repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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SLT-FAI | SLT-FAI-main/sentence_transformers/models/T5.py | from torch import nn
from transformers import T5Model, T5Tokenizer
import json
from typing import List, Dict, Optional
import os
import numpy as np
import logging
class T5(nn.Module):
"""DEPRECATED: Please use models.Transformer instead.
T5 model to generate token embeddings.
Each token is mapped to an o... | 3,402 | 37.235955 | 206 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/RoBERTa.py | from . import Transformer
class RoBERTa(Transformer):
"""
DEPRECATED: Please use models.Transformer instead.
"""
pass
| 140 | 9.846154 | 54 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/CamemBERT.py | from . import Transformer
class CamemBERT(Transformer):
"""
DEPRECATED: Please use models.Transformer instead.
"""
pass
| 144 | 8.666667 | 54 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/BERT.py | from . import Transformer
class BERT(Transformer):
"""
DEPRECATED: Please use models.Transformer instead.
"""
pass
| 138 | 8.928571 | 54 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/XLNet.py | from . import Transformer
class XLNet(Transformer):
"""
DEPRECATED: Please use models.Transformer instead.
"""
pass
| 139 | 9 | 54 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/WordWeights.py | import torch
from torch import Tensor
from torch import nn
from typing import Union, Tuple, List, Iterable, Dict
import os
import json
import logging
class WordWeights(nn.Module):
"""This model can weight word embeddings, for example, with idf-values."""
def __init__(self, vocab: List[str], word_weights: Dict... | 3,017 | 39.783784 | 196 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/WKPooling.py | import torch
from torch import Tensor
from torch import nn
from typing import Union, Tuple, List, Iterable, Dict
import os
import json
import numpy as np
class WKPooling(nn.Module):
"""
Pooling based on the paper: "SBERT-WK: A Sentence Embedding Method ByDissecting BERT-based Word Models"
https://arxiv.or... | 5,864 | 40.595745 | 130 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/Normalize.py | from torch import Tensor
from torch import nn
from typing import Dict
import torch.nn.functional as F
class Normalize(nn.Module):
"""
This layer normalizes embeddings to unit length
"""
def __init__(self):
super(Normalize, self).__init__()
def forward(self, features: Dict[str, Tensor]):
... | 566 | 23.652174 | 104 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/ALBERT.py | from . import Transformer
class ALBERT(Transformer):
"""
DEPRECATED: Please use models.Transformer instead.
"""
pass
| 139 | 9.769231 | 54 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/Dense.py | import torch
from torch import Tensor
from torch import nn
from torch import functional as F
from typing import Union, Tuple, List, Iterable, Dict
import os
import json
from ..util import fullname, import_from_string
class Dense(nn.Module):
"""Feed-forward function with activiation function.
This layer take... | 2,116 | 40.509804 | 175 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/BoW.py | import torch
from torch import Tensor
from torch import nn
from typing import Union, Tuple, List, Iterable, Dict
import os
import json
import logging
import numpy as np
from .tokenizer import WhitespaceTokenizer
class BoW(nn.Module):
"""Implements a Bag-of-Words (BoW) model to derive sentence embeddings.
A we... | 2,940 | 37.194805 | 150 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/__init__.py | from .Transformer import Transformer
from .ALBERT import ALBERT
from .BERT import BERT
from .BoW import BoW
from .CNN import CNN
from .CamemBERT import CamemBERT
from .Dense import Dense
from .DistilBERT import DistilBERT
from .LSTM import LSTM
from .Normalize import Normalize
from .Pooling import Pooling
from .RoBERTa... | 606 | 27.904762 | 54 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/Pooling.py | import torch
from torch import Tensor
from torch import nn
from typing import Union, Tuple, List, Iterable, Dict
import os
import json
class Pooling(nn.Module):
"""Performs pooling (max or mean) on the token embeddings.
Using pooling, it generates from a variable sized sentence a fixed sized sentence embeddi... | 8,028 | 51.477124 | 209 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/LSTM.py | import torch
from torch import nn
from typing import List
import os
import json
class LSTM(nn.Module):
"""
Bidirectional LSTM running over word embeddings.
"""
def __init__(self, word_embedding_dimension: int, hidden_dim: int, num_layers: int = 1, dropout: float = 0, bidirectional: bool = True):
... | 2,323 | 35.888889 | 155 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/MLP3.py | import torch
from torch import nn
import os
import json
from typing import Union, Tuple, List, Iterable, Dict
from torch import Tensor
class MLP3(nn.Module):
def __init__(self, hidden_dim=2048, norm=None, activation='relu'):
super().__init__()
''' page 3 baseline setting
Projection MLP. The... | 3,212 | 32.123711 | 80 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/DistilBERT.py | from . import Transformer
class DistilBERT(Transformer):
"""
DEPRECATED: Please use models.Transformer instead.
"""
pass
| 143 | 10.076923 | 54 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/tokenizer/WordTokenizer.py | from abc import ABC, abstractmethod
from typing import Union, Tuple, List, Iterable, Dict
ENGLISH_STOP_WORDS = ['!', '"', "''", "``", '#', '$', '%', '&', "'", '(', ')', '*', '+', ',', '-', '.', '/', ':', ';', '<', '=', '>', '?', '@', '[', '\\', ']', '^', '_', '`', '{', '|', '}', '~', 'a', 'about', 'above', 'across', ... | 3,838 | 141.185185 | 3,321 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/tokenizer/PhraseTokenizer.py | from typing import Union, Tuple, List, Iterable, Dict
import collections
import string
import os
import json
import logging
from .WordTokenizer import WordTokenizer, ENGLISH_STOP_WORDS
import nltk
class PhraseTokenizer(WordTokenizer):
"""Tokenizes the text with respect to existent phrases in the vocab.
This t... | 4,088 | 42.042105 | 224 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/tokenizer/WhitespaceTokenizer.py | from typing import Union, Tuple, List, Iterable, Dict
import collections
import string
import os
import json
from .WordTokenizer import WordTokenizer, ENGLISH_STOP_WORDS
class WhitespaceTokenizer(WordTokenizer):
"""
Simple and fast white-space tokenizer. Splits sentence based on white spaces.
Punctuation a... | 2,238 | 33.446154 | 140 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/models/tokenizer/__init__.py | from .WordTokenizer import WordTokenizer, ENGLISH_STOP_WORDS
from .WhitespaceTokenizer import WhitespaceTokenizer
from .WhitespaceTokenizer import WhitespaceTokenizer | 166 | 54.666667 | 60 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/datasets/SentenceLabelDataset.py | from torch.utils.data import Dataset
from typing import List
import bisect
import torch
import logging
import numpy as np
from tqdm import tqdm
from .. import SentenceTransformer
from ..readers.InputExample import InputExample
from multiprocessing import Pool, cpu_count
import multiprocessing
class SentenceLabelDatase... | 8,156 | 43.091892 | 161 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/datasets/SentencesDataset.py | from torch.utils.data import Dataset
from typing import List
import torch
from .. import SentenceTransformer
from ..readers.InputExample import InputExample
class SentencesDataset(Dataset):
"""
Dataset for smart batching, that is each batch is only padded to its longest sequence instead of padding all
sequ... | 1,443 | 34.219512 | 115 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/datasets/EncodeDataset.py | from torch.utils.data import Dataset
from typing import List, Union
from .. import SentenceTransformer
class EncodeDataset(Dataset):
def __init__(self,
sentences: Union[List[str], List[int]],
model: SentenceTransformer,
is_tokenized: bool = True):
"""
... | 777 | 28.923077 | 103 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/datasets/ParallelSentencesDataset.py | from torch.utils.data import Dataset
import logging
import gzip
from queue import Queue
from .. import SentenceTransformer
from typing import List
import random
class ParallelSentencesDataset(Dataset):
"""
This dataset reader can be used to read-in parallel sentences, i.e., it reads in a file with tab-seperate... | 7,073 | 43.490566 | 153 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/datasets/__init__.py | from .sampler import *
from .ParallelSentencesDataset import ParallelSentencesDataset
from .SentenceLabelDataset import SentenceLabelDataset
from .SentencesDataset import SentencesDataset
from .EncodeDataset import EncodeDataset
| 229 | 37.333333 | 62 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/datasets/sampler/LabelSampler.py | """
This file contains sampler functions, that can be used to sample mini-batches with specific properties.
"""
from torch.utils.data import Sampler
import numpy as np
from ...datasets import SentenceLabelDataset
class LabelSampler(Sampler):
"""
This sampler is used for some specific Triplet Losses like BATCH... | 3,097 | 39.763158 | 121 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/datasets/sampler/__init__.py | from .LabelSampler import * | 27 | 27 | 27 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/readers/TripletReader.py | from . import InputExample
import csv
import gzip
import os
class TripletReader(object):
"""
Reads in the a Triplet Dataset: Each line contains (at least) 3 columns, one anchor column (s1),
one positive example (s2) and one negative example (s3)
"""
def __init__(self, dataset_folder, s1_col_idx=0, ... | 1,336 | 32.425 | 120 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/readers/PairedFilesReader.py | from . import InputExample
import csv
import gzip
import os
import gzip
class PairedFilesReader(object):
"""
Reads in the a Pair Dataset, split in two files
"""
def __init__(self, filepaths):
self.filepaths = filepaths
def get_examples(self, max_examples=0):
"""
"""
... | 1,058 | 23.068182 | 127 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/readers/NLIDataReader.py | from . import InputExample
import csv
import gzip
import os
class NLIDataReader(object):
"""
Reads in the Stanford NLI dataset and the MultiGenre NLI dataset
"""
def __init__(self, dataset_folder):
self.dataset_folder = dataset_folder
def get_examples(self, filename, max_examples=0):
... | 1,690 | 34.978723 | 113 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/readers/STSDataReader.py | from . import InputExample
import csv
import gzip
import os
class STSDataReader:
"""
Reads in the STS dataset. Each line contains two sentences (s1_col_idx, s2_col_idx) and one label (score_col_idx)
Default values expects a tab seperated file with the first & second column the sentence pair and third colu... | 2,655 | 48.185185 | 185 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/readers/__init__.py | from .InputExample import InputExample
from .LabelSentenceReader import LabelSentenceReader
from .NLIDataReader import NLIDataReader
from .STSDataReader import STSDataReader, STSBenchmarkDataReader
from .TripletReader import TripletReader | 238 | 46.8 | 64 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/readers/LabelSentenceReader.py | from . import InputExample
import csv
import gzip
import os
class LabelSentenceReader:
"""Reads in a file that has at least two columns: a label and a sentence.
This reader can for example be used with the BatchHardTripletLoss.
Maps labels automatically to integers"""
def __init__(self, folder, label_c... | 1,270 | 32.447368 | 86 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/readers/InputExample.py | from typing import Union, List
class InputExample:
"""
Structure for one input example with texts, the label and a unique id
"""
def __init__(self, guid: str = '', texts: List[str] = None, texts_tokenized: List[List[int]] = None, label: Union[int, float] = 0):
"""
Creates one InputExam... | 1,037 | 36.071429 | 135 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/SimSiamLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
import logging
LARGE_NUM = 1e9
class MLP1(nn.Module):
def __init__(self, hidden_dim=2048, norm=None, activation="relu"): # bottleneck structure
super().__i... | 9,366 | 47.786458 | 169 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/CosineSimilarityLoss.py | import torch
from torch import nn, Tensor
from typing import Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
class CosineSimilarityLoss(nn.Module):
"""
CosineSimilarityLoss expects, that the InputExamples consists of two texts and a float label.
It computes the vectors u = model(inpu... | 2,213 | 50.488372 | 177 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/AdvCLSoftmaxLoss_single_stream_backup.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
import logging
LARGE_NUM = 1e9
def scheduler0(cur_step, global_step):
return 1.0, 1.0
def scheduler1(cur_step, global_step):
"""global_step=9814"""
if cur_... | 36,924 | 50.427577 | 226 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/MSELoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
class MSELoss(nn.Module):
"""
Computes the MSE loss between the computed sentence embedding and a target sentence embedding. This loss
is used when extending sentence embeddings to new languages as described in... | 888 | 39.409091 | 118 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/TripletLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
import torch.nn.functional as F
from enum import Enum
from ..SentenceTransformer import SentenceTransformer
class TripletDistanceMetric(Enum):
"""
The metric for the triplet loss
"""
COSINE = lambda x, y: 1 ... | 2,728 | 45.254237 | 164 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/BatchHardSoftMarginTripletLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from .BatchHardTripletLoss import BatchHardTripletLoss, BatchHardTripletLossDistanceFunction
from sentence_transformers.SentenceTransformer import SentenceTransformer
class BatchHardSoftMarginTripletLoss(BatchHardTripletLos... | 4,942 | 54.539326 | 162 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/AdvCLSoftmaxLoss.py | import json
import os
import copy
import numpy as np
import torch
from torch import nn, Tensor
from torch.autograd import Function
from typing import Union, Tuple, List, Iterable, Dict, Set, Any, Optional
from ..SentenceTransformer import SentenceTransformer
import logging
LARGE_NUM = 1e9
def scheduler0(cur_step, gl... | 50,752 | 50.947799 | 226 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/MegaBatchMarginLoss.py | from .. import util
import torch
from torch import nn, Tensor
from typing import Iterable, Dict
import torch.nn.functional as F
class MegaBatchMarginLoss(nn.Module):
"""
Loss function inspired from ParaNMT paper:
https://www.aclweb.org/anthology/P18-1042/
Given a large batch (like 500 or more examples... | 5,229 | 51.828283 | 209 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/BatchHardTripletLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from sentence_transformers import util
from sentence_transformers.SentenceTransformer import SentenceTransformer
class BatchHardTripletLossDistanceFunction:
"""
This class defines distance functions, that can be us... | 9,398 | 45.300493 | 162 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/MultipleNegativesRankingLoss.py | import torch
from torch import nn, Tensor
from typing import Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
from .. import util
class MultipleNegativesRankingLoss(nn.Module):
"""
This loss expects as input a batch consisting of sentence pairs (a_1, p_1), (a_2, p_2)..., (a_n, p_n)
... | 3,613 | 47.837838 | 157 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/SimCLRLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
import logging
LARGE_NUM = 1e9
class MLP1(nn.Module):
def __init__(self, hidden_dim=2048, norm=None, activation="relu"): # bottleneck structure
super().__i... | 10,167 | 48.120773 | 155 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/BatchAllTripletLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from .BatchHardTripletLoss import BatchHardTripletLoss, BatchHardTripletLossDistanceFunction
from sentence_transformers.SentenceTransformer import SentenceTransformer
class BatchAllTripletLoss(nn.Module):
"""
Batch... | 4,700 | 50.659341 | 162 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/BatchSemiHardTripletLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from .BatchHardTripletLoss import BatchHardTripletLoss, BatchHardTripletLossDistanceFunction
from sentence_transformers.SentenceTransformer import SentenceTransformer
class BatchSemiHardTripletLoss(nn.Module):
"""
... | 5,586 | 48.442478 | 162 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/AdvCLSoftmaxLoss_refactoring.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
import logging
LARGE_NUM = 1e9
class MLP(torch.nn.Module):
def __init__(self,
input_dim: int,
hidden_dim: int,
... | 21,694 | 51.026379 | 179 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/__init__.py | from .CosineSimilarityLoss import *
from .SoftmaxLoss import *
from .AdvCLSoftmaxLoss import *
from .MultipleNegativesRankingLoss import *
from .TripletLoss import *
from .MSELoss import *
from .ContrastiveLoss import *
from .OnlineContrastiveLoss import *
from .MegaBatchMarginLoss import *
# Triplet losses
from .Batc... | 519 | 29.588235 | 45 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/OnlineContrastiveLoss.py | from typing import Iterable, Dict
import torch.nn.functional as F
from torch import nn, Tensor
from .ContrastiveLoss import SiameseDistanceMetric
from sentence_transformers.SentenceTransformer import SentenceTransformer
class OnlineContrastiveLoss(nn.Module):
"""
Online Contrastive loss. Similar to Constrativ... | 2,732 | 51.557692 | 162 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/ContrastiveLoss.py | from enum import Enum
from typing import Iterable, Dict
import torch.nn.functional as F
from torch import nn, Tensor
from sentence_transformers.SentenceTransformer import SentenceTransformer
class SiameseDistanceMetric(Enum):
"""
The metric for the contrastive loss
"""
EUCLIDEAN = lambda x, y: F.pai... | 2,794 | 44.080645 | 162 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/SoftmaxLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
import logging
class SoftmaxLoss(nn.Module):
"""
This loss was used in our SBERT publication (https://arxiv.org/abs/1908.10084) to train the SentenceTransformer
... | 3,637 | 45.050633 | 152 | py |
SLT-FAI | SLT-FAI-main/sentence_transformers/losses/AdvSimSiamLoss.py | import torch
from torch import nn, Tensor
from typing import Union, Tuple, List, Iterable, Dict
from ..SentenceTransformer import SentenceTransformer
import logging
LARGE_NUM = 1e9
class MLP(torch.nn.Module):
def __init__(self,
input_dim: int,
hidden_dim: int,
... | 25,701 | 49.794466 | 179 | py |
robust_trust_region | robust_trust_region-main/wrapper/bilateralfilter/setup.py | #File: setup.py
#!/usr/bin/python
from distutils.core import setup, Extension
# Third-party modules - we depend on numpy for everything
import numpy
# Obtain the numpy include directory. This logic works across numpy versions.
try:
numpy_include = numpy.get_include()
except AttributeError:
numpy_include = nu... | 1,000 | 31.290323 | 78 | py |
robust_trust_region | robust_trust_region-main/wrapper/bilateralfilter/bilateralfilter.py | # This file was automatically generated by SWIG (http://www.swig.org).
# Version 3.0.8
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info
if version_info >= (2, 6, 0):
def swig_import_helper():
from os.path imp... | 9,684 | 30.343042 | 99 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/inference.py | import argparse
import os
import numpy as np
from tqdm import tqdm
from PIL import Image
import matplotlib.pyplot as plt
from torchvision import transforms
from torch.autograd import Variable
from mypath import Path
from dataloaders import make_data_loader
from dataloaders.custom_transforms import denormalizeimage
fro... | 5,786 | 37.58 | 111 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/DenseCRFLoss.py | import torch
import torch.nn as nn
from torch.autograd import Function
from torch.autograd import Variable
import torch.nn.functional as F
import numpy as np
import sys
sys.path.append("../wrapper/bilateralfilter/build/lib.linux-x86_64-3.6")
from bilateralfilter import bilateralfilter, bilateralfilter_batch
from datalo... | 2,810 | 39.157143 | 117 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/mypath.py | import os
class Path(object):
@staticmethod
def db_root_dir(dataset):
data_root = os.environ['DATA_ROOT']
if dataset == 'pascal':
# folder that contains pascal/. It should have three subdirectories
# called "JPEGImages", "SegmentationClassAug", and "pascal_2012_scribble"... | 914 | 42.571429 | 103 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/GridCRFLoss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import sys
import math
from dataloaders.custom_transforms import denormalizeimage
from itertools import repeat
class BilinearPottsRelaxation(object):
@staticmethod
def comute(a, b):
return a * (1 - b)
class TVPotts... | 3,859 | 34.740741 | 117 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/train_with_dcr.py | import os, sys
import argparse
import math
import time
from tqdm import tqdm
import numpy as np
import torchvision
import torch
import torch.nn.functional as F
from mypath import Path
from dataloaders import make_data_loader
from dataloaders.utils import decode_seg_map_sequence, normalize_image_to_range
from dataloa... | 26,384 | 46.455036 | 147 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/train_withdensecrfloss.py | import argparse
import os, time
import numbers
import json
import numpy as np
from tqdm import tqdm
from mypath import Path
from dataloaders import make_data_loader
from dataloaders.custom_transforms import denormalizeimage
from modeling.sync_batchnorm.replicate import patch_replication_callback
from modeling.deeplab ... | 16,448 | 44.31405 | 155 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/train.py | import numbers
import json
from tqdm import tqdm
import torch, torchvision
import torch.nn.functional as F
from modeling.deeplab import *
from dataloaders.utils import decode_seg_map_sequence, normalize_image_to_range
from dataloaders import make_data_loader
from utils.lr_scheduler import LR_Scheduler
from utils.save... | 7,395 | 41.751445 | 124 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/AlphaExpansion.py | import torch
import alphaexpansion
import torch.nn as nn
from torch.autograd import Function
from torch.autograd import Variable
import torch.nn.functional as F
import numpy as np
import sys, warnings
from datetime import datetime
class AlphaExpansion(nn.Module):
def __init__(self, max_iter, potts_weight, ce_weig... | 4,248 | 41.49 | 123 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/dataloaders/custom_transforms.py | import torch
import torch.nn.functional as F
import random
import numpy as np
from PIL import Image, ImageOps, ImageFilter
class Normalize(object):
"""Normalize a tensor image with mean and standard deviation.
Args:
mean (tuple): means for each channel.
std (tuple): standard deviations for eac... | 10,938 | 31.363905 | 124 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/dataloaders/utils.py | import numpy as np
import torch
def decode_seg_map_sequence(label_masks, dataset='pascal'):
rgb_masks = []
for label_mask in label_masks:
rgb_mask = decode_segmap(label_mask, dataset)
rgb_masks.append(rgb_mask)
rgb_masks = torch.from_numpy(np.array(rgb_masks).transpose([0, 3, 1, 2]))
re... | 3,959 | 33.137931 | 84 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/dataloaders/__init__.py | from torch.utils.data import DataLoader, dataset
from dataloaders.datasets import combine_dbs, indexed_dataset
import numpy as np
def make_data_loader(args, proposal_generator=None, **kwargs):
def wrap_dataset(set):
if 'single_image_training' in args and args.single_image_training is not None:
if ar... | 2,887 | 44.84127 | 113 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/dataloaders/datasets/cityscapes.py | import os
import numpy as np
import scipy.misc as m
from PIL import Image
from torch.utils import data
from mypath import Path
from torchvision import transforms
from dataloaders import custom_transforms as tr
class CityscapesSegmentation(data.Dataset):
NUM_CLASSES = 19
def __init__(self, args, root=Path.db_r... | 5,370 | 35.537415 | 103 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/dataloaders/datasets/pascal.py | from __future__ import print_function, division
import os
from PIL import Image
import numpy as np
import torch
from torch.utils.data import Dataset
from mypath import Path
from torchvision import transforms
from dataloaders import custom_transforms as tr
class VOCSegmentation(Dataset):
"""
PascalVoc dataset
... | 7,403 | 33.598131 | 105 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/dataloaders/datasets/sbd.py | from __future__ import print_function, division
import os
import numpy as np
import scipy.io
import torch.utils.data as data
from PIL import Image
from mypath import Path
from torchvision import transforms
from dataloaders import custom_transforms as tr
class SBDSegmentation(data.Dataset):
NUM_CLASSES = 21
... | 4,081 | 30.643411 | 106 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/dataloaders/datasets/__init__.py | 0 | 0 | 0 | py | |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/dataloaders/datasets/indexed_dataset.py | import torch.utils.data.dataset
class IndexedDataset(torch.utils.data.dataset.Dataset):
def __init__(self, base):
self.base = base
def __getitem__(self, index):
sample = self.base[index]
sample["index"] = index
return sample
def __len__(self):
return len(self.base)... | 321 | 22 | 55 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/dataloaders/datasets/combine_dbs.py | import torch.utils.data as data
class CombineDBs(data.Dataset):
NUM_CLASSES = 21
def __init__(self, dataloaders, excluded=None):
self.dataloaders = dataloaders
self.excluded = excluded
self.im_ids = []
# Combine object lists
for dl in dataloaders:
for elem ... | 3,310 | 32.11 | 96 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/dataloaders/datasets/coco.py | import numpy as np
import torch
from torch.utils.data import Dataset
from mypath import Path
from tqdm import trange
import os
from pycocotools.coco import COCO
from pycocotools import mask
from torchvision import transforms
from dataloaders import custom_transforms as tr
from PIL import Image, ImageFile
ImageFile.LOAD... | 5,636 | 34.012422 | 96 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/utils/lr_scheduler.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## ECE Department, Rutgers University
## Email: [email protected]
## Copyright (c) 2017
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this sou... | 2,619 | 34.890411 | 83 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/utils/log_lin_softmax.py | import torch
from torch.autograd import Function
from torch.autograd import Variable
import torch.nn.functional as F
class LogLinSoftmax(Function):
# computes log(a + b * s_ijkl) where s_ijkl is softmax of the input
@staticmethod
def forward(ctx, a, b, logits, dim):
ctx.dim, ctx.a, ctx.b = dim, a... | 2,569 | 31.948718 | 88 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/utils/proposal_generator.py | import multiprocessing as mp
import tempfile, shutil, os
import io, pickle
import torch
import torch.nn.functional as F
import gzip
class AlphaBasedProposalGenerator(object):
def __init__(self, alpha_expansion, eps=0):
self.alpha_expansion = alpha_expansion
self.model = None
self.eps = eps... | 3,035 | 27.373832 | 84 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/utils/saver.py | import os
import shutil
import torch
from collections import OrderedDict
import glob
class Saver(object):
def __init__(self, args):
self.args = args
self.directory = os.path.join('run', args.dataset + args.train_dataset_suffix, args.checkname)
self.runs = sorted(glob.glob(os.path.join(self... | 2,581 | 39.34375 | 109 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/utils/vis.py | import torch
def get_edges(seg_map):
edges = torch.zeros_like(seg_map) == 1
edges[..., :-1, :] |= seg_map[..., :-1, :] != seg_map[..., 1:, :]
edges[..., :, :-1] |= seg_map[..., :, :-1] != seg_map[..., :, 1:]
edges[..., 1:, :] |= seg_map[..., :-1, :] != seg_map[..., 1:, :]
edges[..., :, 1:] |= seg... | 378 | 36.9 | 69 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/utils/calculate_weights.py | import os
from tqdm import tqdm
import numpy as np
from mypath import Path
def calculate_weigths_labels(dataset, dataloader, num_classes):
# Create an instance from the data loader
z = np.zeros((num_classes,))
# Initialize tqdm
tqdm_batch = tqdm(dataloader)
print('Calculating classes weights')
... | 985 | 33 | 98 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/utils/loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class SegmentationLosses(object):
def __init__(self, weight=None, reduction_mode='mean', batch_average=True, ignore_index=255, cuda=False):
self.ignore_index = ignore_index
self.weight = weight
self.reduction_mode = reductio... | 3,977 | 33.894737 | 109 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/utils/metrics.py | import numpy as np
class Evaluator(object):
def __init__(self, num_class):
self.num_class = num_class
self.confusion_matrix = np.zeros((self.num_class,)*2)
def Pixel_Accuracy(self):
Acc = np.diag(self.confusion_matrix).sum() / self.confusion_matrix.sum()
return Acc
def Pi... | 1,903 | 37.08 | 99 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/utils/summaries.py | import os
import torch
import numpy as np
import scipy.ndimage
from torchvision.utils import make_grid
from tensorboardX import SummaryWriter
from dataloaders.utils import decode_seg_map_sequence
from utils import vis
class TensorboardSummary(object):
def __init__(self, directory):
self.directory = directo... | 1,691 | 42.384615 | 108 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/modeling/aspp.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
class _ASPPModule(nn.Module):
def __init__(self, inplanes, planes, kernel_size, padding, dilation, BatchNorm):
super(_ASPPModule, self).__init__()
sel... | 3,602 | 36.926316 | 116 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/modeling/decoder.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
class Decoder(nn.Module):
def __init__(self, num_classes, backbone, BatchNorm, skip=False):
super(Decoder, self).__init__()
if backbone == 'resnet' or... | 3,606 | 34.019417 | 104 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/modeling/deeplab.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
from modeling.aspp import build_aspp
from modeling.decoder import build_decoder
from modeling.backbone import build_backbone
def freeze_batchnorm(self):
for m in self.modules():... | 2,898 | 30.857143 | 93 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/modeling/__init__.py | 0 | 0 | 0 | py | |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/modeling/backbone/resnet.py | import math
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, BatchNorm=None):
super(Bottleneck, se... | 6,222 | 37.41358 | 130 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/modeling/backbone/drn.py | import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
webroot = 'https://tigress-web.princeton.edu/~fy/drn/models/'
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'drn-c-26': we... | 14,649 | 35.352357 | 100 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/modeling/backbone/__init__.py | from modeling.backbone import resnet, xception, drn, mobilenet
def build_backbone(backbone, output_stride, BatchNorm):
if backbone == 'resnet':
return resnet.ResNet101(output_stride, BatchNorm)
elif backbone == 'xception':
return xception.AlignedXception(output_stride, BatchNorm)
elif backb... | 514 | 35.785714 | 65 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/modeling/backbone/xception.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
def fixed_padding(inputs, kernel_size, dilation):
kernel_size_effective = kernel_size + (kernel_size - 1) * (dilation - 1)
... | 11,553 | 39.118056 | 116 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/modeling/backbone/mobilenet.py | import torch
import torch.nn.functional as F
import torch.nn as nn
import math
from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
import torch.utils.model_zoo as model_zoo
def conv_bn(inp, oup, stride, BatchNorm):
return nn.Sequential(
nn.Conv2d(inp, oup, 3, stride, 1, bias=False),
... | 5,390 | 34.467105 | 110 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/modeling/sync_batchnorm/replicate.py | # -*- coding: utf-8 -*-
# File : replicate.py
# Author : Jiayuan Mao
# Email : [email protected]
# 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.dat... | 3,218 | 35.579545 | 115 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/modeling/sync_batchnorm/unittest.py | # -*- coding: utf-8 -*-
# File : unittest.py
# Author : Jiayuan Mao
# Email : [email protected]
# 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 numpy as np
from torc... | 834 | 26.833333 | 157 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/modeling/sync_batchnorm/batchnorm.py | # -*- coding: utf-8 -*-
# File : batchnorm.py
# Author : Jiayuan Mao
# Email : [email protected]
# 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... | 12,932 | 44.861702 | 116 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/modeling/sync_batchnorm/comm.py | # -*- coding: utf-8 -*-
# File : comm.py
# Author : Jiayuan Mao
# Email : [email protected]
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
import queue
import collections
import threading
... | 4,440 | 33.161538 | 117 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/modeling/sync_batchnorm/__init__.py | # -*- coding: utf-8 -*-
# File : __init__.py
# Author : Jiayuan Mao
# Email : [email protected]
# Date : 27/01/2018
#
# This file is part of Synchronized-BatchNorm-PyTorch.
# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch
# Distributed under MIT License.
from .batchnorm import SynchronizedBatchNorm1... | 447 | 36.333333 | 96 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/doc/deeplab_resnet.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
BatchNorm2d = SynchronizedBatchNorm2d
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, ... | 11,247 | 34.594937 | 111 | py |
robust_trust_region | robust_trust_region-main/pytorch-deeplab_v3_plus/doc/deeplab_xception.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
BatchNorm2d = SynchronizedBatchNorm2d
class SeparableConv2d(nn.Module):
def __init__(self, inplanes, planes, kernel_size=3,... | 16,199 | 37.117647 | 127 | py |
f2fs-stable-linux-3.18.y | f2fs-stable-linux-3.18.y/tools/perf/python/twatch.py | #! /usr/bin/python
# -*- python -*-
# -*- coding: utf-8 -*-
# twatch - Experimental use of the perf python interface
# Copyright (C) 2011 Arnaldo Carvalho de Melo <[email protected]>
#
# This application is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License... | 1,316 | 30.357143 | 75 | py |
f2fs-stable-linux-3.18.y | f2fs-stable-linux-3.18.y/tools/perf/util/setup.py | #!/usr/bin/python2
from distutils.core import setup, Extension
from os import getenv
from distutils.command.build_ext import build_ext as _build_ext
from distutils.command.install_lib import install_lib as _install_lib
class build_ext(_build_ext):
def finalize_options(self):
_build_ext.finalize_optio... | 1,543 | 30.510204 | 82 | py |
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