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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STAN | STAN-master/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import warnings
#warnings.filterwarnings('ignore')
class GATLayer(nn.Module):
def __init__(self, g, in_dim, out_dim):
super(GATLayer, self).__init__()
self.g = g
self.fc = nn.Linear(in_dim, out_dim)
... | 5,331 | 35.027027 | 101 | py |
wdr | wdr-main/FGWS/main.py | """
Parts based on https://colab.research.google.com/drive/1pTuQhug6Dhl9XalKB0zUGf4FIdYFlpcX
"""
import os
import math
import torch
import numpy as np
import torch.nn as nn
from torch.nn.utils import clip_grad_norm_
import torch.optim as optim
from copy import deepcopy
from config import Config
from logger import Logge... | 9,693 | 29.580442 | 88 | py |
wdr | wdr-main/FGWS/utils.py | """
Parts based on https://colab.research.google.com/drive/1pTuQhug6Dhl9XalKB0zUGf4FIdYFlpcX
"""
import re
import torch
import pickle
import math
import random
import sklearn
import os
import torch.nn as nn
import numpy as np
import statsmodels.stats.api as stats
from shutil import copyfile
from nltk.corpus import word... | 14,632 | 27.358527 | 109 | py |
wdr | wdr-main/FGWS/config.py | import argparse
import os
class Config:
parser = argparse.ArgumentParser(description="args for experiments")
parser.add_argument(
"-mode",
dest="mode",
default="train",
type=str,
help="mode is either train, test, attack or detect",
)
parser.add_argument(
... | 8,742 | 27.571895 | 108 | py |
wdr | wdr-main/FGWS/detect_utils/transformer.py | from copy import deepcopy
from utils import inference, list_join
from nltk.tokenize.treebank import TreebankWordDetokenizer
import torch.nn as nn
import torch
class Transformer:
def __init__(
self, orig_text, model, detector, data_module, tokenizer, config, is_huggingface=True, bert_wrapper=None
):
... | 3,324 | 32.928571 | 126 | py |
wdr | wdr-main/FGWS/data/models/roberta/imdb/config.py | import argparse
import os
class Config:
parser = argparse.ArgumentParser(description="args for experiments")
parser.add_argument(
"-mode",
dest="mode",
default="train",
type=str,
help="mode is either train, test, attack or detect",
)
parser.add_argument(
... | 8,742 | 27.571895 | 108 | py |
wdr | wdr-main/Classifier/lstm_agnews_textattack.py | import tensorflow
import pickle
import re
import numpy as np
import textattack
"""
LSTM 4 Classification
^^^^^^^^^^^^^^^^^^^^^^^
"""
import torch
from torch import nn as nn
import textattack
from textattack.models.helpers import GloveEmbeddingLayer
from textattack.models.helpers.utils import load_cached_state_dic... | 2,763 | 28.404255 | 103 | py |
wdr | wdr-main/Classifier/lstm_imdb_textattack.py | import tensorflow
import pickle
import re
import numpy as np
import textattack
"""
LSTM 4 Classification
^^^^^^^^^^^^^^^^^^^^^^^
"""
import torch
from torch import nn as nn
import textattack
from textattack.models.helpers import GloveEmbeddingLayer
from textattack.models.helpers.utils import load_cached_state_dic... | 2,760 | 28.37234 | 103 | py |
wdr | wdr-main/Classifier/cnn_agnews_textattack.py | """
Word CNN for Classification
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
import textattack
from textattack.models.helpers import GloveEmbeddingLayer
from textattack.models.helpers.utils import load_cached_state_dict
from textatta... | 2,692 | 28.593407 | 101 | py |
wdr | wdr-main/Classifier/cnn_imdb_textattack.py | """
Word CNN for Classification
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
"""
import torch
from torch import nn as nn
from torch.nn import functional as F
import textattack
from textattack.models.helpers import GloveEmbeddingLayer
from textattack.models.helpers.utils import load_cached_state_dict
from textatta... | 2,689 | 28.56044 | 101 | py |
data-preparation | data-preparation-main/preprocessing/training/01a_catalogue_cleaning_and_filtering/clean.py | import argparse
import json
import logging
import random
from functools import partial
import torch
from datasets import Dataset, load_dataset, load_from_disk, concatenate_datasets, set_caching_enabled
from pathlib import Path
from typing import Tuple, Optional, Callable, List, Dict
from datasets.utils.logging import ... | 15,408 | 47.003115 | 166 | py |
shapeunity | shapeunity-master/train.py | #!/usr/bin/env python3
"""Training and Evaluate the Neural Netowrk
Usage:
train.py [options] [<yaml-config>]
train.py (-h | --help )
Options:
-h --help Show this screen.
-d --devices <devices> Comma seperated GPU devices [default: 0]
-i --identifier <identifier> ... | 6,295 | 29.712195 | 81 | py |
shapeunity | shapeunity-master/wireframe/dot.py | from collections import namedtuple
import torch
from graphviz import Digraph
from torch.autograd import Variable
Node = namedtuple("Node", ("name", "inputs", "attr", "op"))
def make_dot(var, params=None):
""" Produces Graphviz representation of PyTorch autograd graph.
Blue nodes are the Variables that requ... | 4,715 | 27.409639 | 83 | py |
shapeunity | shapeunity-master/wireframe/transformation.py |
# -*- coding: utf-8 -*-
# transformations.py
# Copyright (c) 2006-2019, Christoph Gohlke
# Copyright (c) 2006-2019, The Regents of the University of California
# Produced at the Laboratory for Fluorescence Dynamics
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modifi... | 67,038 | 33.221031 | 79 | py |
shapeunity | shapeunity-master/wireframe/datasets.py | import os
import glob
import json
import random
import os.path as osp
from timeit import default_timer as timer
from collections import deque
import cv2
import numpy as np
import torch
import matplotlib.pylab as plt
from skimage import io, transform
from torch.utils.data import Dataset
import wireframe.utils as utils... | 2,245 | 31.085714 | 78 | py |
shapeunity | shapeunity-master/wireframe/trainer.py | import os
import os.path as osp
import shutil
import threading
from timeit import default_timer as timer
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
from skimage import io
import wireframe.utils as utils
plt.rcParams["figure.figsize"] = (24, 24)
def tprint(*args)... | 9,832 | 34.243728 | 87 | py |
shapeunity | shapeunity-master/wireframe/models/fcn8s.py | import os.path as osp
import numpy as np
import torch
import torch.nn as nn
# https://github.com/shelhamer/fcn.berkeleyvision.org/blob/master/surgery.py
def get_upsampling_weight(in_channels, out_channels, kernel_size):
"""Make a 2D bilinear kernel suitable for upsampling"""
factor = (kernel_size + 1) // 2
... | 6,744 | 32.894472 | 86 | py |
shapeunity | shapeunity-master/wireframe/models/multitask_learner.py | from collections import OrderedDict, defaultdict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def l2loss(input, target):
return ((target - input) ** 2).mean(2).mean(1)
def cross_entropy_loss(logits, positive):
"""This loss will bias to false negative"""
nlogp = ... | 3,987 | 31.422764 | 88 | py |
shapeunity | shapeunity-master/wireframe/models/resnet_baseline.py | import torch
import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1, has_bias=False):
"3x3 convolution with padding"
return nn.Conv2d(
in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=has_bias
)
def conv3x3_bn_relu(in_planes, out_planes, stride=1):
return nn.Se... | 6,327 | 31.451282 | 85 | py |
shapeunity | shapeunity-master/wireframe/models/hourglass_pose.py | """
Hourglass network inserted in the pre-activated Resnet
Use lr=0.01 for current version
(c) YANG, Wei
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
# from .preresnet import BasicBlock, Bottleneck
__all__ = ["HourglassNet", "hg"]
class Bottleneck(nn.Module):
expansion = 2
def __... | 6,044 | 30.484375 | 87 | py |
shapeunity | shapeunity-master/wireframe/models/meta_builder.py | # initial copied from CSAIL semantic-segmentation-pytorch
# modified by Haozhi Qi
import torch
import torch.nn as nn
from wireframe.models.resnet_baseline import ResNet, UPerNet
from wireframe.models.backbones.resnet import get_resnet50
class MetaBuilder:
# customized weights
def init_weights(self, m):
... | 2,034 | 33.491525 | 84 | py |
shapeunity | shapeunity-master/wireframe/models/hourglass.py | import torch.nn as nn
import torch.nn.functional as F
FE_STAGE0 = 64
FE_STAGE1 = 128
BN_IO = 256
def ConvReLU(n_in, n_out, kernel_size=1, stride=1, padding=0):
return nn.Sequential(
nn.Conv2d(
n_in,
n_out,
kernel_size=kernel_size,
stride=stride,
... | 3,481 | 28.016667 | 83 | py |
shapeunity | shapeunity-master/wireframe/models/backbones/resnet.py | import os
import sys
import math
import torch
import torch.nn as nn
try:
from urllib import urlretrieve
except ImportError:
from urllib.request import urlretrieve
model_urls = {
"resnet18": "http://sceneparsing.csail.mit.edu/model/pretrained_resnet/resnet18-imagenet.pth",
"resnet50": "http://scenepa... | 4,913 | 31.117647 | 100 | py |
ZS-F-VQA | ZS-F-VQA-main/code/main.py | import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
import pdb
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data... | 15,850 | 43.903683 | 214 | py |
ZS-F-VQA | ZS-F-VQA-main/code/joint_test.py |
import os
import os.path as osp
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
import pdb
import torch.optim as optim
from torch.autograd import Variabl... | 18,694 | 47.18299 | 162 | py |
ZS-F-VQA | ZS-F-VQA-main/code/config.py | import os.path as osp
import numpy as np
import random
import torch
from easydict import EasyDict as edict
import argparse
import pdb
class cfg():
def __init__(self):
self.fusion_model_path = ""
self.answer_net_path = ""
self.joint_test_way = 0
self.this_dir = osp.dirname(__file... | 11,603 | 48.802575 | 119 | py |
ZS-F-VQA | ZS-F-VQA-main/code/torchlight/utils.py | """
Utilizations for common usages.
"""
import os
import random
import torch
import numpy as np
from difflib import SequenceMatcher
from unidecode import unidecode
from datetime import datetime
from torch.nn.parallel import DataParallel, DistributedDataParallel
def personal_display_settings():
"""
Pandas Doc
... | 5,721 | 29.115789 | 94 | py |
ZS-F-VQA | ZS-F-VQA-main/code/torchlight/logger.py | import os
import re
import sys
import time
import json
import torch
import pickle
import random
import getpass
import logging
import argparse
import subprocess
import numpy as np
from datetime import timedelta, date
from .utils import get_code_version
class LogFormatter():
def __init__(self):
self.start_... | 4,509 | 29.472973 | 105 | py |
ZS-F-VQA | ZS-F-VQA-main/code/torchlight/module.py | import math
from typing import Sequence, Union, Callable
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
torch.manual_seed(10086)
# typing, everything in Python is Object.
tensor_activation = Callable[[torch.... | 5,594 | 40.753731 | 105 | py |
ZS-F-VQA | ZS-F-VQA-main/code/torchlight/vocab.py | # coding: utf-8
"""
Every NLP task needs a Vocabulary
Every Vocabulary is built from Instances
Every Instance is a collection of Fields
"""
__all__ = ['DefaultLookupDict', 'Vocabulary']
PAD_TOKEN = '<pad>'
UNK_TOKEN = '<unk>'
BOS_TOKEN = '<bos>'
EOS_TOKEN = '<eos>'
PAD_IDX = 0
UNK_IDX = 1
class DefaultLookupDict(di... | 4,428 | 31.094203 | 81 | py |
ZS-F-VQA | ZS-F-VQA-main/code/torchlight/metric.py | # from abc import ABC, ABCMeta, abstractclassmethod
import torch
import numpy as np
from abc import ABC, abstractmethod, ABCMeta
class Metric(metaclass=ABCMeta):
"""
Abstract Base class (ABC) for all Metrics.
Taken from https://github.com/pytorch/ignite/metrics/metric.py
and modify a bit.
Ofte... | 3,906 | 29.76378 | 79 | py |
ZS-F-VQA | ZS-F-VQA-main/code/utils/tool.py | import os
import json
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from nltk import word_tokenize, pos_tag
from nltk.corpus import wordnet
from nltk.stem import WordNetLemmatizer
import pdb
def instance_bce_with_logits(logits, labels):
assert logits.dim() == 2
loss = nn.fun... | 9,691 | 30.777049 | 114 | py |
ZS-F-VQA | ZS-F-VQA-main/code/utils/metrics.py | import os
import json
import torch
import torch.nn as nn
import time
import pdb
class Metrics:
"""
Stores accuracy (score), loss and timing info
"""
def __init__(self, topnum=10):
self.topnum = topnum
self.total_loss = 0
self.correct_1 = 0
self.correct_3 = 0
... | 6,508 | 35.161111 | 103 | py |
ZS-F-VQA | ZS-F-VQA-main/code/data/base.py | import json
import os
import os.path as osp
import nltk
import h5py
import torch
import torch.utils.data as data
import pdb
from nltk import word_tokenize, pos_tag
import re
import numpy as np
import sys
import pickle as pkl
################
from .preprocess import invert_dict
class VisualQA(data.Dataset):
def _... | 16,497 | 40.039801 | 139 | py |
ZS-F-VQA | ZS-F-VQA-main/code/data/fvqa.py | import json
import os
import os.path as osp
import nltk
from collections import Counter
import torch
import torch.utils.data as data
import pdb
################
from .base import VisualQA
from .preprocess import process_punctuation
def get_loader(args, vector, train=False, val=False):
""" Returns a data loader f... | 9,398 | 38.658228 | 132 | py |
ZS-F-VQA | ZS-F-VQA-main/code/data/preprocess.py | import os
import os.path as osp
import re
import random
import itertools
import h5py
import torch
import torch.utils.data as data
import pdb
from torch.utils.data.dataloader import default_collate
from collections import Counter
from PIL import Image
# this is used for normalizing questions
_special_chars = re.compile(... | 4,774 | 30.622517 | 109 | py |
ZS-F-VQA | ZS-F-VQA-main/code/model/fc.py | from __future__ import print_function
import torch.nn as nn
from torch.nn.utils.weight_norm import weight_norm
import torch
class FCNet(nn.Module):
"""Simple class for non-linear fully connect network
"""
def __init__(self, dims, act='ReLU', dropout=0):
super(FCNet, self).__init__()
laye... | 5,810 | 36.490323 | 91 | py |
ZS-F-VQA | ZS-F-VQA-main/code/model/answer_mlp.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
import pdb
import model.fc as FC
from .fc import GroupMLP, GroupMLP_2lay, GroupMLP_1lay
class MLP(nn.Module):
def __init__(self, args, dataset):
super(MLP, self).__init__()
ans_net_list = ["GroupMLP", "... | 999 | 30.25 | 69 | py |
ZS-F-VQA | ZS-F-VQA-main/code/model/fusion_san.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from utils import freeze_layer
from torch.autograd import Variable
from .attention import SanAttention, apply_attention
from .fc import GroupMLP
from .language_model import Seq2SeqRNN, WordEmbedding
import pdb
class SAN(nn.... | 2,675 | 35.162162 | 99 | py |
ZS-F-VQA | ZS-F-VQA-main/code/model/counting.py | """
Learning to Count Objects in Natural Images for Visual Question Answering
Yan Zhang, Jonathon Hare, Adam Prügel-Bennett
ICLR 2018
This code is from Yan Zhang's repository.
https://github.com/Cyanogenoid/vqa-counting/blob/master/vqa-v2/counting.py
MIT License
"""
import torch
import torch.nn as nn
import torch.nn.f... | 7,535 | 41.576271 | 298 | py |
ZS-F-VQA | ZS-F-VQA-main/code/model/classifier.py | import torch.nn as nn
from torch.nn.utils.weight_norm import weight_norm
class SimpleClassifier(nn.Module):
def __init__(self, in_dim, hid_dim, out_dim, dropout):
super(SimpleClassifier, self).__init__()
layers = [
weight_norm(nn.Linear(in_dim, hid_dim), dim=None),
nn.ReLU(... | 565 | 28.789474 | 62 | py |
ZS-F-VQA | ZS-F-VQA-main/code/model/fusion_updn.py | import torch
import torch.nn as nn
from .language_model import WordEmbedding, UpDnQuestionEmbedding
from .attention import UpDnAttention
from .classifier import SimpleClassifier
from .fc import FCNet
from utils import freeze_layer
class UD(nn.Module):
def __init__(self, args, dataset, question_word2vec):
s... | 1,691 | 35 | 89 | py |
ZS-F-VQA | ZS-F-VQA-main/code/model/fusion_ban.py | """
Bilinear Attention Networks
Jin-Hwa Kim, Jaehyun Jun, Byoung-Tak Zhang
https://arxiv.org/abs/1805.07932
This code is adapted from: https://github.com/jnhwkim/ban-vqa (written by Jin-Hwa Kim)
"""
import torch.nn as nn
from .attention import BiAttention
from .classifier import SimpleClassifier
from .counting import... | 2,655 | 35.888889 | 99 | py |
ZS-F-VQA | ZS-F-VQA-main/code/model/vector.py | # The following code is modified based on https://github.com/pytorch/text/blob/master/torchtext/vocab.py
import array
import zipfile
from tqdm import tqdm
from six.moves.urllib.request import urlretrieve
import os
import os.path as osp
import torch
import io
# from ansemb.config import data_root, cfg
def reporthook(t... | 5,804 | 39.594406 | 104 | py |
ZS-F-VQA | ZS-F-VQA-main/code/model/attention.py | import torch
import torch.nn as nn
from torch.nn.utils.weight_norm import weight_norm
from .fc import FCNet, BCNet
import torch.nn.functional as F
class BaseAttention(nn.Module):
def __init__(self, v_dim, q_dim, num_hid):
super(BaseAttention, self).__init__()
self.nonlinear = FCNet([v_dim + q_dim, ... | 4,941 | 33.559441 | 104 | py |
ZS-F-VQA | ZS-F-VQA-main/code/model/fusion_mlp.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from utils import freeze_layer
from torch.autograd import Variable
from .fc import GroupMLP
from .language_model import WordEmbedding
class MLP(nn.Module):
#args, self.train_loader.dataset, self.question_word2vec
#... | 2,450 | 37.904762 | 112 | py |
ZS-F-VQA | ZS-F-VQA-main/code/model/language_model.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
from torch.nn.utils.rnn import pack_padded_sequence
import torch.nn.init as init
import pdb
class WordEmbedding(nn.Module):
"""Word Embedding
The ntoken-th dim is used for padding_idx, which agrees *implicitly*
with... | 7,794 | 34.431818 | 114 | py |
DeepCCA | DeepCCA-master/DeepCCA.py | try:
import cPickle as thepickle
except ImportError:
import _pickle as thepickle
import gzip
import numpy as np
from keras.callbacks import ModelCheckpoint
from utils import load_data, svm_classify
from linear_cca import linear_cca
from models import create_model
def train_model(model, data1, data2, epoch_n... | 6,852 | 39.791667 | 121 | py |
DeepCCA | DeepCCA-master/utils.py | import gzip
from sklearn import svm
from sklearn.metrics import accuracy_score
import numpy as np
import theano
from keras.utils.data_utils import get_file
def load_data(data_file, url):
"""loads the data from the gzip pickled files, and converts to numpy arrays"""
print('loading data ...')
path = get_fil... | 1,925 | 26.514286 | 93 | py |
DeepCCA | DeepCCA-master/models.py | from keras.layers import Dense, Merge
from keras.models import Sequential
from keras.optimizers import RMSprop
from keras.regularizers import l2
from objectives import cca_loss
def create_model(layer_sizes1, layer_sizes2, input_size1, input_size2,
learning_rate, reg_par, outdim_size, use_all_singu... | 1,455 | 32.860465 | 97 | py |
xfl | xfl-main/xfl/src/classes/gnn.py | import context
from classes.config import Config
import classes.utils
import dgl
import dgl.function as fn
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from dgl import DGLGraph
from dgl.data import citation_graph as citegrh
from dgl.nn.pytorch import GraphConv
import networkx as nx
import ti... | 5,547 | 31.255814 | 107 | py |
xfl | xfl-main/xfl/src/classes/dexter.py | import context
import os
#disable GPU
os.environ["CUDA_VISIBLE_DEVICES"]="-1"
from classes.config import Config
from classes.NLP import NLP
from tqdm import tqdm
import classes.utils
import torch as th
import torch.nn as nn
import networkx as nx
import numpy as np
import scipy as sp
import gc
import pandas as pd
import... | 31,573 | 36.321513 | 157 | py |
cca_zoo | cca_zoo-main/cca_zoo/probabilistic/_probabilisticcca.py | from typing import Iterable
import jax.numpy as jnp
import numpy as np
import numpyro
import numpyro.distributions as dist
from jax.random import PRNGKey
from numpyro.infer import MCMC, NUTS, Predictive
from sklearn.utils.validation import check_is_fitted
from cca_zoo._base import BaseModel
from typing import Iterab... | 6,433 | 34.944134 | 229 | py |
cca_zoo | cca_zoo-main/cca_zoo/linear/_gradient/_stochasticpls.py | import torch
from cca_zoo.linear._gradient._ey import PLSEY
from cca_zoo.linear._gradient._gradient import GradientLoop
from cca_zoo.linear._pls import PLSMixin
class PLSStochasticPower(PLSEY, PLSMixin):
def _get_module(self, weights=None, k=None):
return StochasticPowerLoop(
weights=weights,... | 1,713 | 28.050847 | 75 | py |
cca_zoo | cca_zoo-main/cca_zoo/linear/_gradient/_svd.py | import torch
from cca_zoo.linear._gradient._ey import CCAEY, EYLoop
from cca_zoo.linear._pls import PLSMixin
class CCASVD(CCAEY):
def _get_module(self, weights=None, k=None):
return SVDLoop(
weights=weights,
k=k,
learning_rate=self.learning_rate,
optimizer_... | 1,711 | 26.612903 | 75 | py |
cca_zoo | cca_zoo-main/cca_zoo/linear/_gradient/_ey.py | from typing import Union
import numpy as np
import torch
from cca_zoo.linear._gradient._gradient import GradientLoop
from cca_zoo.linear._iterative._base import BaseIterative
from cca_zoo.linear._pls import PLSMixin
class CCAEY(BaseIterative):
"""
A class used to fit Regularized CCA by Delta-EigenGame
... | 6,733 | 32.502488 | 184 | py |
cca_zoo | cca_zoo-main/cca_zoo/linear/_gradient/_gh.py | import torch
from cca_zoo.linear._gradient._ey import CCAEY, EYLoop
class CCAGH(CCAEY):
def _get_module(self, weights=None, k=None):
return GHALoop(
weights=weights,
k=k,
learning_rate=self.learning_rate,
optimizer_kwargs=self.optimizer_kwargs,
... | 2,223 | 33.75 | 85 | py |
cca_zoo | cca_zoo-main/cca_zoo/linear/_gradient/_gradient.py | import torch
from cca_zoo.linear._iterative._base import BaseLoop
class GradientLoop(BaseLoop):
def __init__(
self,
weights=None,
k=None,
tracking=False,
convergence_checking=False,
optimizer_kwargs=None,
learning_rate=1e-3,
):
super().__init__(... | 1,754 | 34.1 | 92 | py |
cca_zoo | cca_zoo-main/cca_zoo/linear/_iterative/_scca_pmd.py | import itertools
import warnings
import numpy as np
import torch
from cca_zoo.linear._iterative._base import (
BaseIterative,
BaseLoop,
supress_device_warnings,
)
from cca_zoo.linear._iterative._deflation import DeflationMixin
from cca_zoo.linear._pls import PLSMixin
from cca_zoo.linear._search import _de... | 7,252 | 34.038647 | 152 | py |
cca_zoo | cca_zoo-main/cca_zoo/linear/_iterative/_elasticnet.py | import warnings
from typing import Iterable, Union
import numpy as np
import torch
from sklearn.linear_model import ElasticNet, Lasso, Ridge, SGDRegressor
from cca_zoo.linear._iterative._base import (
BaseIterative,
BaseLoop,
supress_device_warnings,
)
from cca_zoo.linear._iterative._deflation import Defl... | 13,319 | 34.710456 | 167 | py |
cca_zoo | cca_zoo-main/cca_zoo/linear/_iterative/_base.py | # filter warnings from pytorch_lightning
import warnings
from abc import abstractmethod
from typing import Any, Iterable, List, Optional, Union
import numpy as np
import pytorch_lightning as pl
import torch
from lightning_fabric.utilities.warnings import PossibleUserWarning
from pytorch_lightning.callbacks import Call... | 12,321 | 32.483696 | 109 | py |
cca_zoo | cca_zoo-main/cca_zoo/linear/_iterative/_deflation.py | from typing import Iterable
import numpy as np
import pytorch_lightning as pl
from tqdm import tqdm
class DeflationMixin:
def _fit(self, views: Iterable[np.ndarray]):
# if views is a tuple then convert to a list
if isinstance(views, tuple):
views = list(views)
# tqdm for each ... | 2,459 | 35.716418 | 113 | py |
cca_zoo | cca_zoo-main/cca_zoo/linear/_iterative/_altmaxvar.py | from typing import Iterable, Union
import numpy as np
import torch
from cca_zoo.linear._iterative._base import BaseIterative, BaseLoop
from cca_zoo.utils import _process_parameter
class AltMaxVar(BaseIterative):
def __init__(
self,
latent_dimensions=1,
copy_data=True,
random_stat... | 7,635 | 31.355932 | 132 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/callbacks.py | from pytorch_lightning import Callback, LightningModule, Trainer
class CorrelationCallback(Callback):
def on_validation_epoch_end(
self, trainer: Trainer, pl_module: LightningModule
) -> None:
pl_module.log(
"val/corr",
pl_module.correlation(trainer.val_dataloaders[0]).... | 337 | 27.166667 | 68 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/objectives.py | import tensorly as tl
import torch
from tensorly.cp_tensor import cp_to_tensor
from tensorly.decomposition import parafac
def inv_sqrtm(A, eps=1e-9):
"""Compute the inverse square-root of a positive definite matrix."""
# Perform eigendecomposition of covariance matrix
U, S, V = torch.svd(A)
# Enforce ... | 8,808 | 33.143411 | 146 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/_base.py | from abc import abstractmethod
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import pytorch_lightning as pl
import torch
from torch.optim.lr_scheduler import CosineAnnealingLR, MultiStepLR
from cca_zoo._base import BaseModel
class BaseDeep(pl.LightningModule, BaseModel):
"""A bas... | 5,270 | 34.614865 | 88 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/architectures.py | from abc import abstractmethod
from math import sqrt
from typing import Iterable
import torch
from torch import nn
class _BaseEncoder(torch.nn.Module):
@abstractmethod
def __init__(self, latent_dimensions: int, variational: bool = False):
super(_BaseEncoder, self).__init__()
self.variational ... | 9,528 | 31.745704 | 112 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/metrics.py | from typing import Iterable
import torch
from torchmetrics import Metric
from cca_zoo.deep.objectives import _demean, inv_sqrtm
class MCCA(Metric):
def __init__(self):
super().__init__(dist_sync_on_step=False, compute_on_step=False)
self.add_state("representations", default=[], persistent=False)... | 1,931 | 30.672131 | 82 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/_discriminative/_dcca_noi.py | import torch
from ..objectives import inv_sqrtm
from ._dcca import DCCA
class DCCA_NOI(DCCA):
"""
A class used to fit a DCCA model by non-linear orthogonal iterations
References
----------
Wang, Weiran, et al. "Stochastic optimization for deep CCA via nonlinear orthogonal iterations." 2015 53rd... | 2,240 | 31.014286 | 202 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/_discriminative/_dcca.py | import torch
from cca_zoo.deep import objectives
from cca_zoo.deep._base import BaseDeep
from cca_zoo.deep.metrics import MCCA
class DCCA(BaseDeep):
"""
A class used to fit a DCCA model.
References
----------
Andrew, Galen, et al. "Deep canonical correlation analysis." International conference o... | 1,777 | 30.75 | 122 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/_discriminative/_dcca_gh.py | import torch
from ._dcca_ey import DCCA_EY
class DCCA_GH(DCCA_EY):
def get_AB(self, z):
A = torch.zeros(
self.latent_dimensions, self.latent_dimensions, device=z[0].device
) # initialize the cross-covariance matrix
B = torch.zeros(
self.latent_dimensions, self.lat... | 1,275 | 35.457143 | 85 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/_discriminative/_dcca_ey.py | import torch
from ._dcca import DCCA
class DCCA_EY(DCCA):
"""
References
----------
Chapman, James, Ana Lawry Aguila, and Lennie Wells. "A GeneralizedDeflation EigenGame with Extensions to Multiview Representation Learning." arXiv preprint arXiv:2211.11323 (2022).
"""
def __init__(self, lat... | 2,401 | 39.033333 | 184 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/_discriminative/_dcca_sdl.py | import torch
import torch.nn.functional as F
from ._dcca_noi import DCCA_NOI
class DCCA_SDL(DCCA_NOI):
"""
A class used to fit a Deep CCA by Stochastic Decorrelation model.
References
----------
Chang, Xiaobin, Tao Xiang, and Timothy M. Hospedales. "Scalable and effective deep CCA via soft decor... | 2,539 | 28.534884 | 200 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/_discriminative/_dcca_svd.py | import torch
from ._dcca import DCCA
class DCCA_SVD(DCCA):
"""
References
----------
Chapman, James, Ana Lawry Aguila, and Lennie Wells. "A GeneralizedDeflation EigenGame with Extensions to Multiview Representation Learning." arXiv preprint arXiv:2211.11323 (2022).
"""
def __init__(self, la... | 1,681 | 34.041667 | 184 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/_discriminative/_dcca_barlow_twins.py | import torch
from ._dcca import DCCA
class BarlowTwins(DCCA):
"""
A class used to fit a Barlow Twins model.
Barlow Twins is a self-supervised learning method that applies redundancy-reduction
to learn representations that are invariant to distortions of the input sample.
References
--------... | 2,342 | 36.190476 | 132 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/_generative/_splitae.py | import torch
from .._base import BaseDeep
from .._generative._base import _GenerativeMixin
from ..architectures import Encoder
class SplitAE(BaseDeep, _GenerativeMixin):
"""
A class used to fit a Split Autoencoder model.
References
----------
Ngiam, Jiquan, et al. "Multimodal deep learning." ICM... | 2,051 | 26 | 104 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/_generative/_dccae.py | import torch
from .. import objectives
from .._discriminative._dcca import DCCA
from .._generative._base import _GenerativeMixin
class DCCAE(DCCA, _GenerativeMixin):
"""
A class used to fit a DCCAE model.
References
----------
Wang, Weiran, et al. "On deep multi-view representation learning." In... | 2,481 | 27.528736 | 128 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/_generative/_dvcca.py | from typing import Iterable
import torch
from .._base import BaseDeep
from .._generative._base import _GenerativeMixin
class DVCCA(BaseDeep, _GenerativeMixin):
"""
A class used to fit a DVCCA model.
References
----------
Wang, Weiran, et al. 'Deep variational canonical correlation analysis.' ar... | 5,339 | 32.375 | 115 | py |
cca_zoo | cca_zoo-main/cca_zoo/deep/_generative/_base.py | from abc import abstractmethod
import torch
from torch.nn import functional as F
class _GenerativeMixin:
def recon_loss(self, x, recon, loss="mse", reduction="mean", **kwargs):
if loss == "mse":
return self.mse_loss(x, recon, reduction=reduction)
elif loss == "bce":
return... | 1,449 | 33.52381 | 83 | py |
cca_zoo | cca_zoo-main/cca_zoo/data/deep.py | import numpy as np
from torch.utils.data import DataLoader, Dataset
class NumpyDataset(Dataset):
"""
Class that turns numpy arrays into a torch dataset
"""
def __init__(self, views, labels=None):
"""
:param views: list/tuple of numpy arrays or array likes with the same number of rows... | 3,176 | 28.691589 | 106 | py |
cca_zoo | cca_zoo-main/test/test_deepmodels.py | import numpy as np
import pytorch_lightning as pl
from sklearn.utils.validation import check_random_state
from torch import manual_seed
from torch.utils.data import random_split
from cca_zoo.data.deep import NumpyDataset, check_dataset, get_dataloaders
from cca_zoo.deep import (
DCCA,
DCCA_EY,
DCCA_GH,
... | 13,070 | 30.345324 | 87 | py |
cca_zoo | cca_zoo-main/test/test_probabilistic.py | import numpy as np
import pytest
from cca_zoo.data.simulated import LinearSimulatedData
from cca_zoo.linear import CCA
def test_PCCA():
# some might not have access to jax/numpyro so leave this as an optional test locally.
numpyro = pytest.importorskip("numpyro")
from cca_zoo.probabilistic import Probabi... | 941 | 29.387097 | 96 | py |
cca_zoo | cca_zoo-main/test/test_stochastic.py | import numpy as np
import pytest
import scipy.sparse as sp
from sklearn.utils import check_random_state
from cca_zoo.linear import CCA, PLS
n = 50
rng = check_random_state(0)
X = rng.rand(n, 10)
Y = rng.rand(n, 11)
Z = rng.rand(n, 12)
X_sp = sp.random(n, 10, density=0.5, random_state=rng)
Y_sp = sp.random(n, 11, dens... | 4,087 | 28.839416 | 78 | py |
cca_zoo | cca_zoo-main/docs/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
import os
import sys
from types import ModuleType
sys.path.insert(0, os.path.abspath("."))
sys.path.insert(0, os.path... | 2,501 | 31.493506 | 87 | py |
cca_zoo | cca_zoo-main/docs/source/examples/plot_dvcca.py | """
Deep Variational CCA and Deep Canonically Correlated Autoencoders
====================================================================
This example demonstrates multiview linear which can reconstruct their inputs
"""
# import matplotlib.pyplot as plt
# import pytorch_lightning as pl
#
# from cca_zoo.deep import DC... | 5,627 | 26.724138 | 78 | py |
cca_zoo | cca_zoo-main/docs/source/examples/plot_dcca_multi.py | """
Deep CCA for more than 2 views
=================================
This example demonstrates how to easily train Deep CCA linear and variants
"""
import pytorch_lightning as pl
from cca_zoo.deep import DCCA, DTCCA, architectures, objectives
# %%
# Data
# -----
from docs.source.examples import example_mnist_data
... | 1,370 | 23.927273 | 87 | py |
cca_zoo | cca_zoo-main/docs/source/examples/plot_dcca.py | """
Deep CCA
===========================
This example demonstrates how to easily train Deep CCA linear and variants
using cca_zoo, a library for canonical correlation analysis and related methods.
"""
# %%
# Imports
# -------
import pytorch_lightning as pl
from matplotlib import pyplot as plt
from cca_zoo.deep impor... | 4,431 | 32.323308 | 100 | py |
cca_zoo | cca_zoo-main/docs/source/examples/plot_dcca_custom_data.py | """
Custom Datasets
===========================
This example demonstrates how to use your own multiview datasets with CCA-Zoo.
"""
import numpy as np
# %%
# Imports
# -----
import pytorch_lightning as pl
# %% NumpyDataset
# --------------------------------------------------------------
# This is arguably the easies... | 2,795 | 30.772727 | 116 | py |
cca_zoo | cca_zoo-main/docs/source/examples/__init__.py | import numpy as np
from multiviewdata.torchdatasets import NoisyMNIST, SplitMNIST
from torch.utils.data import Subset
from cca_zoo.data.deep import get_dataloaders
def example_mnist_data(n_train, n_val, batch_size=50, val_batch_size=10, type="split"):
if type == "split":
train_dataset = SplitMNIST(
... | 1,125 | 37.827586 | 88 | py |
PiMAE | PiMAE-main/Pretrain/main.py | from tools import pretrain_run_net as pretrain
from utils import parser, dist_utils, misc
from utils.logger import *
from utils.config import *
import time
import os
import torch
from tensorboardX import SummaryWriter
import wandb
os.environ["WANDB_API_KEY"] = "your-api-key"
def main():
# args
args = parser.g... | 3,198 | 38.012195 | 123 | py |
PiMAE | PiMAE-main/Pretrain/main_vis.py | # from tools import run_net
from tools import test_net
from utils import parser, dist_utils, misc
from utils.logger import *
from utils.config import *
import time
import os
import torch
from tensorboardX import SummaryWriter
def main():
# args
args = parser.get_args()
# CUDA
args.use_gpu = torch.cuda.... | 2,550 | 33.472973 | 123 | py |
PiMAE | PiMAE-main/Pretrain/tutorial_load.py | """
Modified from 3DETR, which is based on DETR. The weights should work fine on DETR as well.
"""
import argparse
import os
from typing import Optional
import torch
from torch import Tensor, nn
from functools import partial
import copy
# model definition
def get_clones(module, N):
return nn.ModuleList([copy.deepc... | 11,211 | 36.878378 | 135 | py |
PiMAE | PiMAE-main/Pretrain/tools/mae_visualize.py | import sys
import os
import torch
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import cv2
from torchvision import transforms, utils
imagenet_mean = np.array([0.485, 0.456, 0.406])
imagenet_std = np.array([0.229, 0.224, 0.225])
def show_image(image, title='', path=None):
# image is... | 3,354 | 25.84 | 96 | py |
PiMAE | PiMAE-main/Pretrain/tools/runner_pretrain.py | import torch
import torch.nn as nn
import os
import json
from tools import builder
from utils import misc, dist_utils
import time
from utils.logger import *
from utils.AverageMeter import AverageMeter
from sklearn.svm import LinearSVC
import numpy as np
from torchvision import transforms
from datasets import data_tran... | 10,920 | 37.185315 | 236 | py |
PiMAE | PiMAE-main/Pretrain/tools/runner.py | import torch
import torch.nn as nn
import os
import json
from tools import builder
from utils import misc, dist_utils
import time
from PIL import Image
from utils.logger import *
import cv2
import numpy as np
from torch.utils.data import DataLoader, DistributedSampler
from torchvision import transforms
from datasets ... | 7,230 | 33.433333 | 116 | py |
PiMAE | PiMAE-main/Pretrain/tools/builder.py | import os, sys
# online package
import torch
# optimizer
import torch.optim as optim
# dataloader
# from datasets import build_dataset_from_cfg
from models import build_model_from_cfg
# utils
from utils.logger import *
from utils.misc import *
from timm.scheduler import CosineLRScheduler
def dataset_builder(args, conf... | 7,411 | 44.195122 | 126 | py |
PiMAE | PiMAE-main/Pretrain/tools/runner_vis.py | import torch
import torch.nn as nn
import os
import json
from tools import builder
from utils import misc, dist_utils
import time
from PIL import Image
from utils.logger import *
import cv2
import numpy as np
from torch.utils.data import DataLoader, DistributedSampler
from torchvision import transforms
from datasets ... | 14,103 | 35.825065 | 162 | py |
PiMAE | PiMAE-main/Pretrain/third_party/models_mae.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# timm: https://github.com/rwightman/pytorch-image... | 12,032 | 38.844371 | 154 | py |
PiMAE | PiMAE-main/Pretrain/third_party/util/pos_embed.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# Position embedding utils
# -----------------------------------... | 4,092 | 41.195876 | 107 | py |
PiMAE | PiMAE-main/Pretrain/models/pimae.py | from timm.models.vision_transformer import Block
import torch.nn as nn
import torch
import random
import utils.matcher
import numpy as np
import cv2
import torch.nn.functional as F
from collections import OrderedDict
from functools import partial
from typing import Dict, List, Optional, Union
from .multimae_utils imp... | 22,697 | 39.604651 | 163 | py |
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