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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mixture-of-diffusers | mixture-of-diffusers-master/generate_grid_from_json.py | import argparse
import datetime
from diffusers import LMSDiscreteScheduler, DDIMScheduler
import json
from pathlib import Path
import torch
from mixdiff.tiling import StableDiffusionTilingPipeline
def generate_grid(generation_arguments):
model_id = "CompVis/stable-diffusion-v1-4"
# Prepared scheduler
if g... | 2,454 | 49.102041 | 145 | py |
mixture-of-diffusers | mixture-of-diffusers-master/mixdiff/canvas.py | from copy import deepcopy
from dataclasses import asdict, dataclass
from enum import Enum
import numpy as np
from numpy import pi, exp, sqrt
import re
import torch
from torchvision.transforms.functional import resize
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
... | 20,181 | 49.836272 | 224 | py |
mixture-of-diffusers | mixture-of-diffusers-master/mixdiff/tiling.py | from enum import Enum
import inspect
from ligo.segments import segment
from typing import List, Optional, Tuple, Union
import torch
from tqdm.auto import tqdm
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.pi... | 15,330 | 52.232639 | 218 | py |
mixture-of-diffusers | mixture-of-diffusers-master/mixdiff/imgtools.py | import numpy as np
import torch
from PIL import Image, ImageFilter
def preprocess_image(image):
"""Preprocess an input image
Same as https://github.com/huggingface/diffusers/blob/1138d63b519e37f0ce04e027b9f4a3261d27c628/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L44
... | 1,458 | 39.527778 | 180 | py |
ACME | ACME-master/test.py | import os
import time
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import torch.backends.cudnn as cudnn
from ... | 5,828 | 28.145 | 119 | py |
ACME | ACME-master/args.py | import argparse
def get_parser():
parser = argparse.ArgumentParser(description='tri-joint parameters')
# general
parser.add_argument('--seed', default=1234, type=int)
parser.add_argument('--device', default=[0], type=list)
# data
parser.add_argument('--img_path', default='../im2recipe-Pytorch... | 2,906 | 47.45 | 119 | py |
ACME | ACME-master/data_loader.py | from __future__ import print_function
import torch.utils.data as data
from PIL import Image
import os
import sys
import pickle
import numpy as np
import lmdb
import torch
import pdb
import torchvision.transforms as transforms
import nltk
from build_vocab import Vocabulary
from args import get_parser
parser = get_pars... | 6,119 | 35 | 115 | py |
ACME | ACME-master/triplet_loss.py | from __future__ import print_function
import torch
from torch import nn
from torch.autograd import Variable
class TripletLoss(object):
"""Modified from Tong Xiao's open-reid (https://github.com/Cysu/open-reid).
Related Triplet Loss theory can be found in paper 'In Defense of the Triplet
Loss for Person Re-Iden... | 3,225 | 30.940594 | 79 | py |
ACME | ACME-master/models.py | import torch
import torch.nn as nn
import torch.nn.parallel
import torch.legacy as legacy
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import torchwordemb
from args import get_parser
import pdb
import t... | 21,061 | 34.698305 | 121 | py |
ACME | ACME-master/train.py | import os
import time
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import torch.backends.cudnn as cudnn
from ... | 16,687 | 34.430998 | 120 | py |
ACME | ACME-master/build_vocab.py | import nltk
import pickle
import argparse
from collections import Counter
class Vocabulary(object):
"""Simple vocabulary wrapper."""
def __init__(self):
self.word2idx = {}
self.idx2word = {}
self.idx = 0
def add_word(self, word):
if not word in self.word2idx:
s... | 2,783 | 31.752941 | 89 | py |
czsl | czsl-main/test.py | # Torch imports
import torch
from torch.utils.tensorboard import SummaryWriter
import torch.backends.cudnn as cudnn
import numpy as np
from flags import DATA_FOLDER
cudnn.benchmark = True
# Python imports
import tqdm
from tqdm import tqdm
import os
from os.path import join as ospj
# Local imports
from data import d... | 6,227 | 32.12766 | 120 | py |
czsl | czsl-main/train.py | # Torch imports
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
import torch.backends.cudnn as cudnn
cudnn.benchmark = True
# Python imports
import tqdm
from tqdm import tqdm
import os
from os.path import join as ospj
import csv
#Local imports
from... | 7,551 | 31.13617 | 120 | py |
czsl | czsl-main/models/svm.py | import numpy as np
import tqdm
from data import dataset as dset
import os
from utils import utils
import torch
from torch.autograd import Variable
import h5py
from sklearn.svm import LinearSVC
from sklearn.model_selection import GridSearchCV
import torch.nn.functional as F
from joblib import Parallel, delayed
import gl... | 9,922 | 40.345833 | 154 | py |
czsl | czsl-main/models/visual_product.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import numpy as np
from .common import MLP
class VisualProductNN(nn.Module):
def __init__(self, dset, args):
super(VisualProductNN, self).__init__()
self.attr_clf = MLP(dset.feat_dim, len(dset.att... | 1,457 | 29.375 | 78 | py |
czsl | czsl-main/models/symnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .word_embedding import load_word_embeddings
from .common import MLP
device = 'cuda' if torch.cuda.is_available() else 'cpu'
class Symnet(nn.Module):
def __init__(self, dset, args):
super(Symnet, self).__init__()
... | 9,135 | 41.691589 | 188 | py |
czsl | czsl-main/models/manifold_methods.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .word_embedding import load_word_embeddings
from .common import MLP, Reshape
from flags import DATA_FOLDER
device = 'cuda' if torch.cuda.is_available() else 'cpu'
class ManifoldModel(nn.Module):
def __init__(self, dset, ar... | 15,296 | 40.681199 | 118 | py |
czsl | czsl-main/models/common.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import copy
from scipy.stats import hmean
device = 'cuda' if torch.cuda.is_available() else 'cpu'
class MLP(nn.Module):
'''
Baseclass to create a simple MLP
Inputs
inp_dim: Int, Input dimension
out-dim: I... | 20,689 | 39.174757 | 144 | py |
czsl | czsl-main/models/gcn.py | import numpy as np
import scipy.sparse as sp
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_
device = 'cuda' if torch.cuda.is_available() else 'cpu'
def normt_spm(mx, method='in'):
if method == 'in':
mx = mx.transpose()
rows... | 5,869 | 27.634146 | 122 | py |
czsl | czsl-main/models/word_embedding.py | import torch
import numpy as np
from flags import DATA_FOLDER
def load_word_embeddings(emb_type, vocab):
if emb_type == 'glove':
embeds = load_glove_embeddings(vocab)
elif emb_type == 'fasttext':
embeds = load_fasttext_embeddings(vocab)
elif emb_type == 'word2vec':
embeds = load_word... | 6,636 | 35.26776 | 90 | py |
czsl | czsl-main/models/modular_methods.py | # Copyright (c) Facebook, Inc. and its 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.
#
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as tmodels
import numpy as np... | 23,151 | 34.454824 | 90 | py |
czsl | czsl-main/models/compcos.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .word_embedding import load_word_embeddings
from .common import MLP
from itertools import product
device = 'cuda' if torch.cuda.is_available() else 'cpu'
def compute_cosine_similarity(names, weights, return_dict=True):
pairing_names = list(p... | 10,133 | 36.533333 | 111 | py |
czsl | czsl-main/models/graph_method.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from .common import MLP
from .gcn import GCN, GCNII
from .word_embedding import load_word_embeddings
import scipy.sparse as sp
def adj_to_edges(adj):
# Adj sparse matrix to list of edges
rows, cols = np.nonzero(adj)
edge... | 7,321 | 33.701422 | 151 | py |
czsl | czsl-main/models/image_extractor.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import models
from torchvision.models.resnet import ResNet, BasicBlock
class ResNet18_conv(ResNet):
def __init__(self):
super(ResNet18_conv, self).__init__(BasicBlock, [2, 2, 2, 2])
def forward(self, x):
... | 2,428 | 29.3625 | 99 | py |
czsl | czsl-main/utils/reorganize_utzap.py | # Copyright (c) Facebook, Inc. and its 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.
#
"""
Reorganize the UT-Zappos dataset to resemble the MIT-States dataset
root/attr_obj/img1.jpg
root/attr_obj/img2.jpg
root... | 835 | 25.125 | 73 | py |
czsl | czsl-main/utils/config_model.py | import torch
import torch.optim as optim
from models.image_extractor import get_image_extractor
from models.visual_product import VisualProductNN
from models.manifold_methods import RedWine, LabelEmbedPlus, AttributeOperator
from models.modular_methods import GatedGeneralNN
from models.graph_method import GraphFull
fr... | 3,177 | 37.289157 | 123 | py |
czsl | czsl-main/utils/utils.py | import os
from os.path import join as ospj
import torch
import random
import copy
import shutil
import sys
import yaml
def chunks(l, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(l), n):
yield l[i:i + n]
def get_norm_values(norm_family = 'imagenet'):
'''
Inputs
... | 1,943 | 28.454545 | 111 | py |
czsl | czsl-main/data/dataset.py | #external libs
import numpy as np
from tqdm import tqdm
from PIL import Image
import os
import random
from os.path import join as ospj
from glob import glob
#torch libs
from torch.utils.data import Dataset
import torch
import torchvision.transforms as transforms
#local libs
from utils.utils import get_norm_values, chu... | 15,653 | 34.336343 | 106 | py |
MotifClass | MotifClass-master/text_classification/main.py | # The code structure is adapted from the WeSTClass implementation
# https://github.com/yumeng5/WeSTClass
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import numpy as np
np.random.seed(1234)
from time import time
from model import WSTC, f1
from keras.optimizers import... | 7,853 | 36.759615 | 137 | py |
MotifClass | MotifClass-master/text_classification/model.py | import numpy as np
np.random.seed(1234)
import os
from time import time
import csv
import keras.backend as K
# K.set_session(K.tf.Session(config=K.tf.ConfigProto(intra_op_parallelism_threads=30, inter_op_parallelism_threads=30)))
from keras.engine.topology import Layer
from keras.layers import Dense, Input, Convolution... | 9,038 | 32.354244 | 124 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/attack_csgld_pgd_torch.py | """
Implementation of the attacks used in the article
"""
import numpy as np
import pandas as pd
import torch
import argparse
import time
import os
import sys
import re
from tqdm import tqdm
import random
from random import shuffle
from utils.data import CIFAR10, CIFAR100, ImageNet, MNIST
from utils.helpers import key... | 24,661 | 61.753181 | 254 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/compute_accuracy.py | import argparse
import torch
import torch.nn.functional as F
import numpy as np
import pandas as pd
import os
import sys
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from utils.helpers import list_models, guess_and_load_model, guess_method
from utils.data import ImageNet
def nl... | 4,542 | 37.82906 | 163 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/lgv/imagenet/analyse_weights_space.py | import pandas as pd
import random
import os
import argparse
from tqdm import tqdm
import numpy as np
import torch
from torchvision import models as tmodels
import torchvision.datasets as datasets
import torchvision.transforms as transforms
#from pyhessian import hessian
from utils.data import ImageNet
from utils.helper... | 13,744 | 54.873984 | 179 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/lgv/imagenet/analyse_feature_space.py | """
Interpolate between adv ex from 2 surrogate in feature space
"""
import os
import sys
import torch
import math
import random
import argparse
import numpy as np
import pandas as pd
from math import sqrt
from tqdm import tqdm
from utils.n_sphere import convert_spherical, convert_rectangular
from utils.data import CIF... | 14,705 | 54.91635 | 215 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/lgv/imagenet/generate_parametric_path.py | import os
import argparse
from tqdm import tqdm
import numpy as np
import torch
from torchvision import models as tmodels
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from utils.data import ImageNet
from utils.helpers import guess_and_load_model, guess_model
from utils.pca_weights... | 4,285 | 46.622222 | 167 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/lgv/imagenet/train_swag_imagenet.py | import argparse
import os
import random
import sys
import time
import tabulate
from collections import OrderedDict
import torch
import torch.nn.functional as F
import torchvision.models
import timm
from utils.swag import data
from utils.subspace_inference import utils, losses
#from utils.swag.posteriors import SWAG
... | 12,515 | 29.378641 | 128 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/lgv/imagenet/generate_noisy_models.py | import pandas as pd
import os
from pathlib import Path
import argparse
import random
from tqdm import tqdm
import numpy as np
import torch
from torchvision import models as tmodels
import torchvision.datasets as datasets
import torchvision.transforms as transforms
from utils.data import ImageNet
from utils.helpers impo... | 7,194 | 56.103175 | 201 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/lgv/imagenet/hessian/compute_hessian.py | #*
# @file Different utility functions
# Copyright (c) Zhewei Yao, Amir Gholami
# All rights reserved.
# This file is part of PyHessian library.
#
# PyHessian is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, ei... | 4,571 | 30.531034 | 78 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/lgv/imagenet/hessian/utils_hessian.py | #*
# @file Different utility functions
# Copyright (c) Zhewei Yao, Amir Gholami
# All rights reserved.
# This file is part of PyHessian library.
#
# PyHessian is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, ei... | 5,701 | 39.728571 | 75 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/modelsghostpreresnet.py | """
PreResNet model definition
ported from https://github.com/bearpaw/pytorch-classification/blob/master/models/cifar/preresnet.py
-----
Adapted to add skip connection erosion
Do not use to train a model. Only for inference. Train on regular PreResNet
"""
import torch
import torch.nn as nn
import t... | 6,999 | 32.653846 | 141 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/n_sphere.py | # N-sphere Convert to Spherical or Rectangular Coordination
# improve n-sphere package with numerical stability and basic vectorization: https://pypi.org/project/n-sphere/
import numpy as np
import math
import torch
SUPPORTED_TYPES = ['Tensor', 'ndarray', 'list']
def convert_spherical(input, digits=6, tol=1e-8):
... | 3,609 | 37.817204 | 111 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/pca_weights.py | import torch
from sklearn.decomposition import PCA
from utils.subspace_inference.utils import flatten, bn_update
def model2vector(model):
"""
Transform a pytorch model into its weight Tensor
:param model: pytorch model
:return: tensor of size (n_weights,)
"""
w = flatten([param.detach().cpu() ... | 4,046 | 34.191304 | 120 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/optimizers.py | """
File adapted from https://github.com/JavierAntoran/Bayesian-Neural-Networks
"""
from torch.optim.optimizer import Optimizer, required
import numpy as np
import torch
class SGLD(Optimizer):
"""
SGLD optimiser based on pytorch's SGD.
Note that the weight decay is specified in terms of the gaussian prio... | 3,430 | 30.190909 | 111 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/data.py | import os
import logging
import torch
import torchvision
import torchvision.datasets as datasets
import numpy as np
from torchvision import transforms
from .helpers import list_models, guess_and_load_model, DEVICE
def check_args(method):
def inner(ref, **kwargs):
if kwargs.get('validation', False) and not... | 14,626 | 45.582803 | 130 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/utils_sgm.py | """
Code from the following paper:
@inproceedings{wu2020skip,
title={Skip connections matter: On the transferability of adversarial examples generated with resnets},
author={Wu, Dongxian and Wang, Yisen and Xia, Shu-Tao and Bailey, James and Ma, Xingjun},
booktitle={ICLR},
year={2020}
}
https://github.c... | 2,861 | 35.692308 | 107 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/layers.py | import torch
from PIL import Image
from torchvision.transforms import functional as F
class RandomResizePad(torch.nn.Module):
def __init__(self, min_resize):
super().__init__()
self.min_resize = min_resize
def forward(self, img):
size_original = img.size()
if size_original[-1]... | 1,083 | 40.692308 | 96 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/modelsghost.py | # adapted from torchvision ResNet implementation https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
# Add skip connection erosion
# do not use to train a model. Only for inference. Train on regular torchvision resnet
import torch
from torch import Tensor
import torch.nn as nn
try:
from torc... | 17,173 | 40.383133 | 141 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/models.py | import torch
from torch import nn
import torch.nn.functional as F
from random import randrange, shuffle
class ModelWithTemperature(nn.Module):
"""
A thin decorator, which wraps a model with temperature scaling.
Code adapted from https://github.com/gpleiss/temperature_scaling/blob/master/temperature_scalin... | 7,591 | 32.444934 | 147 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/helpers.py | import os
import re
import glob
import argparse
import torch
import numpy as np
from collections import OrderedDict
try:
from art.classifiers import PyTorchClassifier
except ModuleNotFoundError:
from art.estimators.classification import PyTorchClassifier
from .models import TorchEnsemble, CifarLeNet, MnistCnn, ... | 28,359 | 45.79868 | 287 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/attacks.py | import torch
import os
import numpy as np
import scipy.stats as st
from art.attacks.evasion import FastGradientMethod, ProjectedGradientDescentPyTorch
from art.classifiers import PyTorchClassifier
from art.config import ART_NUMPY_DTYPE
from art.utils import (
random_sphere,
projection,
)
from tqdm import tqdm
f... | 23,513 | 45.562376 | 174 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/swag/losses.py | import torch
import torch.nn.functional as F
def cross_entropy(model, input, target):
# standard cross-entropy loss function
output = model(input)
loss = F.cross_entropy(output, target)
return loss, output
def adversarial_cross_entropy(
model, input, target, lossfn=F.cross_entropy, epsilon=0.... | 3,094 | 28.47619 | 97 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/swag/utils.py | import itertools
import torch
import os
import copy
from datetime import datetime
import math
import numpy as np
import tqdm
import torch.nn.functional as F
def flatten(lst):
tmp = [i.contiguous().view(-1, 1) for i in lst]
return torch.cat(tmp).view(-1)
def unflatten_like(vector, likeTensorList):
# Tak... | 7,489 | 26.740741 | 86 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/swag/data.py | """
separate data loader for imagenet
"""
import os
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
def loaders(path, batch_size, num_workers, shuffle_train=True):
train_dir = os.path.join(path, "train")
# vali... | 1,851 | 25.84058 | 153 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/swag/posteriors/swag.py | """
implementation of SWAG
"""
import torch
import numpy as np
import itertools
from torch.distributions.normal import Normal
import copy
import gpytorch
from gpytorch.lazy import RootLazyTensor, DiagLazyTensor, AddedDiagLazyTensor
from gpytorch.distributions import MultivariateNormal
from ..utils import flatten... | 11,331 | 34.63522 | 88 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/losses.py | import torch
import torch.nn.functional as F
class GaussianLikelihood:
"""
Minus Gaussian likelihood for regression problems.
Mean squared error (MSE) divided by `2 * noise_var`.
"""
def __init__(self, noise_var = 0.5):
self.noise_var = noise_var
self.mse = torch.nn.functiona... | 4,258 | 29.862319 | 97 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/utils.py | import itertools
import torch
import os
import copy
from datetime import datetime
import math
import numpy as np
import tqdm
from collections import defaultdict
from time import gmtime, strftime
import sys
import torch.nn.functional as F
def get_logging_print(fname):
cur_time = strftime("%m-%d_%H:%M:%S", gmtime(... | 9,479 | 28.349845 | 110 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/data.py | import numpy as np
import torch
import torchvision
import os
c10_classes = np.array([
[0, 1, 2, 8, 9],
[3, 4, 5, 6, 7]
], dtype=np.int32)
def camvid_loaders(path, batch_size, num_workers, transform_train, transform_test,
use_validation, val_size, shuffle_train=True,
joint_tra... | 9,867 | 39.77686 | 165 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/models/preresnet.py | """
PreResNet model definition
ported from https://github.com/bearpaw/pytorch-classification/blob/master/models/cifar/preresnet.py
"""
import torch.nn as nn
import torchvision.transforms as transforms
import math
__all__ = ['PreResNet110', 'PreResNet56', 'PreResNet8', 'PreResNet83', 'PreResNet164']
def conv... | 7,349 | 30.410256 | 103 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/models/regression_net.py | import math
import torch
import torch.nn as nn
import torchvision.transforms as transforms
try:
import os
os.sys.path.append("/home/izmailovpavel/Documents/Projects/curves/")
import curves
except:
pass
__all__ = [
'RegNet',
'ToyRegNet',
]
class MDropout(torch.nn.Module):
def __init__(self... | 4,672 | 31.006849 | 125 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/models/vgg.py | """
VGG model definition
ported from https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
"""
import math
import torch.nn as nn
import torchvision.transforms as transforms
__all__ = ['VGG16', 'VGG16BN', 'VGG19', 'VGG19BN']
def make_layers(cfg, batch_norm=False):
layers = list()
in... | 2,841 | 27.707071 | 105 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/models/mlp.py | import torch.nn as nn
import torchvision.transforms as transforms
import torch
__all__=['MLP', 'MLPBoston']
class MLPBase(nn.Module):
def __init__(self, num_classes=0, in_dim=1, layers=2, hidden=7):
super(MLPBase, self).__init__()
out_layer_list = [hidden for i in range(layers)]
if num_cl... | 1,419 | 27.979592 | 68 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/models/layers.py | """
layer definitions for 100-layer tiramisu
#from: https://github.com/bfortuner/pytorch_tiramisu
"""
import torch
import torch.nn as nn
class DenseLayer(nn.Sequential):
def __init__(self, in_channels, growth_rate):
super().__init__()
self.add_module('norm', nn.BatchNorm2d(in_channels))
... | 3,117 | 33.644444 | 82 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/models/wide_resnet.py | """
WideResNet model definition
ported from https://github.com/meliketoy/wide-resnet.pytorch/blob/master/networks/wide_resnet.py
"""
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import math
__all__ = ['WideResNet28x10']
def co... | 3,660 | 32.587156 | 100 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/models/vgg_dropout.py | """
VGG model definition
ported from https://github.com/pytorch/vision/blob/master/torchvision/models/vgg.py
"""
import math
import torch.nn as nn
import torchvision.transforms as transforms
__all__ = ['VGG16Drop', 'VGG16BNDrop', 'VGG19Drop', 'VGG19BNDrop']
P = 0.05
def make_layers(cfg, batch_norm=False):
... | 2,927 | 27.990099 | 105 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/models/wide_resnet_dropout.py | """
WideResNet model definition
ported from https://github.com/meliketoy/wide-resnet.pytorch/blob/master/networks/wide_resnet.py
"""
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import math
__all__ = ['WideResNet28x10Drop']
P =... | 3,640 | 31.508929 | 100 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/models/preresnet_dropout.py | """
PreResNet model definition
ported from https://github.com/bearpaw/pytorch-classification/blob/master/models/cifar/preresnet.py
"""
import torch.nn as nn
import torchvision.transforms as transforms
import math
__all__ = ['PreResNet110Drop', 'PreResNet56Drop', 'PreResNet8Drop', 'PreResNet164Drop']
P = 0.01... | 7,057 | 30.092511 | 103 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/posteriors/ess.py | import torch
import numpy as np
from .elliptical_slice import elliptical_slice, slice_sample
from .proj_model import ProjectedModel
class EllipticalSliceSampling(torch.nn.Module):
def __init__(self, base, subspace, var, loader, criterion, num_samples = 20,
use_cuda = False, method='elliptical', *args, **... | 4,179 | 35.347826 | 126 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/posteriors/inferences.py | """
inferences class w/in the subspace
currently only fitting the Gaussian associated is implemented
"""
import abc
import torch
import numpy as np
from torch.distributions import LowRankMultivariateNormal
from .elliptical_slice import elliptical_slice
from ..utils import unflatten_like, flatten, train_epoch
... | 5,185 | 31.21118 | 145 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/posteriors/vinf_model.py | import math
import torch
from ..utils import set_weights
class VINFModel(torch.nn.Module):
def __init__(self, base, subspace, flow,
prior_log_sigma=1.0, *args, **kwargs):
super(VINFModel, self).__init__()
self.base_model = base(*args, **kwargs)
self.flow = flow
... | 2,662 | 33.584416 | 103 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/posteriors/swag.py | import torch
from ..utils import flatten, set_weights
from .subspaces import Subspace
class SWAG(torch.nn.Module):
def __init__(self, base, subspace_type,
subspace_kwargs=None, var_clamp=1e-6, *args, **kwargs):
super(SWAG, self).__init__()
self.base_model = base(*args, **kwargs... | 3,487 | 34.232323 | 96 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/posteriors/subspaces.py | """
subspace classes
CovarianceSpace: covariance subspace
PCASpace: PCA subspace
FreqDirSpace: Frequent Directions Space
"""
import abc
import torch
import numpy as np
from sklearn.decomposition import TruncatedSVD
from sklearn.decomposition._pca import _assess_dimension
from sklearn.utils.extmath i... | 7,016 | 35.357513 | 121 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/posteriors/realnvp.py | import math
import numpy as np
import torch
from torch import nn
from torch import distributions
class RealNVP(nn.Module):
def __init__(self, nets, nett, masks, prior, device=None):
super().__init__()
self.prior = prior
self.mask = nn.Parameter(masks, requires_grad=False)
self.t =... | 3,244 | 32.112245 | 101 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/posteriors/proj_model.py | import torch
from ..utils import unflatten_like
class SubspaceModel(torch.nn.Module):
def __init__(self, mean, cov_factor):
super(SubspaceModel, self).__init__()
self.rank = cov_factor.size(0)
self.register_buffer('mean', mean)
self.register_buffer('cov_factor', cov_factor)
def... | 1,329 | 32.25 | 97 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/posteriors/pyro.py | import numpy as np
import torch
import pyro
import pyro.distributions as dist
from pyro.infer.mcmc import NUTS, MCMC
from pyro.nn import AutoRegressiveNN
from ..utils import extract_parameters
from ..utils import set_weights_old as set_weights
class PyroModel(torch.nn.Module):
def __init__(self,
... | 6,618 | 37.707602 | 116 | py |
lgv-geometric-transferability | lgv-geometric-transferability-main/utils/subspace_inference/posteriors/vi_model.py | import math
import torch
from ..utils import extract_parameters, train_epoch
from ..utils import set_weights_old as set_weights
class VIModel(torch.nn.Module):
def __init__(self, base, subspace, init_inv_softplus_sigma=-3.0,
prior_log_sigma=3.0, eps=1e-6, with_mu=True, *args, **kwargs):
... | 4,676 | 38.635593 | 139 | py |
ActiveVisionManipulation | ActiveVisionManipulation-master/HER/envs/fakercnn_pusher.py | from HER.envs import bb_pusher
import numpy as np
from HER.rcnn import renderer
import random
from keras import backend as K
from HER.rcnn import load_rcnn
import tensorflow as tf
from HER.envs.pusher import _tuple
from ipdb import set_trace as st
class BaxterEnv(bb_pusher.BaxterEnv):
def __init__(self, *args, a... | 4,861 | 32.531034 | 97 | py |
ActiveVisionManipulation | ActiveVisionManipulation-master/HER/rcnn/Mask_RCNN/parallel_model.py | """
Mask R-CNN
Multi-GPU Support for Keras.
Copyright (c) 2017 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by Waleed Abdulla
Ideas and a small code snippets from these sources:
https://github.com/fchollet/keras/issues/2436
https://medium.com/@kuza55/transparent-multi-gpu-training... | 6,863 | 38.448276 | 95 | py |
ActiveVisionManipulation | ActiveVisionManipulation-master/HER/rcnn/Mask_RCNN/model.py | """
Mask R-CNN
The main Mask R-CNN model implemenetation.
Copyright (c) 2017 Matterport, Inc.
Licensed under the MIT License (see LICENSE for details)
Written by Waleed Abdulla
"""
import os
import sys
import glob
import random
import math
import datetime
import itertools
import json
import re
import logging
from col... | 111,322 | 42.553599 | 115 | py |
SimPer | SimPer-main/src/simper.py | """
Minimal SimPer implementation & example training loops.
"""
import tensorflow as tf
from networks import Featurizer, Classifier
@tf.function
def _max_cross_corr(feats_1, feats_2):
# feats_1: 1 x T(# time stamp)
# feats_2: M(# aug) x T(# time stamp)
feats_2 = tf.cast(feats_2, feats_1.dtype)
feats_1... | 5,269 | 35.344828 | 91 | py |
SimPer | SimPer-main/src/networks.py | """
Example network architectures:
- Featurizer (for representation learning)
- Classifier (for downstream tasks)
"""
import tensorflow as tf
from tensorflow.keras.layers import (Conv2D, Conv3D, Dense, Flatten, BatchNormalization,
TimeDistributed, MaxPool2D, GlobalAveragePooling2D)
... | 2,324 | 26.678571 | 88 | py |
sampling_cf | sampling_cf-main/main.py | import os
import time
import importlib
import datetime as dt
from tqdm import tqdm
from utils import file_write, log_end_epoch, INF, valid_hyper_params
from data_path_constants import get_log_file_path, get_model_file_path
# NOTE: No global-level torch imports as the GPU-ID is set through code
def train(model, crite... | 10,944 | 39.238971 | 114 | py |
sampling_cf | sampling_cf-main/data_genie.py | from data_genie.data_genie_config import *
from data_genie.data_genie_trainers import *
from data_genie.data_genie_data import OracleData
from data_genie.data_genie_model import PointwiseDataGenie, PairwiseDataGenie
# NOTE: Please edit the config in `data_genie/data_genie_config.py` before \
# running this trainer s... | 2,771 | 34.088608 | 124 | py |
sampling_cf | sampling_cf-main/loss.py | import torch
import torch.nn.functional as F
from torch_utils import is_cuda_available
class CustomLoss(torch.nn.Module):
def __init__(self, hyper_params):
super(CustomLoss, self).__init__()
self.forward = {
'explicit': self.mse,
'implicit': self.bpr,
'sequentia... | 3,395 | 36.318681 | 94 | py |
sampling_cf | sampling_cf-main/torch_utils.py | import torch
is_cuda_available = torch.cuda.is_available()
if is_cuda_available:
print("Using CUDA...\n")
LongTensor = torch.cuda.LongTensor
FloatTensor = torch.cuda.FloatTensor
BoolTensor = torch.cuda.BoolTensor
else:
LongTensor = torch.LongTensor
FloatTensor = torch.FloatTensor
BoolTens... | 827 | 25.709677 | 59 | py |
sampling_cf | sampling_cf-main/eval.py | import torch
import numpy as np
from numba import jit, float32, float64, int64
from utils import INF
def evaluate(model, criterion, reader, hyper_params, item_propensity, topk = [ 10, 100 ], test = False):
metrics = {}
# Do a negative sampled item-space evaluation (only on the validation set)
# if the da... | 7,384 | 38.704301 | 137 | py |
sampling_cf | sampling_cf-main/svp_handler.py | import numpy as np
from collections import defaultdict
from main import main_pytorch
from data_path_constants import get_svp_log_file_path, get_svp_model_file_path
class SVPHandler:
def __init__(self, model_type, loss_type, hyper_params):
hyper_params['model_type'] = model_type
hyper_params['task'... | 7,281 | 40.375 | 110 | py |
sampling_cf | sampling_cf-main/data_genie/data_genie_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class PointwiseLoss(nn.Module):
def __init__(self): super(PointwiseLoss, self).__init__()
def forward(self, output, y, return_mean = True):
loss = torch.pow(output - y, 2)
if return_mean: return torch.mean(loss)
return loss
class PairwiseLoss... | 560 | 27.05 | 63 | py |
sampling_cf | sampling_cf-main/data_genie/data_genie_trainers.py | import time
import torch
import numpy as np
from tqdm import tqdm
from collections import defaultdict
from sklearn.feature_selection import RFE
from sklearn.metrics import roc_auc_score
from xgboost import XGBClassifier, XGBRegressor
from torch.utils.tensorboard import SummaryWriter
from sklearn.linear_model import Rid... | 8,933 | 38.883929 | 144 | py |
sampling_cf | sampling_cf-main/data_genie/get_embeddings.py | import gc
import os
import dgl
import snap
import torch
import numpy as np
from tqdm import tqdm
import networkx as nx
from collections import defaultdict
from data_genie.data_genie_config import *
from data_genie.data_genie_utils import save_numpy, load_numpy
from data_genie.data_genie_utils import EMBEDDINGS_PATH_G... | 7,131 | 33.960784 | 125 | py |
sampling_cf | sampling_cf-main/data_genie/data_genie_data.py | import torch
import numpy as np
from torch_utils import LongTensor, FloatTensor, is_cuda_available
from data_genie.data_genie_config import *
from data_genie.get_data import get_data_pointwise, get_data_pairwise
from data_genie.get_embeddings import get_embeddings
from data_genie.InfoGraph.infograph_dataset import Syn... | 7,077 | 33.526829 | 136 | py |
sampling_cf | sampling_cf-main/data_genie/data_genie_model.py | import dgl
import torch
import torch.nn as nn
from torch_utils import is_cuda_available
from data_genie.data_genie_loss import PointwiseLoss, PairwiseLoss
# NOTE: Below two are the training classes for data-genie: pointwise/pairwise
class PointwiseDataGenie:
def __init__(self, hyper_params, writer, xavier_init):
s... | 3,352 | 31.240385 | 92 | py |
sampling_cf | sampling_cf-main/data_genie/InfoGraph/infograph_dataset.py | from dgl import save_graphs, load_graphs
from dgl.data import DGLDataset
from tqdm import tqdm
import numpy as np
import networkx as nx
import torch
import dgl
import os
from load_data import DataHolder
from data_path_constants import get_data_path, get_index_path
from data_genie.data_genie_config import *
from data_g... | 4,944 | 30.698718 | 104 | py |
sampling_cf | sampling_cf-main/data_genie/InfoGraph/infograph_model.py | ''' Credit https://github.com/hengruizhang98/InfoGraph & https://github.com/fanyun-sun/InfoGraph '''
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, ModuleList, Linear, ReLU, BatchNorm1d
from dgl.nn import GINConv
from dgl.nn.pytorch.glob import SumPooling, Av... | 4,413 | 25.590361 | 110 | py |
sampling_cf | sampling_cf-main/data_genie/InfoGraph/train_infograph.py | import dgl
import time
import argparse
import torch as th
from dgl.dataloading import GraphDataLoader
from tqdm import tqdm
from data_genie.data_genie_utils import INFOGRAPH_MODEL_PATH
from data_genie.InfoGraph.infograph_model import InfoGraph
from data_genie.InfoGraph.infograph_dataset import SyntheticDataset
def ar... | 3,615 | 33.438095 | 121 | py |
sampling_cf | sampling_cf-main/data_genie/InfoGraph/infograph_utils.py | ''' Credit: https://github.com/fanyun-sun/InfoGraph '''
import torch
import torch as th
import torch.nn.functional as F
import math
def local_global_loss_(l_enc, g_enc, graph_id, measure):
num_graphs = g_enc.shape[0]
num_nodes = l_enc.shape[0]
device = g_enc.device
pos_mask = th.zeros((num_nodes, n... | 3,376 | 26.680328 | 82 | py |
sampling_cf | sampling_cf-main/pytorch_models/SASRec.py | import torch
import numpy as np
import torch.nn as nn
from torch_utils import LongTensor, BoolTensor, is_cuda_available
class PointWiseFeedForward(nn.Module):
def __init__(self, hidden_units, dropout_rate):
super(PointWiseFeedForward, self).__init__()
self.conv1 = nn.Conv1d(hidden_units, hidden_u... | 5,147 | 40.853659 | 122 | py |
sampling_cf | sampling_cf-main/pytorch_models/NeuMF.py | import torch
import torch.nn as nn
from pytorch_models.MF import BaseMF
class GMF(BaseMF):
def __init__(self, hyper_params):
super(GMF, self).__init__(hyper_params)
self.final = nn.Linear(hyper_params['latent_size'], 1)
self.dropout = nn.Dropout(hyper_params['dropout'])
def g... | 5,009 | 43.732143 | 118 | py |
sampling_cf | sampling_cf-main/pytorch_models/SVAE.py | import torch
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
from torch_utils import is_cuda_available
class Encoder(nn.Module):
def __init__(self, hyper_params):
super(Encoder, self).__init__()
self.linear1 = nn.Linear(
hyper_params['latent_size'], hyper_p... | 3,388 | 38.406977 | 107 | py |
sampling_cf | sampling_cf-main/pytorch_models/MVAE.py | import torch
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
from torch_utils import is_cuda_available
class Encoder(nn.Module):
def __init__(self, hyper_params):
super(Encoder, self).__init__()
self.linear1 = nn.Linear(
hyper_params['total_items'], hyper_p... | 2,331 | 32.797101 | 107 | py |
sampling_cf | sampling_cf-main/pytorch_models/MF.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_utils import LongTensor, FloatTensor
class BaseMF(nn.Module):
def __init__(self, hyper_params, keep_gamma = True):
super(BaseMF, self).__init__()
self.hyper_params = hyper_params
# Declaring alpha, beta, gamma
... | 4,075 | 38.960784 | 108 | py |
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