchvision
from torch.utils.data import DataLoader, Subset
import random
import argparse
from dataset import DeclareTransforms, prepare_batch, ImageData
from loss import VGGPercept... | 9,601 | 29.579618 | 88 | py |
ATN | ATN-main/AQ_CNR.py | import torch
from torch.utils.data import DataLoader
from dataset import DeclareTransforms, ImageData
from model import getmodels, getCNRmodel
import util
import numpy as np
from pathlib import Path
def outputellipse(vals):
'''
Convert output of model variables to interpretable ellipse parameters.
input v... | 2,062 | 28.898551 | 94 | py |
ATN | ATN-main/MakeDatasetHead.py | # By Ashkan Pakzad (ashkanpakzad.github.io) 2022
from pathlib import Path
import torch
from torch.utils.data import DataLoader
import custom_transforms
import argparse
from dataset import ImageData
import json
from tqdm import tqdm
def args_parser():
parser = argparse.ArgumentParser('Explore', add_help=False)
... | 3,029 | 31.234043 | 116 | py |
terngrad | terngrad-master/terngrad/inception/data/build_imagenet_data.py | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 26,828 | 36.314325 | 87 | py |
terngrad | terngrad-master/terngrad/inception/slim/models.py | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agree... | 28,545 | 50.434234 | 131 | py |
terngrad | terngrad-master/slim/nets/resnet_utils.py | # Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable ... | 10,613 | 40.623529 | 80 | py |
swat-seq2seq | swat-seq2seq-master/test_dataset.py | import torch
from torch.utils.data import DataLoader
import conf
from swat_dataset import SWaTDataset
def test_parsed_dataset():
BATCH_SIZE = 5
N_SAMPLES = 20
for datatype in ['normal', 'attack']:
for pidx in range(conf.N_PROCESS):
dataset = SWaTDataset('dat/{}-P{}.dat'.format(datatyp... | 1,131 | 39.428571 | 119 | py |
swat-seq2seq | swat-seq2seq-master/network.py | import torch
from torch import nn, optim
from torch.utils.data import DataLoader
import conf
import model
from db import InfluxDB, swat_time_to_nanosec
DB = InfluxDB('swat')
class Network:
def __init__(self, pidx: int, gidx: int, n_features: int, n_hiddens: int):
self.n_features = n_features
sel... | 3,787 | 36.88 | 102 | py |
swat-seq2seq | swat-seq2seq-master/validate.py | import argparse
import sys
from datetime import datetime
import torch
from torch.utils.data import ConcatDataset
import conf
from db import InfluxDB, datetime_to_nanosec
from network import Network
from swat_dataset import SWaTDataset
N_DUPLICATE_RUNS = 2
BATCH_SIZE = 1024 # larger validation batch is possible, bu... | 1,512 | 28.666667 | 115 | py |
swat-seq2seq | swat-seq2seq-master/model.py | import torch
import torch.nn.parallel
from torch import nn
import conf
class Encoder(nn.Module):
def __init__(self, n_inputs, n_hiddens):
super().__init__()
self.n_hiddens = n_hiddens
self.lstm1 = nn.LSTMCell(input_size=n_inputs, hidden_size=n_hiddens)
self.lstm2 = nn.LSTMCell(inp... | 3,679 | 41.298851 | 113 | py |
swat-seq2seq | swat-seq2seq-master/swat_dataset.py | import pickle
import torch
from torch.utils.data import Dataset
import conf
class SWaTDataset(Dataset):
def __init__(self, pickle_jar: str):
with open(pickle_jar, 'rb') as f:
self.picks = pickle.load(f)
def __len__(self):
return len(self.picks)
def __getitem__(self, idx):
... | 717 | 26.615385 | 69 | py |
swat-seq2seq | swat-seq2seq-master/train.py | import argparse
import configparser
import sys
from datetime import datetime
import torch
import conf
from db import InfluxDB, datetime_to_nanosec
from network import Network
from swat_dataset import SWaTDataset
assert torch.cuda.device_count() >= 6
BATCH_SIZE = 4096
DB = InfluxDB('swat')
parser = argparse.Argumen... | 2,203 | 29.611111 | 100 | py |
resalloc | resalloc-main/setup.py | import codecs
import os.path
from setuptools import setup, find_packages
with open("README.md", "r") as fh:
long_description = fh.read()
def read(rel_path):
here = os.path.abspath(os.path.dirname(__file__))
with codecs.open(os.path.join(here, rel_path), "r") as fp:
return fp.read()
def get_ve... | 1,214 | 24.851064 | 62 | py |
resalloc | resalloc-main/resalloc/optim.py | import logging
import sys
import time
import torch
from resalloc.lbfgs import LBFGS
LOGGER = logging.getLogger("__doubly_projected__")
LOGGER.propagate = False
LOGGER.setLevel(logging.INFO)
_stream_handler = logging.StreamHandler(sys.stdout)
_stream_handler.setLevel(logging.INFO)
_formatter = logging.Formatter(
... | 9,382 | 27.69419 | 83 | py |
resalloc | resalloc-main/resalloc/lbfgs.py | """
L-BFGS, but with constraints.
Based on the official PyTorch implementation.
"""
import numpy as np
import torch
from functools import reduce
#from pymde.util import SolverError
def _cubic_interpolate(x1, f1, g1, x2, f2, g2, bounds=None):
# ported from https://github.com/torch/optim/blob/master/polyinterp... | 19,742 | 34.255357 | 79 | py |
resalloc | resalloc-main/resalloc/latexify.py | import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
import torch
def latexify(figsize_inches=None, font_size=12):
"""Set up matplotlib's RC params for LaTeX plotting.
This function only needs to be called once per Python session.
... | 2,202 | 26.5375 | 79 | py |
resalloc | resalloc-main/resalloc/constraints.py | import abc
import torch
class Constraint(abc.ABC):
"""A generic constraint.
To create a custom constraint, create a subclass of this class,
and implement its abstract methods.
"""
@abc.abstractmethod
def name(self) -> str:
"""The name of the constraint."""
raise NotImplement... | 3,194 | 26.307692 | 79 | py |
resalloc | resalloc-main/resalloc/fungible/fungible.py | import time
import numpy as np
import torch
import resalloc.constraints as constraints
import resalloc.optim as optim
import resalloc.fungible.utilities as utilities
class _Allocator(torch.autograd.Function):
@staticmethod
def forward(ctx, prices, allocator_object):
value, gradient = allocator_object... | 11,073 | 30.460227 | 84 | py |
resalloc | resalloc-main/resalloc/fungible/utilities.py | import torch
def _initial_prices(utility, alloc_problem):
x = alloc_problem.resource_limits[1:] / alloc_problem.n_jobs
if x.sum() > 1:
x = x / x.sum()
t = alloc_problem.A[:, 1:] @ x
u_prime_t = utility._derivative(t)
prices = (u_prime_t[:, None] * alloc_problem.A[:, 1:]).sum(
axis=... | 4,629 | 28.119497 | 80 | py |
resalloc | resalloc-main/notebooks/latexify.py | import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
import torch
def latexify(figsize_inches=None, font_size=12):
"""Set up matplotlib's RC params for LaTeX plotting.
This function only needs to be called once per Python session.
... | 2,202 | 26.5375 | 79 | py |
pypop | pypop-main/pypop7/optimizers/cem/dcem.py | import numpy as np
import torch
from lml import LML
from pypop7.optimizers.cem.scem import SCEM
class DCEM(SCEM):
"""Differentiable Cross-Entropy Method (DCEM).
.. note:: Since the underlying `lml` library may be not successfully installed via `pip`, please run the following
two commands before invok... | 5,686 | 45.235772 | 119 | py |
pypop | pypop-main/pypop7/optimizers/cem/_repeat_dcem.py | """Repeat the following paper for `DCEM`:
Amos, B. and Yarats, D., 2020, November.
The differentiable cross-entropy method.
In International Conference on Machine Learning (pp. 291-302). PMLR.
http://proceedings.mlr.press/v119/amos20a.html
Luckily our Python code could repeat the data generated by ... | 2,140 | 34.683333 | 101 | py |
pypop | pypop-main/pypop7/optimizers/cc/cosyne.py | import numpy as np
from pypop7.optimizers.cc.cc import CC
class COSYNE(CC):
"""CoOperative SYnapse NEuroevolution (COSYNE).
.. note:: This is a wrapper of `COSYNE`, which has been implemented in the Python library `EvoTorch
<https://docs.evotorch.ai/v0.3.0/reference/evotorch/algorithms/ga/#evotorch.a... | 7,527 | 49.864865 | 119 | py |
pypop | pypop-main/pypop7/optimizers/cc/_repeat_cosyne.py | """Repeat the following paper for `COSYNE`:
Gomez, F., Schmidhuber, J. and Miikkulainen, R., 2008.
Accelerated neural evolution through cooperatively coevolved synapses.
Journal of Machine Learning Research, 9(31), pp.937-965.
https://jmlr.org/papers/v9/gomez08a.html
We notice that the EvoTorch lib... | 2,420 | 39.35 | 112 | py |
affective_bias_in_plm | affective_bias_in_plm-main/LargePLMs based Emotion Detection/GPT2_semeval_finetuning.py | #!/usr/bin/env python
# coding: utf-8
import numpy as np
import pandas as pd
import seaborn as sns
import nltk
nltk.download('punkt')
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, RegexpTokenizer
from nltk.corpus import wordnet
from nltk.stem import WordNetLemma... | 8,660 | 31.438202 | 128 | py |
affective_bias_in_plm | affective_bias_in_plm-main/LargePLMs based Emotion Detection/XLNet_semeval_finetuning.py | #!/usr/bin/env python
# coding: utf-8
import numpy as np
import pandas as pd
import tensorflow as tf
import seaborn as sns
import transformers
import keras
import nltk
nltk.download('punkt')
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, RegexpTokenizer
from nlt... | 9,406 | 35.746094 | 127 | py |
affective_bias_in_plm | affective_bias_in_plm-main/LargePLMs based Emotion Detection/BERT_semeval_finetuning.py | #!/usr/bin/env python
# coding: utf-8
import numpy as np
import pandas as pd
import seaborn as sns
import nltk
nltk.download('punkt')
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, RegexpTokenizer
from nltk.corpus import wordnet
from nltk.stem import WordNetLemm... | 9,331 | 29.900662 | 200 | py |
affective_bias_in_plm | affective_bias_in_plm-main/LargePLMs based Emotion Detection/T5_semeval_finetuning.py | #!/usr/bin/env python
# coding: utf-8
import numpy as np
import pandas as pd
import seaborn as sns
import nltk
nltk.download('punkt')
nltk.download('stopwords')
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, RegexpTokenizer
from nltk.corpus import wordnet
from nltk.stem import WordNetLemma... | 8,935 | 33.501931 | 128 | py |
MutexMatch4SSL | MutexMatch4SSL-master/utils.py | import os
import time
from torch.utils.tensorboard import SummaryWriter
import logging
import torchvision.models as models_torch
from models.nets.net import *
def setattr_cls_from_kwargs(cls, kwargs):
#if default values are in the cls,
#overlap the value by kwargs
for key in kwargs.keys():
if hasatt... | 2,834 | 32.75 | 122 | py |
MutexMatch4SSL | MutexMatch4SSL-master/train_mutex.py | #import needed library
import os
import logging
import random
import warnings
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.multiprocessing as mp
from utils import net_builder, get_logger, count_paramet... | 16,721 | 43.951613 | 173 | py |
MutexMatch4SSL | MutexMatch4SSL-master/train_utils.py | import torch
from torch.utils.tensorboard import SummaryWriter
from torch.optim.lr_scheduler import LambdaLR
import torch.nn.functional as F
import math
import time
import os
class TBLog:
"""
Construc tensorboard writer (self.writer).
The tensorboard is saved at os.path.join(tb_dir, file_name).
"""
... | 5,272 | 31.549383 | 128 | py |
MutexMatch4SSL | MutexMatch4SSL-master/eval_mutex.py | from __future__ import print_function, division
import os
from models.nets.net import *
import torch
import torch.nn as nn
import numpy as np
from utils import net_builder
from datasets.ssl_dataset import SSL_Dataset
from datasets.data_utils import get_data_loader
class TotalNet(nn.Module):
def __init__(self, net... | 4,096 | 38.394231 | 131 | py |
MutexMatch4SSL | MutexMatch4SSL-master/datasets_mini/miniimage.py | from PIL import Image, ImageFilter
import os, sys
import random
import numpy as np
import pandas as pd
import os.path as osp
import torch
import torchvision
from torch.utils.data import Dataset
from torchvision import transforms
# from torchvision.transforms import InterpolationMode
from datasets_mini.randaugment im... | 11,699 | 36.620579 | 230 | py |
MutexMatch4SSL | MutexMatch4SSL-master/datasets_mini/randaugment.py | # copyright: https://github.com/ildoonet/pytorch-randaugment
# code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
# This code is modified version of one of ildoonet, for randaugmentation of fixmatch.
import random
import PIL, PIL.ImageOps, PIL... | 4,485 | 23.248649 | 85 | py |
MutexMatch4SSL | MutexMatch4SSL-master/models/nets/resnet18.py | import torch.nn as nn
class ReverseCLS(nn.Module):
def __init__(self, in_dim, out_dim):
super(ReverseCLS, self).__init__()
self.fc = nn.Linear(in_dim, out_dim)
self.main = nn.Sequential(self.fc, nn.Softmax(dim=-1))
def forward(self, x):
out = [x]
for module in self.mai... | 1,212 | 28.585366 | 79 | py |
MutexMatch4SSL | MutexMatch4SSL-master/models/nets/net.py | import torch.nn as nn
class ReverseCLS(nn.Module):
def __init__(self, in_dim, out_dim):
super(ReverseCLS, self).__init__()
self.fc = nn.Linear(in_dim, out_dim)
self.main = nn.Sequential(self.fc, nn.Softmax(dim=-1))
def forward(self, x):
out = [x]
for module in self.mai... | 408 | 20.526316 | 62 | py |
MutexMatch4SSL | MutexMatch4SSL-master/models/nets/cnn13.py | ## CNN-13
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import weight_norm
class CNN13(nn.Module):
def __init__(self, num_classes=10, dropout=0.5):
super(CNN13, self).__init__()
#self.gn = GaussianNoise(0.15)
self.activation = nn.LeakyReLU(0.1)
s... | 3,601 | 29.786325 | 70 | py |
MutexMatch4SSL | MutexMatch4SSL-master/models/nets/wrn.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
momentum=0.001
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, bn_momentum=0.1, leaky_slope=0.0, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes, mome... | 5,545 | 41.335878 | 140 | py |
MutexMatch4SSL | MutexMatch4SSL-master/models/mutexmatch/mutexmatch.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import os
import contextlib
from models.nets.net import *
from torch.cuda.amp import autocast, GradScaler
from .mutexmatch_utils import consistency_loss, Get_Scalar
from train_utils import ce_loss
class TotalNet(... | 14,992 | 41.59375 | 172 | py |
MutexMatch4SSL | MutexMatch4SSL-master/models/mutexmatch/mutexmatch_utils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from train_utils import ce_loss
class Get_Scalar:
def __init__(self, value):
self.value = value
def get_value(self, iter):
return self.value
def __call__(self, iter):
return self.value
def consistency_... | 2,113 | 36.087719 | 146 | py |
MutexMatch4SSL | MutexMatch4SSL-master/datasets/tinyimage.py | from torch.utils.data import Dataset, DataLoader
from torchvision import models, utils, datasets, transforms
import numpy as np
import sys
import os
from PIL import Image
from .augmentation.randaugment import RandAugment
class TinyImageNet(Dataset):
def __init__(self, root, train=True):
self.Train = trai... | 4,844 | 37.149606 | 106 | py |
MutexMatch4SSL | MutexMatch4SSL-master/datasets/ssl_dataset.py | import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from torch.utils.data.sampler import BatchSampler
from .augmentation.randaugment import RandAugment
from .data_utils import get_sampler_by_name, get_data_loader, get_onehot, split_ssl_data
from .dataset import BasicDataset
import torchv... | 10,287 | 44.321586 | 167 | py |
MutexMatch4SSL | MutexMatch4SSL-master/datasets/data_utils.py | import torch
import torchvision
from torchvision import datasets
from torch.utils.data import sampler, DataLoader
from torch.utils.data.sampler import BatchSampler
import torch.distributed as dist
import numpy as np
import math
from datasets.DistributedProxySampler import DistributedProxySampler
def split_ssl_da... | 4,979 | 34.070423 | 103 | py |
MutexMatch4SSL | MutexMatch4SSL-master/datasets/dataset.py | from torchvision import datasets, transforms
from torch.utils.data import Dataset
from .data_utils import get_onehot
from .augmentation.randaugment import RandAugment
from PIL import Image
import numpy as np
import copy
class BasicDataset(Dataset):
"""
BasicDataset returns a pair of image and labels (targets... | 2,980 | 33.264368 | 106 | py |
MutexMatch4SSL | MutexMatch4SSL-master/datasets/DistributedProxySampler.py | # copyright: https://github.com/pytorch/pytorch/issues/23430#issuecomment-562350407
import math
import torch
from torch.utils.data.distributed import DistributedSampler
class DistributedProxySampler(DistributedSampler):
"""Sampler that restricts data loading to a subset of input sampler indices.
It is espec... | 1,748 | 37.866667 | 115 | py |
MutexMatch4SSL | MutexMatch4SSL-master/datasets/augmentation/randaugment.py | # copyright: https://github.com/ildoonet/pytorch-randaugment
# code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
# This code is modified version of one of ildoonet, for randaugmentation of fixmatch.
import random
import PIL, PIL.ImageOps, PIL... | 4,485 | 23.248649 | 85 | py |
BTAI_3MF | BTAI_3MF-master/main_BTAI_3MF.py | import time
from agent.inference.TemporalSliceBuilder import TemporalSliceBuilder
from env.dSpritesEnv import dSpritesEnv
from env.wrapper.dSpritesPreProcessingWrapper import dSpritesPreProcessingWrapper
from agent.BTAI_3MF import BTAI_3MF
import torch
# ---------------------------------------------------------------... | 4,873 | 47.257426 | 107 | py |
BTAI_3MF | BTAI_3MF-master/analysis/widgets/NavBar.py | import tkinter as tk
from tkinter import messagebox
import torch
#
# Class representing the main navigation bar.
#
class NavBar(tk.Menu):
def __init__(self, gui):
"""
Construct the main navigation bar.
:param gui: the graphical user interface.
"""
# Call super class contru... | 1,114 | 26.195122 | 81 | py |
BTAI_3MF | BTAI_3MF-master/analysis/frames/VisualisationFrame.py | import tkinter as tk
from PIL import Image, ImageTk
import numpy as np
class VisualisationFrame(tk.Frame):
"""
Class representing the frame used to visualise the agent's planning
and action selection scheme.
"""
def __init__(self, parent, gui):
"""
Construct the visulisation frame... | 12,279 | 36.553517 | 99 | py |
BTAI_3MF | BTAI_3MF-master/env/dSpritesEnv.py | import random
import torch
from env.viewer.DefaultViewer import DefaultViewer
from data.dSpritesDataset import DataSet
import numpy as np
class dSpritesEnv:
"""
A class implementing the dSprites environment.
"""
def __init__(self, granularity=4, repeat=8, dataset_file="./data/dsprites.npz"):
... | 6,643 | 32.22 | 110 | py |
BTAI_3MF | BTAI_3MF-master/env/wrapper/dSpritesPreProcessingWrapper.py | import torch
from torch.nn.functional import one_hot
class dSpritesPreProcessingWrapper:
"""
Class preforming the pre-processing of the observation coming out
of the dSprites environment.
"""
def __init__(self, env, obs_names=None):
"""
Construct the pre-processor of the dSprites ... | 7,148 | 36.626316 | 120 | py |
BTAI_3MF | BTAI_3MF-master/agent/graph/VariableNode.py | import torch
from agent.graph.Node import Node
class VariableNode(Node):
"""
Class representing a variable node in the factor graph.
"""
def __init__(self, name):
"""
Construct a node of the factor graph.
:param name: the node name.
"""
super().__init__(name, {... | 793 | 26.37931 | 71 | py |
BTAI_3MF | BTAI_3MF-master/agent/inference/TemporalSlice.py | import math
import queue
import torch
from torch.nn.functional import one_hot
from agent.inference.Operators import Operators
class TemporalSlice:
"""
A class representing a temporal slice that can contain several states,
actions and observations.
"""
def __init__(
self, fg, n_actions... | 10,702 | 38.936567 | 114 | py |
BTAI_3MF | BTAI_3MF-master/agent/inference/Operators.py | import torch
class Operators:
@staticmethod
def expansion(x1, n, dim):
"""
Expand the input tensor along a dimension by repating its content n times.
:param x1: the input tensor.
:param n: the number of times the content needs to be repeated.
:param dim: the dimension ... | 2,831 | 33.962963 | 95 | py |
fvcore | fvcore-main/tests/test_focal_loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import typing
import unittest
import numpy as np
import torch
from fvcore.nn import (
sigmoid_focal_loss,
sigmoid_focal_loss_jit,
sigmoid_focal_loss_star,
sigmoid_focal_loss_star_jit,
)
from torch.nn import functional as F
def l... | 18,603 | 38.752137 | 88 | py |
fvcore | fvcore-main/tests/test_checkpoint.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import copy
import os
import random
import string
import typing
import unittest
from collections import OrderedDict
from tempfile import TemporaryDirectory
from typing import Tuple
from unittest.mock import MagicMock
import torch
from fvcore.comm... | 13,541 | 37.362606 | 88 | py |
fvcore | fvcore-main/tests/test_transform.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import itertools
import unittest
from typing import Any, Tuple
import numpy as np
import torch
from fvcore.transforms import transform as T
from fvcore.transforms.transform_util import to_float_tensor, to_numpy
# pyre-ignore-all-errors
class Te... | 40,628 | 36.138026 | 96 | py |
fvcore | fvcore-main/tests/bm_focal_loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from fvcore.common.benchmark import benchmark
from test_focal_loss import TestFocalLoss, TestFocalLossStar
def bm_focal_loss() -> None:
if not torch.cuda.is_available():
print("Skipped: CUDA unavailable")
return
... | 1,377 | 23.175439 | 85 | py |
fvcore | fvcore-main/tests/test_flop_count.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# pyre-ignore-all-errors[2,3,14,53]
import typing
import unittest
from collections import Counter, defaultdict
from typing import Any, Dict, Tuple
import torch
import torch.nn as nn
from fvcore.nn.flop_count import _DEFAULT_SUPPORTED_OPS, flop_cou... | 28,987 | 30.069668 | 88 | py |
fvcore | fvcore-main/tests/test_activation_count.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# pyre-ignore-all-errors[2]
import typing
import unittest
from collections import Counter, defaultdict
from typing import Any, Dict, List, Tuple
import torch
import torch.nn as nn
from fvcore.nn.activation_count import activation_count, Activatio... | 5,215 | 31.397516 | 87 | py |
fvcore | fvcore-main/tests/test_smooth_l1_loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import unittest
import numpy as np
import torch
from fvcore.nn import smooth_l1_loss
class TestSmoothL1Loss(unittest.TestCase):
def setUp(self) -> None:
super().setUp()
np.random.seed(42)
def test_smooth_l1_loss(self) -... | 1,209 | 34.588235 | 86 | py |
fvcore | fvcore-main/tests/test_weight_init.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import itertools
import math
import unittest
import torch
import torch.nn as nn
from fvcore.nn.weight_init import c2_msra_fill, c2_xavier_fill
class TestWeightInit(unittest.TestCase):
"""
Test creation of WeightInit.
"""
def se... | 5,795 | 38.162162 | 80 | py |
fvcore | fvcore-main/tests/test_layers_squeeze_excitation.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import itertools
import unittest
from typing import Iterable
import torch
from fvcore.nn.squeeze_excitation import (
ChannelSpatialSqueezeExcitation,
SpatialSqueezeExcitation,
SqueezeExcitation,
)
class TestSqueezeExcitation(unittest... | 3,784 | 29.772358 | 81 | py |
fvcore | fvcore-main/tests/test_precise_bn.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# -*- coding: utf-8 -*-
import itertools
import unittest
from typing import List, Tuple
import numpy as np
import torch
from fvcore.nn import update_bn_stats
from torch import nn
class TestPreciseBN(unittest.TestCase):
def setUp(self) -> No... | 3,345 | 36.595506 | 86 | py |
fvcore | fvcore-main/tests/test_param_count.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import unittest
from fvcore.nn.parameter_count import parameter_count, parameter_count_table
from torch import nn
class NetWithReuse(nn.Module):
def __init__(self, reuse: bool = False) -> None:
super().__init__()
self.conv1... | 1,398 | 29.413043 | 76 | py |
fvcore | fvcore-main/tests/test_jit_model_analysis.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# pyre-ignore-all-errors[2,56]
import logging
import typing
import unittest
import warnings
from collections import Counter
from typing import Any, Dict, List
import torch
import torch.nn as nn
from fvcore.nn.flop_count import FlopCountAnalysis
fr... | 28,372 | 33.308343 | 92 | py |
fvcore | fvcore-main/tests/test_print_model_statistics.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import unittest
from typing import Dict
import torch
import torch.nn as nn
from fvcore.nn import ActivationCountAnalysis, FlopCountAnalysis
from fvcore.nn.print_model_statistics import (
_fill_missing_statistics,
_group_by_module,
_ind... | 19,540 | 34.723949 | 87 | py |
fvcore | fvcore-main/tests/test_giou_loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import unittest
import numpy as np
import torch
from fvcore.nn import giou_loss
class TestGIoULoss(unittest.TestCase):
def setUp(self) -> None:
super().setUp()
np.random.seed(42)
def test_giou_loss(self) -> None:
... | 2,057 | 33.3 | 72 | py |
fvcore | fvcore-main/fvcore/nn/activation_count.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# pyre-ignore-all-errors[2,33]
from collections import defaultdict
from typing import Any, Counter, DefaultDict, Dict, Optional, Tuple, Union
import torch.nn as nn
from torch import Tensor
from .jit_analysis import JitModelAnalysis
from .jit_han... | 4,577 | 35.919355 | 87 | py |
fvcore | fvcore-main/fvcore/nn/giou_loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
def giou_loss(
boxes1: torch.Tensor,
boxes2: torch.Tensor,
reduction: str = "none",
eps: float = 1e-7,
) -> torch.Tensor:
"""
Generalized Intersection over Union Loss (Hamid Rezatofighi et. al)
https://ar... | 2,042 | 30.921875 | 84 | py |
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