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
value |
|---|---|---|---|---|---|---|
NORPPA | NORPPA-main/datasets.py | import os
from pathlib import Path
from tools import read_image
import csv
import numpy as np
from torch.utils.data import Dataset
import os
class DatasetSlice(Dataset):
def __init__(self, dataset, slice=None):
self.dataset = dataset
self.slice = (0, len(self.dataset)) if slice is None else slice
... | 8,014 | 29.708812 | 106 | py |
NORPPA | NORPPA-main/vis_new_pattern.py | import os
# import sys
# sys.path.append('/ekaterina/work/src/NORPPA/repository/NORPPA')
os.environ["CUDA_VISIBLE_DEVICES"]="1"
from config_whaleshark import config
import matplotlib.pyplot as plt
from pathlib import Path
import numpy as np
import zipfile
import tensorflow as tf
import wget
import pickle
physical_dev... | 3,022 | 34.564706 | 136 | py |
NORPPA | NORPPA-main/config_whaleshark.py |
import sys
from pathlib import Path
import cv2
import numpy as np
file_folder = Path(__file__).resolve().parent
sys.path.append(str(file_folder / "reidentification/hesaff_pytorch"))
from HessianAffinePatches import init_affnet, init_orinet, init_hardnet
from segmentation.detectron_segment import create_predicto... | 4,166 | 41.520408 | 131 | py |
NORPPA | NORPPA-main/codebooks_whaleshark.py | import os
# import sys
# sys.path.append('/ekaterina/work/src/NORPPA/repository/NORPPA')
os.environ["CUDA_VISIBLE_DEVICES"]="1"
from config_whaleshark import config
import matplotlib.pyplot as plt
from pathlib import Path
import numpy as np
import zipfile
import tensorflow as tf
import wget
import pickle
physical_dev... | 1,668 | 30.490566 | 111 | py |
NORPPA | NORPPA-main/segmentation/train_dataset.py | from pathlib import Path
import os
import pycocotools
from PIL import Image
import numpy as np
from detectron2.structures import BoxMode
def create_dataset_json(full_dir, segmented_dir, keyword="", suffix=".result.png"):
full_dir = Path(full_dir)
segmented_dir = Path(segmented_dir)
result = []
counter ... | 1,616 | 34.933333 | 93 | py |
NORPPA | NORPPA-main/segmentation/segmentation.py | from segmentation.detectron_segment import detectron_segment
def add_instance_info(label, instance, num_instances):
if type(label) is dict:
label["instance"] = instance
label["num_instances"] = num_instances
return label
else:
return (label, instance)
def segment(input, predic... | 733 | 30.913043 | 108 | py |
NORPPA | NORPPA-main/segmentation/detectron_segment.py | from argparse import ArgumentParser
from PIL import Image
from detectron2 import model_zoo
from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor
import numpy as np
import cv2
import rawpy
from pathlib import Path
def is_raw_image(filename):
return filename.lower().endswith(('cr2', 'p... | 3,654 | 32.842593 | 105 | py |
NORPPA | NORPPA-main/reidentification/geometric.py | from skimage.measure import label
from sklearn.decomposition import KernelPCA
from skimage.morphology import convex_hull_image, skeletonize
from cyvlfeat.fisher import fisher
from PIL import Image
import math
from sql import *
import torch
from torchvision import transforms
import pickle
from reidentification.encodi... | 4,128 | 32.298387 | 111 | py |
NORPPA | NORPPA-main/reidentification/visualisation.py | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from reidentification.identify import fisher_single, do_matching
from reidentification.encoding_utils import calculate_dists
def rescale_img(img, scale):
return img.resize([int(s*scale) for s in img.size], Image.Resampling.LANCZOS)
def resi... | 6,875 | 43.36129 | 282 | py |
NORPPA | NORPPA-main/reidentification/encoding_utils.py | from scipy.spatial.distance import cdist
from sklearn.decomposition import IncrementalPCA
from cyvlfeat.gmm import gmm
from cyvlfeat.fisher import fisher
from scipy.spatial.distance import cdist, cosine
import numpy as np
import os
import shutil
from PIL import Image
import io
from base64 import encodebytes
from sklear... | 4,071 | 29.616541 | 149 | py |
NORPPA | NORPPA-main/reidentification/find_matches.py | from PIL import Image
import matplotlib.pyplot as plt
import numpy as np
from reidentification.identify import fisher_single, do_matching
from reidentification.encoding_utils import calculate_dists
def find_matches(identification_result, cfg):
matches, query_labels = identification_result
query_images = query... | 1,666 | 38.690476 | 101 | py |
NORPPA | NORPPA-main/reidentification/identify.py | from skimage.measure import label
from sklearn.decomposition import KernelPCA
from skimage.morphology import convex_hull_image, skeletonize
from cyvlfeat.fisher import fisher
from PIL import Image
import math
from sql import *
import torch
from torchvision import transforms
import pickle
from reidentification.encodi... | 16,717 | 33.328542 | 155 | py |
NORPPA | NORPPA-main/reidentification/hesaff_pytorch/HandCraftedModules.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import math
import numpy as np
from Utils import GaussianBlur, CircularGaussKernel
from LAF import abc2A,rectifyAffineTransformationUpIsUp, sc_y_x2LAFs
from Utils import generate_2dgrid, generate_2dgrid, generate_3dg... | 13,280 | 43.27 | 145 | py |
NORPPA | NORPPA-main/reidentification/hesaff_pytorch/HardNet.py | import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
import time
import os
import math
import numpy as np
class L2Norm(nn.Module):
def __init__(self):
super(L2Norm,self).__init__()
self.eps = 1e-8
... | 3,589 | 34.544554 | 155 | py |
NORPPA | NORPPA-main/reidentification/hesaff_pytorch/HessianAffinePatches.py | import torch
import torch.nn as nn
import numpy as np
from torch.autograd import Variable
from SparseImgRepresenter import ScaleSpaceAffinePatchExtractor
from LAF import denormalizeLAFs, LAFs2ell
from Utils import line_prepender
from architectures import AffNetFast, OriNetFast
from skimage.filters import unsharp_mask... | 3,848 | 36.735294 | 111 | py |
NORPPA | NORPPA-main/reidentification/hesaff_pytorch/LAF.py | import numpy as np
import matplotlib.pyplot as plt
from copy import deepcopy
from scipy.spatial.distance import cdist
from numpy.linalg import inv
from scipy.linalg import schur, sqrtm
import torch
from torch.autograd import Variable
import torch.nn.functional as F
##########numpy
def invSqrt(a,b,c):
eps = 1e-... | 17,704 | 36.352321 | 142 | py |
NORPPA | NORPPA-main/reidentification/hesaff_pytorch/SparseImgRepresenter.py | import torch
import torch.nn as nn
import numpy as np
import math
import torch.nn.functional as F
from torch.autograd import Variable
from copy import deepcopy
from Utils import GaussianBlur, batch_eig2x2, line_prepender, batched_forward
from LAF import LAFs2ell,abc2A, angles2A, generate_patch_grid_from_normalized_LAFs... | 10,911 | 49.753488 | 231 | py |
NORPPA | NORPPA-main/reidentification/hesaff_pytorch/ReprojectonStuff.py | import torch
from torch.autograd import Variable
import numpy as np
from LAF import rectifyAffineTransformationUpIsUp
from Utils import zeros_like
def distance_matrix_vector(anchor, positive):
"""Given batch of anchor descriptors and positive descriptors calculate distance matrix"""
d1_sq = torch.sum(anchor * ... | 6,844 | 45.25 | 177 | py |
NORPPA | NORPPA-main/reidentification/hesaff_pytorch/Utils.py | import torch
import torch.nn.init
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import cv2
import numpy as np
# resize image to size 32x32
cv2_scale = lambda x: cv2.resize(x, dsize=(32, 32),
interpolation=cv2.INTER_LINEAR)
# reshape image
np_... | 6,244 | 33.125683 | 144 | py |
NORPPA | NORPPA-main/reidentification/hesaff_pytorch/architectures.py | from __future__ import division, print_function
import os
import errno
import numpy as np
import sys
from copy import deepcopy
import math
import torch
import torch.nn.init
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision.transforms as transforms
from torch.autograd i... | 36,272 | 45.32567 | 223 | py |
NORPPA | NORPPA-main/reidentification/hesaff_pytorch/extract_features_oxaff.py | import torch
import torch.nn as nn
import numpy as np
import sys
import time
from PIL import Image
from torch.autograd import Variable
from SparseImgRepresenter import ScaleSpaceAffinePatchExtractor
from LAF import denormalizeLAFs, LAFs2ell
from Utils import line_prepender
USE_CUDA = False
try:
input_img_fname =... | 1,140 | 27.525 | 107 | py |
NORPPA | NORPPA-main/reidentification/hesaff_pytorch/pytorch_sift.py | import torch
import math
import torch.nn.init
import torch.nn as nn
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
import numpy as np
class L2Norm(nn.Module):
def __init__(self):
super(L2Norm,self).__init__()
self.eps = 1e-10
def forward(self, x):
norm = torch.... | 4,815 | 42.781818 | 129 | py |
NORPPA | NORPPA-main/tonemapping/tonemapping.py | """
The script peforms tone mapping.
Usage:
python correct.py -s <source_dir_path> -d <dest_dir_path>
There is no need to create the directory for the results manually since it will
be generated automatically preseving the structure of the source directory.
"""
from argparse import ArgumentParser
import json
import... | 2,371 | 25.065934 | 195 | py |
NORPPA | NORPPA-main/pattern_extraction/extract_pattern.py | from math import ceil, floor
import numpy as np
from PIL import ImageFile
import tensorflow as tf
from math import ceil, floor
from pattern_extraction.model import *
from pattern_extraction.utils import *
from pathlib import Path
import skimage.transform as trans
file_folder = Path(__file__).resolve().parent
ImageFi... | 1,445 | 27.92 | 109 | py |
NORPPA | NORPPA-main/pattern_extraction/utils.py | import numpy as np
import skimage.transform as trans
from skimage.morphology import skeletonize
from PIL import Image
from math import ceil, floor
def crop(img_path, flag_multi_class=False):
img = np.asarray(Image.open(img_path).convert('L'))
size_y, size_x = img.shape
where = np.where(img!=0)
y1, ... | 2,283 | 28.662338 | 113 | py |
NORPPA | NORPPA-main/pattern_extraction/model.py | import numpy as np
import os
import skimage.io as io
import skimage.transform as trans
import numpy as np
from tensorflow.keras.models import *
from tensorflow.keras.layers import *
from tensorflow.keras.optimizers import *
from tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler
from tensorflow.k... | 3,797 | 56.545455 | 132 | py |
llm_expository | llm_expository-main/ChatGPT_generated_code_01.py | import numpy as np
import matplotlib.pyplot as plt
def ewma(x, y, alpha):
return (1 - alpha) * x + alpha * y
def simulate_ewma(n, alpha, k, num_simulations):
arls = []
for i in range(num_simulations):
x = np.random.normal(0, 1, n)
ewma_stat = np.zeros(n)
ewma_stat[0] = x[0]
... | 792 | 24.580645 | 60 | py |
robust-transformers | robust-transformers-main/conftest.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 2,846 | 35.037975 | 107 | py |
robust-transformers | robust-transformers-main/setup.py | # Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 14,253 | 33.019093 | 259 | py |
robust-transformers | robust-transformers-main/hubconf.py | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 8,496 | 51.450617 | 189 | py |
robust-transformers | robust-transformers-main/examples/research_projects/longform-qa/eli5_app.py | import datasets
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
import faiss
import transformers
from eli5_utils import (
embed_questions_for_retrieval,
make_qa_s2s_model,
qa_s2s_generate,
query_es_index,
query_qa_dense_index,
)
from transformers impor... | 13,474 | 37.28125 | 159 | py |
robust-transformers | robust-transformers-main/examples/research_projects/longform-qa/eli5_utils.py | import functools
import math
import os # noqa: F401
from random import choice, randint
from time import time
import datasets # noqa: F401
import numpy as np
import pandas as pd
import torch
import torch.utils.checkpoint as checkpoint
from elasticsearch import Elasticsearch # noqa: F401
from elasticsearch.helpers im... | 28,299 | 40.07402 | 119 | py |
robust-transformers | robust-transformers-main/examples/research_projects/codeparrot/scripts/preprocessing.py | import gzip
import multiprocessing
import os
import shutil
import time
import numpy as np
from datasets import load_dataset
from arguments import PreprocessingArguments
from transformers import HfArgumentParser
def get_hash(example):
"""Get hash of content field."""
return {"hash": hash(example["content"])}... | 3,864 | 30.422764 | 100 | py |
robust-transformers | robust-transformers-main/examples/research_projects/codeparrot/scripts/arguments.py | from dataclasses import dataclass, field
from typing import Optional
@dataclass
class TrainingArguments:
"""
Configuration for training model.
"""
model_ckpt: Optional[str] = field(
default="lvwerra/codeparrot",
metadata={"help": "Model name or path of model to be trained."},
)
... | 8,452 | 44.446237 | 174 | py |
robust-transformers | robust-transformers-main/examples/research_projects/codeparrot/scripts/codeparrot_training.py | import logging
from argparse import Namespace
from pathlib import Path
import datasets
import torch
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.tensorboard import SummaryWriter
import transformers
import wandb
from ... | 9,194 | 37.153527 | 119 | py |
robust-transformers | robust-transformers-main/examples/research_projects/codeparrot/scripts/initialize_model.py | from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
parser = HfArgumentParser(InitializationArguments)
args = parser.parse_args()
# Load codeparrot tokenizer trained for Python code tokenization
tokenizer = AutoToken... | 857 | 36.304348 | 112 | py |
robust-transformers | robust-transformers-main/examples/research_projects/codeparrot/scripts/validation_loss.py | import logging
import torch
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from accelerate import Accelerator
from arguments import EvaluationArguments
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set... | 3,496 | 33.97 | 114 | py |
robust-transformers | robust-transformers-main/examples/research_projects/codeparrot/scripts/human_eval.py | import json
import multiprocessing
import os
import re
from datasets import load_dataset, load_metric
from tqdm import tqdm
import transformers
from arguments import HumanEvalArguments
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
HfArgumentParser,
StoppingCriteria,
StoppingCrite... | 4,789 | 36.716535 | 147 | py |
robust-transformers | robust-transformers-main/examples/research_projects/codeparrot/scripts/bpe_training.py | from datasets import load_dataset
from tqdm import tqdm
from arguments import TokenizerTrainingArguments
from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
# Iterator for Training
def batch_iterator(batch_size=10):
for _ in tqdm(range(... | 1,015 | 29.787879 | 80 | py |
robust-transformers | robust-transformers-main/examples/research_projects/bertology/run_prune_gpt.py | #!/usr/bin/env python3
""" This script is adapted from the Bertology pruning code (https://github.com/huggingface/transformers/blob/783d7d2629e97c5f0c5f9ef01b8c66410275c204/examples/research_projects/bertology/run_bertology.py)
to prune GPT-like models. The author is @altsoph.
"""
import argparse
import logging
import... | 15,469 | 38.666667 | 204 | py |
robust-transformers | robust-transformers-main/examples/research_projects/bertology/run_bertology.py | #!/usr/bin/env python3
# Copyright 2018 CMU and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requir... | 18,572 | 40.181818 | 118 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag/use_own_knowledge_dataset.py | import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import torch
from datasets import Features, Sequence, Value, load_dataset
import faiss
from transformers import (
DPRCo... | 8,174 | 38.878049 | 152 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag/consolidate_rag_checkpoint.py | """
A script creating a RAG checkpoint from a generator and a question encoder checkpoints.
"""
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def consolidate(
model_type,
generator_name_or_path: str,
... | 3,640 | 35.41 | 124 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag/utils_rag.py | import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transfo... | 8,114 | 32.122449 | 118 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag/finetune_rag.py | """Finetuning script for RAG models. Adapted from examples.seq2seq.finetune.py"""
import argparse
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Any, Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
import... | 25,623 | 40.462783 | 197 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag/distributed_pytorch_retriever.py | import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
logger = logging.getLogger(__name__)
class RagPyTorchDistributedRetriever(RagRetriever):
"""
A distributed retriever built on top of ... | 6,539 | 46.05036 | 155 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag/test_distributed_retriever.py | import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
import faiss
from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transform... | 13,794 | 39.693215 | 118 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag/eval_rag.py | """ Evaluation script for RAG models."""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as trans... | 11,101 | 34.469649 | 132 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag/lightning_base.py | import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
... | 15,609 | 37.734491 | 124 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag/callbacks_rag.py | import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def count_trainable_parameters(model):
model_parame... | 4,428 | 36.854701 | 126 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag/parse_dpr_relevance_data.py | """
This script reads DPR retriever training data and parses each datapoint. We save a line per datapoint.
Each line consists of the query followed by a tab-separated list of Wikipedia page titles constituting
positive contexts for a given query.
"""
import argparse
import json
from tqdm import tqdm
def main():
... | 1,353 | 27.208333 | 102 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag/_test_finetune_rag.py | import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.ba... | 3,969 | 34.765766 | 85 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag/__init__.py | import os
import sys
sys.path.insert(1, os.path.dirname(os.path.realpath(__file__)))
| 87 | 13.666667 | 63 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag/distributed_ray_retriever.py | import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
logger = logging.getLogger(__name__)
class RayRetriever:
def __init__(self):
self.initialized = False
def create_rag_retriever(self... | 7,185 | 46.276316 | 132 | py |
robust-transformers | robust-transformers-main/examples/research_projects/pplm/run_pplm.py | #! /usr/bin/env python3
# coding=utf-8
# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless ... | 29,044 | 34.078502 | 182 | py |
robust-transformers | robust-transformers-main/examples/research_projects/pplm/run_pplm_discrim_train.py | #! /usr/bin/env python3
# coding=utf-8
# Copyright (c) 2019 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless ... | 18,788 | 34.92543 | 117 | py |
robust-transformers | robust-transformers-main/examples/research_projects/pplm/pplm_classification_head.py | from torch import nn
class ClassificationHead(nn.Module):
"""Classification Head for transformer encoders"""
def __init__(self, class_size, embed_size):
super().__init__()
self.class_size = class_size
self.embed_size = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size... | 651 | 31.6 | 68 | py |
robust-transformers | robust-transformers-main/examples/research_projects/deebert/test_glue_deebert.py | import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger()
def get_setup_file():
parser = argparse.... | 3,690 | 34.152381 | 109 | py |
robust-transformers | robust-transformers-main/examples/research_projects/deebert/run_glue_deebert.py | from __future__ import absolute_import, division, print_function
import argparse
import glob
import logging
import os
import random
import time
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from torch.utils.data.distribute... | 31,693 | 42.297814 | 150 | py |
robust-transformers | robust-transformers-main/examples/research_projects/deebert/src/modeling_highway_bert.py | import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEmbeddings,
BertLayer,
... | 17,668 | 43.506297 | 172 | py |
robust-transformers | robust-transformers-main/examples/research_projects/deebert/src/__init__.py | 0 | 0 | 0 | py | |
robust-transformers | robust-transformers-main/examples/research_projects/deebert/src/modeling_highway_roberta.py | from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta... | 6,791 | 42.261146 | 172 | py |
robust-transformers | robust-transformers-main/examples/research_projects/lxmert/modeling_frcnn.py | """
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal
Adapted From Facebook Inc, Detectron2 && Huggingface Co.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www... | 73,726 | 37.359521 | 152 | py |
robust-transformers | robust-transformers-main/examples/research_projects/lxmert/extracting_data.py | import getopt
import json
import os
# import numpy as np
import sys
from collections import OrderedDict
import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess
from utils import Config
"""
USAGE:
``python extracting_data.py -i <img_dir> -o ... | 5,254 | 34.033333 | 109 | py |
robust-transformers | robust-transformers-main/examples/research_projects/lxmert/utils.py | """
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal, Huggingface team :)
Adapted From Facebook Inc, Detectron2
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://w... | 18,199 | 31.5 | 143 | py |
robust-transformers | robust-transformers-main/examples/research_projects/lxmert/visualizing_image.py | """
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal
Adapted From Facebook Inc, Detectron2
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/license... | 13,420 | 25.842 | 100 | py |
robust-transformers | robust-transformers-main/examples/research_projects/lxmert/processing_image.py | """
coding=utf-8
Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal
Adapted From Facebook Inc, Detectron2
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/license... | 5,678 | 36.86 | 114 | py |
robust-transformers | robust-transformers-main/examples/research_projects/bertabs/modeling_bertabs.py | # MIT License
# Copyright (c) 2019 Yang Liu and the HuggingFace team
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, c... | 38,263 | 35.1322 | 114 | py |
robust-transformers | robust-transformers-main/examples/research_projects/bertabs/configuration_bertabs.py | # coding=utf-8
# Copyright 2019 The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.a... | 3,261 | 32.285714 | 147 | py |
robust-transformers | robust-transformers-main/examples/research_projects/bertabs/convert_bertabs_original_pytorch_checkpoint.py | # coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable... | 6,523 | 34.075269 | 117 | py |
robust-transformers | robust-transformers-main/examples/research_projects/bertabs/utils_summarization.py | import os
from collections import deque
import torch
from torch.utils.data import Dataset
# ------------
# Data loading
# ------------
class CNNDMDataset(Dataset):
"""Abstracts the dataset used to train seq2seq models.
The class will process the documents that are located in the specified
folder. The ... | 5,753 | 33.25 | 106 | py |
robust-transformers | robust-transformers-main/examples/research_projects/bertabs/test_utils_summarization.py | # coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 4,419 | 43.646465 | 99 | py |
robust-transformers | robust-transformers-main/examples/research_projects/bertabs/__init__.py | 0 | 0 | 0 | py | |
robust-transformers | robust-transformers-main/examples/research_projects/bertabs/run_summarization.py | #! /usr/bin/python3
import argparse
import logging
import os
import sys
from collections import namedtuple
import torch
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm
from modeling_bertabs import BertAbs, build_predictor
from transformers import BertTokenizer
from .utils_summarizati... | 10,188 | 28.278736 | 137 | py |
robust-transformers | robust-transformers-main/examples/research_projects/fsner/setup.py | import setuptools
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.setup(
name="fsner",
version="0.0.1",
author="msi sayef",
author_email="msi.sayef@gmail.com",
description="Few-shot Named Entity Recognition",
long_description=long_description,
... | 866 | 29.964286 | 99 | py |
robust-transformers | robust-transformers-main/examples/research_projects/fsner/src/fsner/tokenizer_utils.py | import torch
from transformers import AutoTokenizer
class FSNERTokenizerUtils(object):
def __init__(self, pretrained_model_name_or_path):
self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path)
def tokenize(self, x):
"""
Wrapper function for tokenizing query and... | 3,974 | 37.970588 | 182 | py |
robust-transformers | robust-transformers-main/examples/research_projects/fsner/src/fsner/model.py | import torch
from transformers import AutoModel
class FSNERModel(torch.nn.Module):
"""
The FSNER model implements a few-shot named entity recognition method from the paper `Example-Based Named Entity Recognition <https://arxiv.org/abs/2008.10570>`__ by
Morteza Ziyadi, Yuting Sun, Abhishek Goswami, Jade H... | 3,100 | 37.283951 | 169 | py |
robust-transformers | robust-transformers-main/examples/research_projects/fsner/src/fsner/__init__.py | from .model import FSNERModel
from .tokenizer_utils import FSNERTokenizerUtils
__all__ = ["FSNERModel", "FSNERTokenizerUtils"]
| 129 | 20.666667 | 48 | py |
robust-transformers | robust-transformers-main/examples/research_projects/adversarial/run_hans.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | 8,213 | 33.225 | 133 | py |
robust-transformers | robust-transformers-main/examples/research_projects/adversarial/utils_hans.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | 11,767 | 33.510264 | 118 | py |
robust-transformers | robust-transformers-main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_streaming.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | 27,868 | 41.225758 | 158 | py |
robust-transformers | robust-transformers-main/examples/research_projects/robust-speech-event/run_speech_recognition_ctc_bnb.py | #!/usr/bin/env python
# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | 31,246 | 40.006562 | 158 | py |
robust-transformers | robust-transformers-main/examples/research_projects/robust-speech-event/eval.py | #!/usr/bin/env python3
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def log_results(result: Dataset, args: Dict[str, str]):
"""DO NOT CHANGE. This function computes and logs t... | 4,716 | 33.181159 | 147 | py |
robust-transformers | robust-transformers-main/examples/research_projects/performer/run_mlm_performer.py | # coding=utf-8
# Copyright 2020 The HuggingFace Team All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless require... | 28,527 | 40.586006 | 119 | py |
robust-transformers | robust-transformers-main/examples/research_projects/performer/modeling_flax_performer.py | # coding=utf-8
# Copyright 2018 The Google Flax Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
... | 21,123 | 37.129964 | 120 | py |
robust-transformers | robust-transformers-main/examples/research_projects/performer/modeling_flax_performer_utils.py | # coding=utf-8
# Copyright 2020 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 25,683 | 37.856278 | 119 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag-end2end-retriever/use_own_knowledge_dataset.py | import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import torch
from datasets import Features, Sequence, Value, load_dataset
import faiss
from transformers import DPRContextE... | 6,909 | 39.174419 | 152 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag-end2end-retriever/utils_rag.py | import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transfo... | 8,114 | 32.122449 | 118 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag-end2end-retriever/finetune_rag.py | """Finetuning script for RAG models. Adapted from examples.seq2seq.finetune.py"""
import argparse
import copy
import json
import logging
import multiprocessing
import os
import random
import shutil
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Any, Dict, List, T... | 33,046 | 40.831646 | 197 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag-end2end-retriever/eval_rag.py | """ Evaluation script for RAG models."""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as trans... | 11,101 | 34.469649 | 132 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag-end2end-retriever/lightning_base.py | import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.plugins.training_type import DDPPlugin
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
Au... | 16,400 | 38.425481 | 124 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag-end2end-retriever/callbacks_rag.py | import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def count_trainable_parameters(model):
model_parame... | 4,463 | 36.2 | 126 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag-end2end-retriever/kb_encode_utils.py | import os
from functools import partial
from glob import glob
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk
import faiss
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast
def split_text(text, n=100, character=" "):
"""Split the text e... | 3,179 | 37.780488 | 112 | py |
robust-transformers | robust-transformers-main/examples/research_projects/rag-end2end-retriever/distributed_ray_retriever.py | import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
logger = logging.getLogger(__name__)
class RayRetriever:
def __init__(self):
self.initialized = False
def create_rag_retriever(self... | 8,211 | 43.150538 | 132 | py |
robust-transformers | robust-transformers-main/examples/research_projects/movement-pruning/counts_parameters.py | # Copyright 2020-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | 3,395 | 35.516129 | 124 | py |
robust-transformers | robust-transformers-main/examples/research_projects/movement-pruning/masked_run_glue.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | 40,528 | 41.662105 | 156 | py |
robust-transformers | robust-transformers-main/examples/research_projects/movement-pruning/bertarize.py | # Copyright 2020-present, the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law o... | 5,086 | 37.24812 | 155 | py |
robust-transformers | robust-transformers-main/examples/research_projects/movement-pruning/masked_run_squad.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | 47,575 | 41.214729 | 156 | py |
robust-transformers | robust-transformers-main/examples/research_projects/movement-pruning/emmental/modeling_bert_masked.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | 47,084 | 45.161765 | 152 | py |
robust-transformers | robust-transformers-main/examples/research_projects/movement-pruning/emmental/__init__.py | # flake8: noqa
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 301 | 26.454545 | 55 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.