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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LAVA | LAVA-main/models/googlenet.py | '''GoogLeNet with PyTorch.'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Inception(nn.Module):
def __init__(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
super(Inception, self).__init__()
# 1x1 conv branch
self.b1 = nn.Sequential(
... | 3,223 | 28.851852 | 83 | py |
LAVA | LAVA-main/models/resnext.py | '''ResNeXt in PyTorch.
See the paper "Aggregated Residual Transformations for Deep Neural Networks" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
'''Grouped convolution block.'''
expansion = 2
def __init__(self, in_planes, cardinality=32... | 3,478 | 35.239583 | 129 | py |
LAVA | LAVA-main/models/senet.py | '''SENet in PyTorch.
SENet is the winner of ImageNet-2017. The paper is not released yet.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(... | 4,027 | 32.016393 | 102 | py |
LAVA | LAVA-main/models/shufflenet.py | '''ShuffleNet in PyTorch.
See the paper "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class ShuffleBlock(nn.Module):
def __init__(self, groups):
super(ShuffleBlock, self).__init... | 3,542 | 31.209091 | 126 | py |
LAVA | LAVA-main/models/lenet.py | '''LeNet in PyTorch.'''
import torch.nn as nn
import torch.nn.functional as F
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16*5*5, 120)
self.fc2 = nn.Linear... | 699 | 28.166667 | 43 | py |
LAVA | LAVA-main/models/mobilenet.py | '''MobileNet in PyTorch.
See the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
'''Depthwise conv + Pointwise conv'''
def __init__(self, in_planes, out_... | 2,025 | 31.677419 | 123 | py |
LAVA | LAVA-main/models/dpn.py | '''Dual Path Networks in PyTorch.'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer):
super(Bottleneck, self).__init__()
self.out_planes = out_planes
sel... | 3,562 | 34.989899 | 116 | py |
LAVA | LAVA-main/otdd/plotting.py | """Plotting tools for Optimal Transport Dataset Distance.
"""
import logging
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
import seaborn as sns
import torch
import scipy.stats
from scipy.stats import pearsonr, spearmanr
from mpl_toolkits.axes_grid1 import m... | 16,936 | 35.980349 | 123 | py |
LAVA | LAVA-main/otdd/pytorch/utils_2.py | import os
from itertools import zip_longest, product
from functools import partial
from os.path import dirname
import numpy as np
import scipy.sparse
from tqdm.autonotebook import tqdm
import torch
import random
import pdb
import string
import logging
from sklearn.cluster import k_means, DBSCAN
import matplotlib.pyplo... | 21,003 | 36.241135 | 136 | py |
LAVA | LAVA-main/otdd/pytorch/utils.py | import os
from itertools import zip_longest, product
from functools import partial
from os.path import dirname
import numpy as np
import scipy.sparse
from tqdm.autonotebook import tqdm
import torch
import random
import pdb
import string
import logging
from sklearn.cluster import k_means, DBSCAN
import matplotlib.pyplo... | 20,906 | 37.716667 | 136 | py |
LAVA | LAVA-main/otdd/pytorch/distance_fast.py | """ Main module for optimal transport dataset distance.
Throught this module, source and target are often used to refer to the two datasets
being compared. This notation is legacy from NLP, and does not carry other particular
meaning, e.g., the distance is nevertheless symmetric (though not always identical -
due to s... | 69,297 | 42.859494 | 122 | py |
LAVA | LAVA-main/otdd/pytorch/datasets.py | import os
import pdb
from functools import partial
import random
import logging
import string
import numpy as np
import torch
from torch.distributions.multivariate_normal import MultivariateNormal
from torch.utils.data import TensorDataset
import torch.nn as nn
import torch.utils.data as torchdata
import torch.utils.d... | 80,397 | 41.628844 | 217 | py |
LAVA | LAVA-main/otdd/pytorch/wasserstein.py | import sys
import logging
import pdb
import itertools
import numpy as np
import torch
from tqdm.autonotebook import tqdm
from joblib import Parallel, delayed
import geomloss
import ot
from .sqrtm import sqrtm, sqrtm_newton_schulz
from .utils import process_device_arg
logger = logging.getLogger(__name__)
def bures_d... | 13,245 | 37.283237 | 123 | py |
LAVA | LAVA-main/otdd/pytorch/functionals.py | ################################################################################
############### COLLECTION OF FUNCTIONALS ON DATASETS ##########################
################################################################################
import numpy as np
import torch
class Functional():
"""
Defines ... | 4,733 | 32.574468 | 86 | py |
LAVA | LAVA-main/otdd/pytorch/moments.py | """
Tools for moment (mean/cov) computation needed by OTTD and other routines.
"""
import logging
import pdb
import torch
import torch.utils.data.dataloader as dataloader
from torch.utils.data.sampler import SubsetRandomSampler
from .utils import process_device_arg, extract_data_targets
logger = logging.getLogg... | 17,700 | 41.550481 | 141 | py |
LAVA | LAVA-main/otdd/pytorch/sqrtm.py | """
Routines for computing matrix square roots.
With ideas from:
https://github.com/steveli/pytorch-sqrtm/blob/master/sqrtm.py
https://github.com/pytorch/pytorch/issues/25481
"""
import pdb
import torch
from torch.autograd import Function
from functools import partial
import numpy as np
import scipy.... | 8,103 | 34.388646 | 145 | py |
LAVA | LAVA-main/otdd/pytorch/nets.py | """
Collection of basic neural net models used in the OTDD experiments
"""
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import pdb
from .. import ROOT_DIR, HOME_DIR
MODELS_DIR = os.path.join(ROOT_DIR, 'models')
MNIST_FLAT_DIM = 28 * 28
def reset_parameters(m):
if isinstance(... | 13,512 | 33.917313 | 102 | py |
LAVA | LAVA-main/otdd/pytorch/flows.py | import os
import sys
import time
import pdb
import logging
import matplotlib
if os.name == 'posix' and "DISPLAY" not in os.environ:
matplotlib.use('Agg')
nodisplay = True
else:
nodisplay = False
import matplotlib.pyplot as plt
import numpy as np
from tqdm.autonotebook import tqdm
import torch
from torch... | 49,130 | 41.208763 | 155 | py |
mastquery | mastquery-master/mastquery/jwst.py | """
JWST queries
https://mast.stsci.edu/api/v0/_jwst_inst_keywd.html
https://mast.stsci.edu/api/v0/_services.html#MastScienceInstrumentKeywordsNircam
"""
import os
import logging
import numpy as np
import yaml
import json
from tqdm import tqdm
import astropy.table
import astropy.time
from shapely.geometry import ... | 23,191 | 34.68 | 221 | py |
SecBERT | SecBERT-main/downstream_tasks/run_ner.py | #!python
# -*- coding: utf-8 -*-
""" Fine-tuning the library models for named entity recognition """
from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import random
import numpy as np
import torch
from seqeval.metrics import precision_score, recall_... | 28,366 | 52.929658 | 184 | py |
OpenCompatible | OpenCompatible-master/test.py | import argparse
import os
from datetime import timedelta
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.nn as nn
import data_loader.data_loaders as module_data
from model.build import build_model, build_lr_scheduler
from trainer import LandmarkTrainer, FaceTrainer
from... | 3,801 | 38.195876 | 108 | py |
OpenCompatible | OpenCompatible-master/train.py | import argparse
import os
from datetime import timedelta
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.nn as nn
import data_loader.data_loaders as module_data
from model.build import build_model, build_lr_scheduler
from trainer import LandmarkTrainer, FaceTrainer
from... | 5,198 | 39.617188 | 108 | py |
OpenCompatible | OpenCompatible-master/train_bct.py | import argparse
import os
from datetime import timedelta
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.nn as nn
import data_loader.data_loaders as module_data
from model.build import build_model, build_lr_scheduler
from trainer import LandmarkTrainer, FaceTrainer
from... | 6,177 | 42.815603 | 108 | py |
OpenCompatible | OpenCompatible-master/trainer/trainer.py | import os
from pathlib import Path
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from evaluate.evaluate import evaluate_func
from model.margin_softmax import large_margin_module
from utils.util import AverageMeter, tensor_to_float
from torch.utils.tensorboard import SummaryWriter
clas... | 15,691 | 44.883041 | 146 | py |
OpenCompatible | OpenCompatible-master/data_loader/data_loaders.py | import os
from torch.utils.data import DataLoader
from torchvision import transforms
from data_loader.GLDv2 import GLDv2_train_dataloader, GLDv2_test_dataloader, ROxford_test_dataloader
cls_num_dic = {'gldv2': 81313, 'imagenet': 1000, 'places365': 365, 'market': 1502}
normalize = transforms.Normalize(mean=[0.485, 0.... | 4,991 | 39.918033 | 115 | py |
OpenCompatible | OpenCompatible-master/data_loader/sampler.py | '''
Code is modified from https://github.com/yxgeee/BAKE/blob/main/imagenet/pycls/datasets/sampler.py
'''
import math
from collections import defaultdict
import numpy as np
import torch
import torch.distributed as dist
from torch.utils.data.sampler import Sampler
__all__ = ["DistributedClassSampler"]
class Dis... | 3,209 | 33.148936 | 106 | py |
OpenCompatible | OpenCompatible-master/data_loader/GLDv2.py | """
Landmark Retrieval dataset
"""
import os
import pickle
import numpy as np
import torch
import torch.distributed as dist
import torch.utils.data as data
import torch.utils.data.distributed
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from .sampler import DistributedClassSampler, Subse... | 5,658 | 36.230263 | 111 | py |
OpenCompatible | OpenCompatible-master/evaluate/evaluate.py | import os
import time
import faiss
import argparse
import numpy as np
import torch
import torch.distributed as dist
from utils.util import AverageMeter
from .roxford_rparis_metrics import calculate_mAP_roxford_rparis
def evaluate_func(model, query_loader, gallery_loader, query_gts, logger,
config, ... | 6,119 | 41.797203 | 116 | py |
OpenCompatible | OpenCompatible-master/evaluate/roxford_rparis_metrics.py | """
Modified from https://github.com/filipradenovic/cnnimageretrieval-pytorch/blob/master/cirtorch/utils/evaluate.py
"""
import numpy as np
def compute_ap(ranks, nres):
"""
Computes average precision for given ranked indexes.
Arguments
---------
ranks : zerro-based ranks of positive images
n... | 4,597 | 29.052288 | 113 | py |
OpenCompatible | OpenCompatible-master/utils/util.py | import argparse
import collections
import json
import os
import warnings
from collections import OrderedDict
from itertools import repeat
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from torch.optim.lr_scheduler import _LRScheduler
from torch.optim.lr_scheduler import ReduceLROnPlateau... | 17,306 | 43.953247 | 116 | py |
OpenCompatible | OpenCompatible-master/model/resnet_gem.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class Resnet_GeM(nn.Module):
def __init__(self, backbone, num_classes, emb_dim):
super().__init__()
self.backbone = backbone
self.backbone.avgpool = GeM()
self.fc_emb = nn.Linear(self.backbone.fc.in_features, emb_di... | 1,218 | 32.861111 | 86 | py |
OpenCompatible | OpenCompatible-master/model/margin_softmax.py | '''
Modified from https://github.com/deepinsight/insightface/blob/master/recognition/arcface_torch/losses.py
'''
import torch
def large_margin_module(name, cosine, label, s, m):
if name == "arcface":
return arcface(cosine, label, s, m)
elif name == "cosface":
return cosface(cosine, label, s, ... | 924 | 26.205882 | 104 | py |
OpenCompatible | OpenCompatible-master/model/inception.py | import warnings
from collections import namedtuple
from typing import Callable, Any, Optional, Tuple, List
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn, Tensor
from torch.hub import load_state_dict_from_url
__all__ = ['Inception3', 'inception_v3', 'InceptionOutputs', '_Inceptio... | 17,835 | 36.235908 | 119 | py |
OpenCompatible | OpenCompatible-master/model/loss.py | import torch
from torch import nn, Tensor
import torch.nn.functional as F
import torch.distributed as dist
__all__ = ['BackwardCompatibleLoss']
def gather_tensor(raw_tensor):
"""
Performs all_gather operation on the provided tensors.
*** Warning ***: torch.distributed.all_gather has no gradient.
"""
... | 10,423 | 40.863454 | 119 | py |
OpenCompatible | OpenCompatible-master/model/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class Resnet_GeM(nn.Module):
def __init__(self, backbone, class_num, emb_dim):
super().__init__()
self.backbone = backbone
self.backbone.avgpool = GeM()
self.fc_emb = nn.Linear(self.backbone.fc.in... | 2,440 | 31.546667 | 89 | py |
OpenCompatible | OpenCompatible-master/model/build.py | import torch
import torch.nn as nn
import torchvision
from timm.scheduler.cosine_lr import CosineLRScheduler
from timm.scheduler.step_lr import StepLRScheduler
from utils.util import load_pretrained_model
from .model import Resnet_GeM, BackwardCompatibleModel
from .inception import Inception3
def build_backbone(mode... | 3,349 | 37.505747 | 115 | py |
OpenCompatible | OpenCompatible-master/model/metric.py | import torch
def accuracy(output, target):
with torch.no_grad():
pred = torch.argmax(output, dim=1)
assert pred.shape[0] == len(target)
correct = 0
correct += torch.sum(pred == target).item()
return correct / len(target)
def top_k_acc(output, target, k=3):
with torch.no_g... | 560 | 25.714286 | 61 | py |
snakelines | snakelines-master/rules/paired_end/classification/contig_based/scripts/annotate_with_taxonomy.py | import sys
import pickle
import pandas as pd
from glob import glob
from Bio import SeqIO
input_blast = sys.argv[1]
input_taxes = sys.argv[2]
output_blast = sys.argv[3]
taxes = pickle.load(open(input_taxes, 'rb'))
def get_tax(taxid):
if not taxid or taxid == 'N/A':
return 'Unknown'
taxid = int(taxid)
... | 837 | 25.1875 | 73 | py |
CLIP4CirDemo | CLIP4CirDemo-main/hubconf.py | dependencies = ["torch"]
import torch
from torch import nn
from model import Combiner
CIRR_URL = "https://www.dropbox.com/s/cdesqz7yincaq8g/cirr_combiner.pth?dl=1"
FASHIONIQ_URL = "https://www.dropbox.com/s/tra1no8ionus3lk/fashionIQ_combiner.pth?dl=1"
if torch.cuda.is_available():
torch.cuda.set_device(0)
... | 1,240 | 33.472222 | 117 | py |
CLIP4CirDemo | CLIP4CirDemo-main/extract_features.py | import pickle
from typing import Union
import clip
import torch
from torch import nn
from torch.utils.data import DataLoader
from tqdm import tqdm
from data_utils import FashionIQDataset, targetpad_transform, CIRRDataset, data_path
from utils import collate_fn
if torch.cuda.is_available():
device = torch.device(... | 3,255 | 39.7 | 114 | py |
CLIP4CirDemo | CLIP4CirDemo-main/app.py | import json
import os
import pickle
import random
import time
from io import BytesIO
from multiprocessing import Process
from typing import Optional, Tuple, Union
import torch.nn.functional as F
import PIL.Image
import PIL.ImageOps
import clip
import numpy as np
import torch
from flask import Flask, send_file, url_for
... | 22,727 | 42.127135 | 127 | py |
CLIP4CirDemo | CLIP4CirDemo-main/utils.py | import torch
def collate_fn(batch: list):
"""
Discard None images in a batch when using torch DataLoader
:param batch: input_batch
:return: output_batch = input_batch - None_values
"""
batch = list(filter(lambda x: x is not None, batch))
return torch.utils.data.dataloader.default_collate(b... | 326 | 26.25 | 62 | py |
CLIP4CirDemo | CLIP4CirDemo-main/model.py | import torch
import torch.nn.functional as F
from torch import nn
class Combiner(nn.Module):
"""
Combiner module which once trained fuses textual and visual information
"""
def __init__(self, clip_feature_dim: int, projection_dim: int, hidden_dim: int):
"""
:param clip_feature_dim: CL... | 2,212 | 42.392157 | 114 | py |
CLIP4CirDemo | CLIP4CirDemo-main/data_utils.py | import json
from pathlib import Path
from typing import List, Optional
import PIL.Image
import torchvision.transforms.functional as F
from torch.utils.data import Dataset
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
server_base_path = Path(__file__).absolute().parent.absolute()
... | 5,763 | 34.361963 | 119 | py |
GALAXY | GALAXY-main/run.py | """
Running scripts.
"""
import argparse
import json
import os
import random
import numpy as np
import torch
from galaxy.args import parse_args
from galaxy.args import str2bool
from galaxy.data.dataset import Dataset
from galaxy.data.field import BPETextField, MultiWOZBPETextField, CamRestBPETextField, KvretBPETextF... | 4,515 | 33.473282 | 108 | py |
GALAXY | GALAXY-main/run_pretrain.py | """
Running scripts.
"""
import argparse
import json
import os
import random
import numpy as np
import torch
from galaxy.args import parse_args
from galaxy.args import str2bool
from galaxy.data.data_loader import DataLoader
from galaxy.data.dataset import Dataset
from galaxy.data.dataset import LazyDataset
from gala... | 4,948 | 34.604317 | 109 | py |
GALAXY | GALAXY-main/galaxy/modules/multihead_attention.py | """
MultiheadAttention class.
"""
import torch
import torch.nn as nn
class MultiheadAttention(nn.Module):
"""
Multi head attention layer.
"""
def __init__(self, hidden_dim, num_heads, dropout):
assert hidden_dim % num_heads == 0
super(MultiheadAttention, self).__init__()
sel... | 3,362 | 30.726415 | 108 | py |
GALAXY | GALAXY-main/galaxy/modules/feedforward.py | """
FeedForward class.
"""
import torch
import torch.nn as nn
class FeedForward(nn.Module):
"""
Positional feed forward layer.
"""
def __init__(self, hidden_dim, inner_dim, dropout):
super(FeedForward, self).__init__()
self.hidden_dim = hidden_dim
self.inner_dim = inner_dim
... | 954 | 19.76087 | 58 | py |
GALAXY | GALAXY-main/galaxy/modules/embedder.py | """
Embedder class.
"""
import torch
import torch.nn as nn
class Embedder(nn.Module):
"""
Composite embedding layer.
"""
def __init__(self,
hidden_dim,
num_token_embeddings,
num_pos_embeddings,
num_type_embeddings,
... | 2,111 | 32 | 80 | py |
GALAXY | GALAXY-main/galaxy/modules/functions.py | """
Helpful functions.
"""
import numpy as np
import torch
import torch.nn.functional as F
def unsqueeze(input, dims):
""" Implement multi-dimension unsqueeze function. """
if isinstance(dims, (list, tuple)):
dims = [dim if dim >= 0 else dim + len(input.shape) + 1 for dim in dims]
dims = sort... | 1,712 | 25.353846 | 80 | py |
GALAXY | GALAXY-main/galaxy/modules/transformer_block.py | """
TransformerBlock class.
"""
import torch
import torch.nn as nn
from galaxy.modules.feedforward import FeedForward
from galaxy.modules.multihead_attention import MultiheadAttention
class TransformerBlock(nn.Module):
"""
Transformer block module.
"""
def __init__(self, hidden_dim, num_heads, drop... | 2,173 | 28.378378 | 81 | py |
GALAXY | GALAXY-main/galaxy/models/model_base.py | """
Model base
"""
import torch
import torch.nn as nn
class ModelBase(nn.Module):
"""
Basic model wrapper.
"""
_registry = dict()
@classmethod
def register(cls, name):
ModelBase._registry[name] = cls
return
@staticmethod
def by_name(name):
return ModelBase._re... | 3,060 | 29.919192 | 91 | py |
GALAXY | GALAXY-main/galaxy/models/unified_transformer.py | """
UnifiedTransformer
"""
import numpy as np
import torch
import torch.nn as nn
from galaxy.args import str2bool
from galaxy.modules.embedder import Embedder
from galaxy.models.model_base import ModelBase
from galaxy.modules.transformer_block import TransformerBlock
from galaxy.utils.eval import DAEvaluation
class ... | 19,305 | 41.061002 | 105 | py |
GALAXY | GALAXY-main/galaxy/models/pretrain_unified_transformer.py | """
PretrainUnifiedTransformer
"""
import torch
import torch.nn as nn
from galaxy.args import str2bool
from galaxy.models.unified_transformer import UnifiedTransformer
from galaxy.utils.criterions import compute_kl_loss
from galaxy.utils.eval import DAEvaluation
class PretrainUnifiedTransformer(UnifiedTransformer):... | 7,014 | 39.784884 | 103 | py |
GALAXY | GALAXY-main/galaxy/models/generator.py | """
Generator class.
"""
import math
import torch
import numpy as np
from galaxy.args import str2bool
def repeat(var, times):
if isinstance(var, list):
return [repeat(x, times) for x in var]
elif isinstance(var, dict):
return {k: repeat(v, times) for k, v in var.items()}
elif isinstance(... | 12,426 | 39.087097 | 102 | py |
GALAXY | GALAXY-main/galaxy/utils/criterions.py | import torch
from torch.nn.modules.loss import _Loss
import torch.nn.functional as F
def compute_kl_loss(p, q, filter_scores=None):
p_loss = F.kl_div(F.log_softmax(p, dim=-1), F.softmax(q, dim=-1), reduction='none')
q_loss = F.kl_div(F.log_softmax(q, dim=-1), F.softmax(p, dim=-1), reduction='none')
# You... | 1,369 | 26.4 | 87 | py |
GALAXY | GALAXY-main/galaxy/data/data_loader.py | """
DataLoader class
"""
import math
from galaxy.args import str2bool
from galaxy.data.batch import batch
from galaxy.data.sampler import RandomSampler
from torch.utils.data.distributed import DistributedSampler
class DataLoader(object):
""" Implement of DataLoader. """
@classmethod
def add_cmdline_arg... | 1,660 | 30.942308 | 101 | py |
GALAXY | GALAXY-main/galaxy/trainers/pretrain_trainer.py | """
Pretrain Trainer class.
"""
import logging
import os
import sys
import time
from collections import OrderedDict
import torch
import numpy as np
from transformers.optimization import AdamW, get_linear_schedule_with_warmup
from galaxy.args import str2bool
from galaxy.data.data_loader import DataLoader
from galaxy... | 14,586 | 44.584375 | 111 | py |
GALAXY | GALAXY-main/galaxy/trainers/trainer.py | """
Trainer class.
"""
import json
import logging
import os
import sys
import time
from collections import OrderedDict
import torch
import numpy as np
from tqdm import tqdm
from transformers.optimization import AdamW, get_linear_schedule_with_warmup
from galaxy.args import str2bool
from galaxy.data.data_loader impor... | 47,085 | 47.3926 | 113 | py |
proof-sharing | proof-sharing-main/__main__.py | import argparse
import torch
from time import time
import config
import utils
import templates
from relaxations import Zonotope_Net
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'... | 15,688 | 38.618687 | 106 | py |
proof-sharing | proof-sharing-main/utils.py | import os
import pickle
import torch
from torchvision import datasets, transforms
import numpy as np
from scipy.io import loadmat
import itertools
from time import time
from tqdm import tqdm
import re
import logging
import logging.handlers
import datetime
from relaxations import Zonotope_Net, Box_Net, Zonotope # noqa... | 37,893 | 32.153106 | 102 | py |
proof-sharing | proof-sharing-main/networks.py | import numpy as np
import torch
import torch.nn as nn
import config
class Normalization(nn.Module):
def __init__(self, device, mean=0.1307, sigma=0.3081):
super(Normalization, self).__init__()
# self.mean = torch.FloatTensor([0.1307]).view((1, 1, 1, 1)).to(device)
# self.sigma = torch.Flo... | 9,620 | 36.877953 | 92 | py |
proof-sharing | proof-sharing-main/relaxations.py | import torch
import networks
import scipy
from scipy import spatial, linalg # noqa: F401
import numpy as np
# import matplotlib.pyplot as plt
import matplotlib.patches as patches
import itertools
import logging
import gurobipy as gp
from gurobipy import GRB
logger = logging.getLogger()
class Zonotope:
def __in... | 112,023 | 35.009 | 109 | py |
proof-sharing | proof-sharing-main/templates.py | import torch
import logging
import os
import pickle
from tqdm import tqdm
from time import time
from joblib import Parallel, delayed
import multiprocessing
from sklearn import cluster as sklearn_cluster
from relaxations import Zonotope_Net, Star_Net, Box, Parallelotope
import utils
logger = logging.getLogger()
def ... | 39,865 | 35.981447 | 156 | py |
proof-sharing | proof-sharing-main/models.py | import torch
import pickle
from tqdm import tqdm
from joblib import Parallel, delayed
import multiprocessing
from time import time
from relaxations import Zonotope, Zonotope_Net, Star, Box_Star, Star_Net, Box_Net # noqa
import utils
# import config
import logging
import gurobipy as gp
from gurobipy import GRB
logger ... | 155,583 | 37.664016 | 152 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/Train_EAL_GAN.py | # -*- coding: utf-8 -*-
from __future__ import division
from __future__ import print_function
import os
import sys
from time import time
sys.path.append(
os.path.abspath(os.path.join(os.path.dirname("__file__"), '..')))
# supress warnings for clean output
import warnings
warnings.filterwarnings("ignore")
import... | 2,599 | 30.325301 | 171 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/EAL_GAN.py | from collections import defaultdict
import functools
from numpy.random import gamma
import torch
import torch.nn as nn
from torch.optim import Adam
#import torch.optim.lr_scheduler.StepLR as StepLR
import numpy as np
import pandas as pd
from tqdm import tqdm
from src.BigGANdeep import Generator, Discriminator
from s... | 9,041 | 41.650943 | 174 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/BigGANdeep.py | import numpy as np
import math
import functools
import torch
import torch.nn as nn
from torch.nn import init
import torch.optim as optim
import torch.nn.functional as F
from torch.nn import Parameter as P
import src.layers as layers
from src.sync_batchnorm import SynchronizedBatchNorm2d as SyncBatchNorm2d
# BigGAN-d... | 21,726 | 42.541082 | 126 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/BigGAN.py | import numpy as np
import math
import functools
import torch
import torch.nn as nn
from torch.nn import init
import torch.optim as optim
import torch.nn.functional as F
from torch.nn import Parameter as P
import src.layers as layers
from src.sync_batchnorm import SynchronizedBatchNorm2d as SyncBatchNorm2d
# Archite... | 18,405 | 43.674757 | 98 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/my_utils.py | import torch
import numpy as np
from sklearn.metrics import roc_auc_score
import argparse
def active_sampling(args, real_x, real_y, NetD_Ensemble, need_sample=True):
if need_sample:
pt = None
for i in range(args.ensemble_num):
netD = NetD_Ensemble[i]
pt_i = netD(real_x, mod... | 6,870 | 46.715278 | 128 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/loss.py | import torch
import torch.nn as nn
import pandas as pd
import numpy as np
import os
import torch.nn.functional as F
def loss_dis_real(dis_real, out_category, y, weights=None, cat_weight=None, gamma=2.0):
#step 1: the loss for GAN
logpt = F.softplus(-dis_real)
pt = torch.exp(-logpt)
if weights is None:
... | 2,640 | 28.674157 | 87 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/utils.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
''' Utilities file
This file contains utility functions for bookkeeping, logging, and data loading.
Methods which directly affect training should either go in layers, the model,
or train_fns.py.
'''
from __future__ import print_function
import sys
import os
import numpy a... | 47,107 | 39.436052 | 109 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/layers.py | ''' Layers
This file contains various layers for the BigGAN models.
'''
import numpy as np
import torch
import torch.nn as nn
from torch.nn import init
import torch.optim as optim
import torch.nn.functional as F
from torch.nn import Parameter as P
from src.sync_batchnorm import SynchronizedBatchNorm2d as SyncBN2d
... | 17,134 | 36.331155 | 101 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/sync_batchnorm/replicate.py | # -*- coding: utf-8 -*-
# File : replicate.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import functools
from torch.nn.parallel.da... | 3,226 | 32.968421 | 115 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/sync_batchnorm/unittest.py | # -*- coding: utf-8 -*-
# File : unittest.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import unittest
import torch
class TorchTes... | 746 | 23.9 | 59 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/sync_batchnorm/batchnorm.py | # -*- coding: utf-8 -*-
# File : batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import collections
import torch
import torc... | 14,882 | 41.644699 | 159 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/sync_batchnorm/batchnorm_reimpl.py | #! /usr/bin/env python3
# -*- coding: utf-8 -*-
# File : batchnorm_reimpl.py
# Author : acgtyrant
# Date : 11/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import torch
import torch.nn as nn
import torch... | 2,383 | 30.786667 | 95 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/datasets/preprocessing.py | import torch
import numpy as np
def create_semisupervised_setting(labels, normal_classes, outlier_classes, known_outlier_classes,
ratio_known_normal, ratio_known_outlier, ratio_pollution):
"""
Create a semi-supervised data setting.
:param labels: np.array with labels of ... | 3,563 | 52.19403 | 113 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/datasets/odds.py | from torch.utils.data import DataLoader, Subset
from src.base.base_dataset import BaseADDataset
from src.base.odds_dataset import ODDSDataset
from src.datasets.preprocessing import create_semisupervised_setting
import torch
class ODDSADDataset(BaseADDataset):
def __init__(self, root: str, dataset_name: str, n_k... | 2,464 | 48.3 | 129 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/datasets/fmnist.py | from torch.utils.data import Subset
from PIL import Image
from torchvision.datasets import FashionMNIST
from src.base.torchvision_dataset import TorchvisionDataset
from src.datasets.preprocessing import create_semisupervised_setting
import torch
import torchvision.transforms as transforms
import random
class Fashion... | 3,912 | 40.62766 | 129 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/datasets/cifar10.py | from torch.utils.data import Subset
from PIL import Image
from torchvision.datasets import CIFAR10
from src.base.torchvision_dataset import TorchvisionDataset
from src.datasets.preprocessing import create_semisupervised_setting
import torch
import torchvision.transforms as transforms
import random
import numpy as np
... | 3,791 | 39.340426 | 129 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/datasets/mnist.py | from torch.utils.data import Subset
from PIL import Image
from torchvision.datasets import MNIST
from src.base.torchvision_dataset import TorchvisionDataset
from src.datasets.preprocessing import create_semisupervised_setting
import torch
import torchvision.transforms as transforms
import random
class MNIST_Dataset(... | 3,793 | 38.936842 | 129 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/base/base_net.py | import logging
import torch.nn as nn
import numpy as np
class BaseNet(nn.Module):
"""Base class for all neural networks."""
def __init__(self):
super().__init__()
self.logger = logging.getLogger(self.__class__.__name__)
self.rep_dim = None # representation dimensionality, i.e. dim of... | 797 | 28.555556 | 102 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/base/odds_dataset.py | from pathlib import Path
from torch.utils.data import Dataset
from scipy.io import loadmat
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from torchvision.datasets.utils import download_url
import pandas as pd
import os
import torch
import numpy as ... | 4,935 | 38.174603 | 112 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/base/torchvision_dataset.py | from .base_dataset import BaseADDataset
from torch.utils.data import DataLoader
class TorchvisionDataset(BaseADDataset):
"""TorchvisionDataset class for datasets already implemented in torchvision.datasets."""
def __init__(self, root: str):
super().__init__(root)
def loaders(self, batch_size: in... | 823 | 44.777778 | 105 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/base/base_dataset.py | from abc import ABC, abstractmethod
from torch.utils.data import DataLoader
class BaseADDataset(ABC):
"""Anomaly detection dataset base class."""
def __init__(self, root: str):
super().__init__()
self.root = root # root path to data
self.n_classes = 2 # 0: normal, 1: outlier
... | 1,006 | 36.296296 | 105 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN-image/src/base/__init__.py | from .base_dataset import *
from .torchvision_dataset import *
from .odds_dataset import *
from .base_net import *
from .base_trainer import *
| 143 | 23 | 34 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN/Train_EAL_GAN.py | # -*- coding: utf-8 -*-
from __future__ import division
from __future__ import print_function
import os
import sys
from time import time
sys.path.append(
os.path.abspath(os.path.join(os.path.dirname("__file__"), '..')))
# supress warnings for clean output
import warnings
warnings.filterwarnings("ignore")
import... | 5,967 | 38.263158 | 173 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN/models/losses.py | import torch
import torch.nn as nn
import pandas as pd
import numpy as np
import os
import torch.nn.functional as F
def loss_dis_real(dis_real, out_category, y, weights=None, cat_weight=None, gamma=2.0):
#step 1: the loss for GAN
logpt = F.softplus(-dis_real)
pt = torch.exp(-logpt)
if weights is None:
... | 2,640 | 28.674157 | 87 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN/models/EAL_GAN.py | import numpy as np
import math
import functools
import random
import os
from collections import defaultdict
import matplotlib.pyplot as plt
import matplotlib.font_manager
from numpy import percentile
import pandas as pd
import torch
import torch.nn as nn
from torch.nn import init
import torch.optim as optim
import to... | 16,932 | 40.199513 | 119 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN/models/utils.py | import torch
import torch.nn as nn
import pandas as pd
import numpy as np
import os
import torch.nn.functional as F
from numpy import *
import argparse
def load_data_V2(data_name):
#data_path = os.path.join('./data/', data_name)
data_path = data_name
data = pd.read_table('{path}'.format(path = data_path), ... | 20,750 | 35.088696 | 118 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN/models/layers.py | ''' Layers
This file contains various layers for the BigGAN models.
'''
import numpy as np
import torch
import torch.nn as nn
from torch.nn import init
import torch.optim as optim
import torch.nn.functional as F
from torch.nn import Parameter as P
from .sync_batchnorm import SynchronizedBatchNorm2d as SyncBN2d
#... | 13,726 | 36.40327 | 101 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN/models/sync_batchnorm/replicate.py | # -*- coding: utf-8 -*-
# File : replicate.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import functools
from torch.nn.parallel.da... | 3,226 | 32.968421 | 115 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN/models/sync_batchnorm/unittest.py | # -*- coding: utf-8 -*-
# File : unittest.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import unittest
import torch
class TorchTes... | 746 | 23.9 | 59 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN/models/sync_batchnorm/batchnorm.py | # -*- coding: utf-8 -*-
# File : batchnorm.py
# Author : Jiayuan Mao
# Email : maojiayuan@gmail.com
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import collections
import torch
import torc... | 14,882 | 41.644699 | 159 | py |
EAL-GAN | EAL-GAN-main/EAL-GAN/models/sync_batchnorm/batchnorm_reimpl.py | #! /usr/bin/env python3
# -*- coding: utf-8 -*-
# File : batchnorm_reimpl.py
# Author : acgtyrant
# Date : 11/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import torch
import torch.nn as nn
import torch... | 2,383 | 30.786667 | 95 | py |
kg_one2set | kg_one2set-master/train_ml.py | import logging
import math
import os
import sys
import time
import torch
import torch.nn as nn
import pykp.utils.io as io
from inference.evaluate import evaluate_loss
from pykp.utils.label_assign import hungarian_assign
from pykp.utils.masked_loss import masked_cross_entropy
from utils.functions import time_since
fro... | 12,566 | 49.46988 | 120 | py |
kg_one2set | kg_one2set-master/predict.py | import argparse
import os
import time
import torch
import config
from inference.evaluate import evaluate_greedy_generator
from pykp.model import Seq2SeqModel
from pykp.utils.io import build_interactive_predict_dataset
from utils.data_loader import load_vocab, build_data_loader
from utils.functions import common_proces... | 2,966 | 29.27551 | 103 | py |
kg_one2set | kg_one2set-master/train.py | import argparse
import json
import logging
import os
import time
import torch
from torch.optim import Adam
import config
import train_ml
from pykp.model import Seq2SeqModel
from utils.data_loader import load_data_and_vocab
from utils.functions import common_process_opt
from utils.functions import time_since
def pro... | 3,275 | 30.5 | 126 | py |
kg_one2set | kg_one2set-master/preprocess.py | import argparse
import logging
import os
from collections import Counter
import torch
import config
import pykp.utils.io as io
from utils.functions import read_src_and_trg_files
def build_vocab(tokenized_src_trg_pairs):
token_freq_counter = Counter()
for src_word_list, trg_word_lists in tokenized_src_trg_pai... | 4,694 | 40.184211 | 120 | py |
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