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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Few-shot-WSI | Few-shot-WSI-master/openselfsup/datasets/byol.py | import torch
from torch.utils.data import Dataset
from openselfsup.utils import build_from_cfg
from torchvision.transforms import Compose
from .registry import DATASETS, PIPELINES
from .builder import build_datasource
from .utils import to_numpy
@DATASETS.register_module
class BYOLDataset(Dataset):
"""Dataset ... | 1,284 | 29.595238 | 74 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/datasets/contrastive.py | import torch
from PIL import Image
from .registry import DATASETS
from .base import BaseDataset
from .utils import to_numpy
@DATASETS.register_module
class ContrastiveDataset(BaseDataset):
"""Dataset for contrastive learning methods that forward
two views of the image at a time (MoCo, SimCLR).
"""
... | 1,210 | 34.617647 | 81 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/datasets/data_sources/cifar.py | from abc import ABCMeta, abstractmethod
from PIL import Image
from torchvision.datasets import CIFAR10, CIFAR100
from ..registry import DATASOURCES
class Cifar(metaclass=ABCMeta):
CLASSES = None
def __init__(self, root, split, return_label=True):
assert split in ['train', 'test']
self.root... | 1,990 | 26.273973 | 76 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/datasets/loader/sampler.py | from __future__ import division
import math
import numpy as np
import torch
from mmcv.runner import get_dist_info
from torch.utils.data import DistributedSampler as _DistributedSampler
from torch.utils.data import Sampler
class DistributedSampler(_DistributedSampler):
def __init__(self,
dataset... | 10,628 | 34.079208 | 92 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/datasets/loader/build_loader.py | import platform
import random
import torch
from functools import partial
import numpy as np
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from torch.utils.data import DataLoader
#from .sampler import DistributedGroupSampler, DistributedSampler, GroupSampler
from .sampler import DistributedSa... | 4,179 | 30.428571 | 92 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/datasets/pipelines/transforms.py | import cv2
import inspect
import numpy as np
from PIL import Image, ImageFilter
import torch
from torchvision import transforms as _transforms
from openselfsup.utils import build_from_cfg
from ..registry import PIPELINES
# register all existing transforms in torchvision
_EXCLUDED_TRANSFORMS = ['GaussianBlur']
for ... | 3,142 | 26.330435 | 80 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/hooks/extractor.py | import torch.nn as nn
from torch.utils.data import Dataset
from openselfsup.utils import nondist_forward_collect, dist_forward_collect
class Extractor(object):
"""Feature extractor.
Args:
dataset (Dataset | dict): A PyTorch dataset or dict that indicates
the dataset.
imgs_per_gpu... | 2,196 | 34.435484 | 77 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/hooks/validate_hook.py | from mmcv.runner import Hook
import torch
from torch.utils.data import Dataset
from openselfsup.utils import nondist_forward_collect, dist_forward_collect
from .registry import HOOKS
@HOOKS.register_module
class ValidateHook(Hook):
"""Validation hook.
Args:
dataset (Dataset | dict): A PyTorch datas... | 3,003 | 33.528736 | 77 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/hooks/deepcluster_hook.py | import numpy as np
from mmcv.runner import Hook
import torch
import torch.distributed as dist
from openselfsup.third_party import clustering as _clustering
from openselfsup.utils import print_log
from .registry import HOOKS
from .extractor import Extractor
@HOOKS.register_module
class DeepClusterHook(Hook):
""... | 4,637 | 36.104 | 79 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/utils/contextmanagers.py | # coding: utf-8
import asyncio
import contextlib
import logging
import os
import time
from typing import List
import torch
logger = logging.getLogger(__name__)
DEBUG_COMPLETED_TIME = bool(os.environ.get('DEBUG_COMPLETED_TIME', False))
@contextlib.asynccontextmanager
async def completed(trace_name='',
... | 4,103 | 32.365854 | 79 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/utils/optimizers.py | import torch
from torch.optim.optimizer import Optimizer, required
from torch.optim import *
class LARS(Optimizer):
r"""Implements layer-wise adaptive rate scaling for SGD.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float): b... | 4,327 | 35.991453 | 88 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/utils/profiling.py | import contextlib
import sys
import time
import torch
if sys.version_info >= (3, 7):
@contextlib.contextmanager
def profile_time(trace_name,
name,
enabled=True,
stream=None,
end_stream=None):
"""Print time spent by CP... | 1,363 | 32.268293 | 74 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/utils/collect.py | import numpy as np
import mmcv
import torch
from .gather import gather_tensors_batch
def nondist_forward_collect(func, data_loader, length):
"""Forward and collect network outputs.
This function performs forward propagation and collects outputs.
It can be used to collect results, features, losses, etc.... | 2,773 | 32.02381 | 78 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/utils/alias_multinomial.py | import torch
import numpy as np
class AliasMethod(object):
"""The alias method for sampling.
From: https://hips.seas.harvard.edu/blog/2013/03/03/the-alias-method-efficient-sampling-with-many-discrete-outcomes/
Args:
probs (Tensor): Sampling probabilities.
"""
def __init__(self, probs):
... | 2,132 | 27.065789 | 120 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/utils/gather.py | import numpy as np
import torch
import torch.distributed as dist
def gather_tensors(input_array):
world_size = dist.get_world_size()
## gather shapes first
myshape = input_array.shape
mycount = input_array.size
shape_tensor = torch.Tensor(np.array(myshape)).cuda()
all_shape = [
torch.... | 2,629 | 36.571429 | 100 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/utils/collect_env.py | import os.path as osp
import subprocess
import sys
from collections import defaultdict
import cv2
import mmcv
import torch
import torchvision
import openselfsup
def collect_env():
"""Collect the information of the running environments."""
env_info = {}
env_info['sys.platform'] = sys.platform
env_inf... | 2,055 | 30.630769 | 81 | py |
Few-shot-WSI | Few-shot-WSI-master/openselfsup/utils/flops_counter.py | # Modified from flops-counter.pytorch by Vladislav Sovrasov
# original repo: https://github.com/sovrasov/flops-counter.pytorch
# MIT License
# Copyright (c) 2018 Vladislav Sovrasov
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (th... | 14,304 | 31.146067 | 79 | py |
rivuletpy | rivuletpy-master/filtering/riveal.py | import numpy as np
import math
import skfmm
from tqdm import tqdm
from scipy.ndimage.morphology import binary_dilation
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.layers.noise import GaussianDropout, GaussianNois... | 8,673 | 32.233716 | 78 | py |
Mr.Right | Mr.Right-main/main.py | import yaml
import os
import utils
import warnings
from argparse import ArgumentParser
from torch import nn
from pytorch_lightning import Trainer,seed_everything
from pytorch_lightning import loggers as pl_loggers
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.callbacks import ModelCheckpoint,... | 5,871 | 42.496296 | 150 | py |
Mr.Right | Mr.Right-main/pltrainer.py | import pdb
import utils
import json
import pickle
import torch
import os
import torch.nn.functional as F
import torch.distributed as dist
import pytorch_lightning as pl
from metric import score
from scheduler import CosineLRScheduler
from tqdm import tqdm
class TextToMultiTrainer(pl.LightningModule):
def __init__(... | 43,280 | 57.095302 | 126 | py |
Mr.Right | Mr.Right-main/metric.py | import numpy as np
import torch
import pdb
from torchmetrics.functional import retrieval_recall,retrieval_reciprocal_rank
@torch.no_grad()
def score(scores_t2m, query_doc_id):
"""
scores_t2m: (q_size, d_size)
query_doc_id: (q_size)
"""
ids = query_doc_id.unsqueeze(1)
top1_i = torch.topk(scores... | 1,097 | 30.371429 | 103 | py |
Mr.Right | Mr.Right-main/scheduler/cosine_lr.py | """ Cosine Scheduler
Cosine LR schedule with warmup, cycle/restarts, noise.
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import math
import numpy as np
import torch
from .scheduler import Scheduler
from pdb import set_trace as breakpoint
_logger = logging.getLogger(__name__)
class CosineL... | 4,027 | 33.135593 | 121 | py |
Mr.Right | Mr.Right-main/scheduler/scheduler.py | from typing import Dict, Any
import torch
class Scheduler:
""" Parameter Scheduler Base Class
A scheduler base class that can be used to schedule any optimizer parameter groups.
Unlike the builtin PyTorch schedulers, this is intended to be consistently called
* At the END of each epoch, before incre... | 4,750 | 43.820755 | 112 | py |
Mr.Right | Mr.Right-main/models/matching.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class MatchingModel(nn.Module):
def __init__(self, args, config, text_width, n_layers):
super().__init__()
self.config = config
from models.ALBEF.models.xbert import BertModel
self.config.num_hidden_layers =... | 1,817 | 29.3 | 89 | py |
Mr.Right | Mr.Right-main/models/model.py | import pdb
import torch
import torch.nn.functional as F
from torch import nn
from models.ALBEF.models.model_retrieval import ALBEF
from models.ALBEF.models.vit import interpolate_pos_embed
from models.ALBEF.models.xbert import BertOnlyMLMHead,BertConfig
from models.ViLT.vilt.modules import ViLTransformerSS
from models.... | 21,799 | 50.294118 | 151 | py |
Mr.Right | Mr.Right-main/models/METER/azure_distributed_run.py | import os
import copy
import pytorch_lightning as pl
import os
os.environ["NCCL_DEBUG"] = "INFO"
from meter.config import ex
from meter.modules import METERTransformerSS
from meter.datamodules.multitask_datamodule import MTDataModule
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlim... | 4,388 | 31.272059 | 97 | py |
Mr.Right | Mr.Right-main/models/METER/setup.py | from setuptools import setup, find_packages
setup(
name="meter",
packages=find_packages(
exclude=[".dfc", ".vscode", "dataset", "notebooks", "result", "scripts"]
),
version="0.1.0",
license="MIT",
description="METER: Multimodal End-to-end TransformER",
author="Microsoft Corporation"... | 511 | 29.117647 | 80 | py |
Mr.Right | Mr.Right-main/models/METER/run.py | import os
import copy
import pytorch_lightning as pl
import os
os.environ["NCCL_DEBUG"] = "INFO"
from meter.config import ex
from meter.modules import METERTransformerSS
from meter.datamodules.multitask_datamodule import MTDataModule
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlim... | 2,373 | 29.050633 | 97 | py |
Mr.Right | Mr.Right-main/models/METER/meter/modules/clip_model.py | from collections import OrderedDict
from typing import Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: torch.Tensor):
orig_type = x.dtype
ret... | 11,209 | 39.179211 | 142 | py |
Mr.Right | Mr.Right-main/models/METER/meter/modules/meter_utils.py | import torch
import random
from transformers.optimization import AdamW
from transformers import (
get_polynomial_decay_schedule_with_warmup,
get_cosine_schedule_with_warmup,
)
from .dist_utils import all_gather
from .objectives import compute_irtr_recall
from ..gadgets.my_metrics import Accuracy, VQAScore, Sca... | 11,926 | 38.363036 | 100 | py |
Mr.Right | Mr.Right-main/models/METER/meter/modules/swin_transformer.py | """ Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer, original copyright/license info below
"""
# --------------------------------------------------------
... | 27,086 | 41.191589 | 125 | py |
Mr.Right | Mr.Right-main/models/METER/meter/modules/bert_model.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... | 76,774 | 41.915036 | 213 | py |
Mr.Right | Mr.Right-main/models/METER/meter/modules/meter_module.py | import torch
import torch.nn as nn
import pytorch_lightning as pl
import numpy as np
import pdb
from transformers.models.bert.modeling_bert import BertConfig, BertEmbeddings, BertModel, BertEncoder, BertLayer
from .bert_model import BertCrossLayer, BertAttention
from . import swin_transformer as swin
from . import head... | 15,962 | 40.141753 | 134 | py |
Mr.Right | Mr.Right-main/models/METER/meter/modules/dist_utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
"""
import functools
import logging
import numpy as np
import pickle
import torch
import torch.distributed as dist
import torch
_LOCAL_... | 7,814 | 27.837638 | 100 | py |
Mr.Right | Mr.Right-main/models/METER/meter/modules/objectives.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from .dist_utils import all_gather
def compute_mlm(pl_module, batch):
infer = pl_module.infer... | 17,360 | 33.514911 | 88 | py |
Mr.Right | Mr.Right-main/models/METER/meter/modules/swin_helpers.py | """ Model creation / weight loading / state_dict helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
import logging
import os
import math
from collections import OrderedDict
from copy import deepcopy
from typing import Any, Callable, Optional, Tuple
import torch
import torch.nn as nn
from timm.models.featu... | 23,550 | 43.519849 | 153 | py |
Mr.Right | Mr.Right-main/models/METER/meter/modules/heads.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.models.bert.modeling_bert import BertPredictionHeadTransform
class Pooler(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.activation =... | 1,257 | 27.590909 | 83 | py |
Mr.Right | Mr.Right-main/models/METER/meter/transforms/transform.py | from .utils import (
inception_normalize,
imagenet_normalize,
MinMaxResize,
)
from PIL import Image
from torchvision import transforms
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from .randaug import RandAugment
def pixelbert_transform(size=800):
longer = int((1... | 2,733 | 26.34 | 93 | py |
Mr.Right | Mr.Right-main/models/METER/meter/transforms/utils.py | from torchvision import transforms
from PIL import Image
class MinMaxResize:
def __init__(self, shorter=800, longer=1333):
self.min = shorter
self.max = longer
def __call__(self, x):
w, h = x.size
scale = self.min / min(w, h)
if h < w:
newh, neww = self.min... | 1,792 | 27.919355 | 98 | py |
Mr.Right | Mr.Right-main/models/METER/meter/transforms/randaug.py | # code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from PIL import Image
def ShearX(img, v): # [-0.3, 0.3]
assert -0.3 <= v <= 0.3
... | 6,990 | 24.892593 | 134 | py |
Mr.Right | Mr.Right-main/models/ALBEF/models/xbert.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... | 82,187 | 41.873239 | 213 | py |
Mr.Right | Mr.Right-main/models/ALBEF/models/vit.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from timm.models.vision_transformer import _cfg, PatchEmbed
from timm.models.registry import register_model
from timm.models.layers import trunc_normal_, DropPath
class Mlp(nn.Module):
""" MLP as used in Vision Trans... | 8,558 | 41.162562 | 118 | py |
Mr.Right | Mr.Right-main/models/ALBEF/models/model_retrieval.py | from functools import partial
from models.ALBEF.models.vit import VisionTransformer
from models.ALBEF.models.xbert import BertConfig, BertModel
import torch
from torch import nn
import torch.nn.functional as F
class ALBEF(nn.Module):
def __init__(self,
text_encoder = None,
... | 3,499 | 45.666667 | 129 | py |
Mr.Right | Mr.Right-main/models/ViLT/vilt/modules/vilt_utils.py | import torch
import random
from transformers.optimization import AdamW
from transformers import (
get_polynomial_decay_schedule_with_warmup,
get_cosine_schedule_with_warmup,
)
from models.ViLT.vilt.modules.dist_utils import all_gather
from models.ViLT.vilt.modules.objectives import compute_irtr_recall
from mod... | 10,650 | 37.451264 | 88 | py |
Mr.Right | Mr.Right-main/models/ViLT/vilt/modules/dist_utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
"""
import functools
import logging
import numpy as np
import pickle
import torch
import torch.distributed as dist
import torch
_LOCAL_... | 7,814 | 27.837638 | 100 | py |
Mr.Right | Mr.Right-main/models/ViLT/vilt/modules/objectives.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import glob
import json
import tqdm
import functools
from torch.utils.data.distributed import DistributedSampler
from einops import rearrange
from models.ViLT.vilt.modules.dist_utils import all_gather
def cost_matrix_cosine(x, y, eps=1e-5)... | 22,098 | 32.842266 | 88 | py |
Mr.Right | Mr.Right-main/models/ViLT/vilt/modules/vilt_module.py | import torch
torch.autograd.set_detect_anomaly(True)
import torch.nn as nn
import pytorch_lightning as pl
import models.ViLT.vilt.modules.vision_transformer as vit
import pdb
from transformers.models.bert.modeling_bert import BertConfig, BertEmbeddings
from models.ViLT.vilt.modules import heads, objectives
# from model... | 10,172 | 28.148997 | 119 | py |
Mr.Right | Mr.Right-main/models/ViLT/vilt/modules/vision_transformer.py | """ Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929
The official jax code is released and available at https://github.com/google-research/vision_transformer
... | 49,034 | 34.558376 | 155 | py |
Mr.Right | Mr.Right-main/models/ViLT/vilt/modules/heads.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.models.bert.modeling_bert import BertPredictionHeadTransform
class Pooler(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.activation =... | 1,569 | 27.035714 | 83 | py |
Mr.Right | Mr.Right-main/models/ViLT/vilt/transforms/utils.py | from torchvision import transforms
from PIL import Image
class MinMaxResize:
def __init__(self, shorter=800, longer=1333):
self.min = shorter
self.max = longer
def __call__(self, x):
w, h = x.size
scale = self.min / min(w, h)
if h < w:
newh, neww = self.min... | 1,645 | 27.877193 | 98 | py |
Mr.Right | Mr.Right-main/models/ViLT/vilt/transforms/randaug.py | # code in this file is adpated from rpmcruz/autoaugment
# https://github.com/rpmcruz/autoaugment/blob/master/transformations.py
import random
import PIL, PIL.ImageOps, PIL.ImageEnhance, PIL.ImageDraw
import numpy as np
import torch
from PIL import Image
def ShearX(img, v): # [-0.3, 0.3]
assert -0.3 <= v <= 0.3
... | 6,990 | 24.892593 | 134 | py |
Mr.Right | Mr.Right-main/models/ViLT/vilt/transforms/pixelbert.py | from .utils import (
inception_normalize,
MinMaxResize,
)
from torchvision import transforms
from .randaug import RandAugment
def pixelbert_transform(size=800):
longer = int((1333 / 800) * size)
return transforms.Compose(
[
MinMaxResize(shorter=size, longer=longer),
tra... | 714 | 22.064516 | 54 | py |
Mr.Right | Mr.Right-main/data/data_module.py | import random
import torch
import os
import json
import pickle
from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import Compose, ToTensor, Normalize, Resize, RandomResizedCrop, RandomHorizontalFlip
from pytorch_lightning import LightningDataModule
from data.utils import pre_caption, RandomAug... | 13,856 | 44.136808 | 176 | py |
NORPPA | NORPPA-main/config.py | import os
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_pre... | 4,039 | 41.526316 | 123 | py |
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/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/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/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 |
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/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/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/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/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/_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/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/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 |
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