python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
|---|---|---|
import torch
import meerkat as mk
from itertools import product
import numpy as np
from typing import List
import pandas as pd
PAD_TOKEN_ID = 103
def generate_candidate_descriptions(
templates: List[str],
device: int = 0,
k: int = 2,
bert_size: str = "base",
num_candidates: str = 30_000,
num... | domino-main | domino/_describe/generate.py |
from typing import Union
import meerkat as mk
import numpy as np
from scipy.stats import mode
from domino.utils import unpack_args
def describe(
data: mk.DataPanel = None,
embeddings: Union[str, np.ndarray] = "embedding",
targets: Union[str, np.ndarray] = "target",
slices: Union[str, np.ndarray] = "s... | domino-main | domino/_describe/__init__.py |
from typing import Union
import meerkat as mk
import numpy as np
from scipy.stats import mode, pearsonr
from .abstract import Describer
from ..utils import unpack_args
class MeanDescriber(Describer):
"""
Args:
text (str, optional): A `Meerkat DataPanel` with columns for text phrases and
... | domino-main | domino/_describe/mean.py |
from __future__ import annotations
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import Any, Dict, Union
import meerkat as mk
import numpy as np
import torch.nn as nn
from sklearn.base import BaseEstimator
@dataclass
class Config:
pass
class Slicer(ABC, BaseEstimator):
... | domino-main | domino/_slice/abstract.py |
from typing import Union
import meerkat as mk
import numpy as np
import torch
import torch.optim as optim
from torch.nn.functional import cross_entropy
from tqdm import tqdm
from domino.utils import unpack_args
from .abstract import Slicer
class SpotlightSlicer(Slicer):
r"""
Slice a dataset with The Spotl... | domino-main | domino/_slice/spotlight.py |
from __future__ import annotations
import warnings
from functools import wraps
from typing import Union
import meerkat as mk
import numpy as np
import sklearn.cluster as cluster
from scipy import linalg
from scipy.special import logsumexp
from sklearn.decomposition import PCA
from sklearn.exceptions import Convergenc... | domino-main | domino/_slice/mixture.py |
from __future__ import annotations
from typing import Union
from domino._slice.abstract import Slicer
from torch import nn
from torch.nn import functional as F
from torch.nn.functional import cross_entropy
import torch
from sklearn.linear_model import LinearRegression, LogisticRegression, Ridge, Lasso
from sklearn.prep... | domino-main | domino/_slice/fused.py |
domino-main | domino/_slice/__init__.py | |
from typing import Union
import meerkat as mk
import numpy as np
import torch
import torch.optim as optim
from torch.nn.functional import cross_entropy
from tqdm import tqdm
from domino.utils import unpack_args
from abstract import Slicer
## PlaneSpot imports
from sklearn import mixture
import glob
from collections... | domino-main | domino/_slice/planespot.py |
from __future__ import annotations
from typing import Union
from domino._slice.abstract import Slicer
from torch import nn
from torch.nn import functional as F
from torch.nn.functional import cross_entropy
import torch
from tqdm import tqdm
import numpy as np
import meerkat as mk
from ..utils import convert_to_torch, ... | domino-main | domino/_slice/mlp.py |
from __future__ import annotations
import datetime
from dataclasses import dataclass
from typing import Union
import meerkat as mk
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from meerkat.columns.tensor_column import TensorColumn
from sklearn.linear_model import Ridge
from sklea... | domino-main | domino/_slice/multiaccuracy.py |
from __future__ import annotations
from collections import defaultdict
from multiprocessing.sharedctypes import Value
from typing import Union
import meerkat as mk
import numpy as np
from sklearn.tree import DecisionTreeClassifier
from domino.utils import convert_to_numpy, unpack_args
from .abstract import Slicer
c... | domino-main | domino/_slice/barlow.py |
from sklearn.decomposition import FactorAnalysis
"""Factor Analysis.
A latent linear variable model.
FactorAnalysis is similar to probabilistic PCA implemented by PCA.score
While PCA assumes Gaussian noise with the same variance for each
feature, the FactorAnalysis model assumes different variances for
each of them.... | domino-main | domino/_slice/factor.py |
from dataclasses import dataclass
import meerkat as mk
import numpy as np
import torch
import torch.nn as nn
import umap
from sklearn.decomposition import PCA
#from stratification.cluster.models.cluster import AutoKMixtureModel
from torch.nn.functional import cross_entropy
from umap import UMAP
from domino.utils impo... | domino-main | domino/_slice/george.py |
from typing import Dict, Union
from .encoder import Encoder
def transformers(
variant: str = "bert-large-cased", device: Union[int, str] = "cpu"
) -> Dict[str, Encoder]:
"""Contrastive Language-Image Pre-training (CLIP) encoders [radford_2021]_. Includes
encoders for the following modalities:
- "tex... | domino-main | domino/_embed/gpt_j.py |
from typing import Union, List, Dict
import torch
from .encoder import Encoder
def transformers(
variant: str = "bert-large-cased", device: Union[int, str] = "cpu"
) -> Dict[str, Encoder]:
"""Transformer encoders
- "text"
Encoders will map these different modalities to the same embedding space.
... | domino-main | domino/_embed/transformers.py |
import os
from typing import Callable, Union
import meerkat as mk
import torch
from domino._embed.encoder import Encoder
from ..registry import Registry
from .bit import bit
from .clip import clip
from .robust import robust
from .transformers import transformers
__all__ = ["clip", "bit"]
encoders = Registry(name="... | domino-main | domino/_embed/__init__.py |
from dataclasses import dataclass
@dataclass
class Encoder:
encode: callable
preprocess: callable = None
collate: callable = None
| domino-main | domino/_embed/encoder.py |
from functools import partial
import torch
def _get_reduction_fn(reduction_name):
if reduction_name == "max":
reduction_fn = partial(torch.mean, dim=[-1, -2])
elif reduction_name == "mean":
reduction_fn = partial(torch.mean, dim=[-1, -2])
else:
raise ValueError(f"reduction_fn {red... | domino-main | domino/_embed/utils.py |
import io
from collections import OrderedDict
from typing import Dict, Union
import numpy as np
import PIL
import requests
import torch
import torch.nn as nn
import torch.nn.functional as F
from ..utils import nested_getattr
from .encoder import Encoder
from .utils import ActivationExtractor, _get_reduction_fn
# thi... | domino-main | domino/_embed/bit.py |
from ast import Import
import subprocess
from typing import Dict, Union
import os
from .encoder import Encoder
VARIANTS = {
"imagenet_l2_3_0": "https://www.dropbox.com/s/knf4uimlqsi1yz8/imagenet_l2_3_0.pt?dl=0",
"cifar_l2_1_0": "https://www.dropbox.com/s/s2x7thisiqxz095/cifar_l2_1_0.pt?dl=0",
"imagenet_li... | domino-main | domino/_embed/robust.py |
from typing import Dict, Union
from .encoder import Encoder
def clip(
variant: str = "ViT-B/32", device: Union[int, str] = "cpu"
) -> Dict[str, Encoder]:
"""Contrastive Language-Image Pre-training (CLIP) encoders [radford_2021]_. Includes
encoders for the following modalities:
- "text"
- "image"... | domino-main | domino/_embed/clip.py |
from typing import List, Tuple
from dcbench import SliceDiscoveryProblem, SliceDiscoverySolution
import meerkat as mk
import numpy as np
import sklearn.metrics as skmetrics
from domino.utils import unpack_args
from scipy.stats import rankdata
import pandas as pd
from tqdm import tqdm
def compute_metrics(
solution... | domino-main | domino/eval/metrics.py |
from __future__ import annotations
from contextlib import redirect_stdout
import dataclasses
from gettext import dpgettext
import io
import itertools
from random import choice, sample
from typing import Collection, Dict, Iterable, List, Tuple, Union
from dataclasses import dataclass
from sklearn.linear_model import Li... | domino-main | domino/eval/run.py |
domino-main | domino/eval/__init__.py | |
from typing import Union
import meerkat as mk
import numpy as np
import pandas as pd
class CorrelationImpossibleError(ValueError):
def __init__(
self,
corr: float,
n: int,
attr_a: str,
attr_b: str,
mu_a: float,
mu_b: float,
msg: str,
):
... | domino-main | domino/eval/utils.py |
from __future__ import annotations
import meerkat as mk
import torch
import PIL
from torchvision.models import ResNet as _ResNet
from torchvision.models.resnet import BasicBlock, Bottleneck
from torchvision.models.resnet import model_urls as resnet_model_urls
from torch.hub import load_state_dict_from_url
from torchvi... | domino-main | domino/eval/train.py |
domino-main | tests/__init__.py | |
import os
import meerkat as mk
import numpy as np
from PIL import Image
from sklearn.datasets import make_blobs
import torch
class ImageColumnTestBed:
def __init__(
self,
tmpdir: str,
length: int = 16,
):
self.image_paths = []
self.image_arrays = []
self.ims = ... | domino-main | tests/testbeds.py |
domino-main | tests/_describe/__init__.py | |
from sklearn import metrics
import pytest
from itertools import product
from domino import DominoSlicer
from ..testbeds import SliceTestBed
@pytest.mark.parametrize(
"init_params,type", product(["random", "confusion"], ["numpy", "torch"])
)
def test_domino_results(init_params: str, type: str):
testbed = Sl... | domino-main | tests/_slice/test_domino.py |
domino-main | tests/_slice/__init__.py | |
from sklearn import metrics
import pytest
import numpy as np
from domino import SpotlightSlicer
from ..testbeds import SliceTestBed
@pytest.mark.parametrize("pass_losses", [True, False])
def test_domino_results(pass_losses):
testbed = SliceTestBed(length=9)
method = SpotlightSlicer(n_slices=2, n_steps=3)... | domino-main | tests/_slice/test_spotlight.py |
import meerkat as mk
import PIL
import pytest
import torch
import hashlib
import numpy as np
import domino
from domino import embed, encoders
from domino._embed.encoder import Encoder
from domino.registry import Registry
from ..testbeds import ImageColumnTestBed, TextColumnTestBed
EMB_SIZE = 4
def simple_encode(ba... | domino-main | tests/_embed/test__init__.py |
domino-main | tests/_embed/__init__.py | |
domino-main | tests/_embed/test_clip.py | |
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | domino-main | docs/source/conf.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
import subprocess
from subprocess import check_output
import os
import embeddings
class VecMap:
"""
wrapper for vecmap https://github.com/artetxem/vecmap
assumes vecmap is in the directory ./vecmap
"""
def __init__(self, srcvec, tgtvec, dictpath,... | coocmap-main | baselines.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
from typing import Optional
import collections
import numpy as np
import pandas as pd
from tokenizers import Tokenizer
# faithfully recreate the protocol of vecmap with minimal code modifications
def vecmap_evaluate(sim: np.ndarray, tokenizer1: Tokenizer, tokeniz... | coocmap-main | evaluation.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# Copyright (C) 2016-2018 Mikel Artetxe <artetxem@gmail.com>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the Licen... | coocmap-main | embeddings.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
import os
from dataclasses import dataclass
import wandb
import shutil
import pandas as pd
import numpy as np
import data
import match
import evaluation
import embeddings
# experimental parameters
defaults = dict(
lan1='./europarl-v7.hu-en.en',
lan2='./eur... | coocmap-main | test_coocmap.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
import itertools
import os
import sys
import subprocess
import time
# import lzma # needed for BUCC20Corpus
import numpy as np
from tokenizers import Token, Tokenizer
from tokenizers.models import BPE, WordLevel
from tokenizers.trainers import BpeTrainer, WordLevel... | coocmap-main | data.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
from collections import Counter
import numpy as np
import embeddings
np.set_printoptions(formatter={'float': lambda x: "{0:0.3f}".format(x)})
MAX_SVD_DIM = 5000 # maximum SVD to avoid long compute time
### initialization methods ###
def vecmap_unsup(x, z, norm_proc... | coocmap-main | match.py |
import os
import subprocess
from dataclasses import dataclass
import lzma
import wandb
import argparse
import shutil
import pandas as pd
import numpy as np
import data
import match
import evaluation
import embeddings
# from baselines import VecMap
os.environ['WANDB_IGNORE_GLOBS'] = 'lan1/*,lan2/*'
os.environ["OMP_NU... | coocmap-main | experiments/test_accvsize_cooc.py |
import os
import subprocess
from dataclasses import dataclass
import lzma
import wandb
import argparse
import shutil
import pandas as pd
import numpy as np
import data
import match
import evaluation
import embeddings
# from baselines import VecMap
os.environ['WANDB_IGNORE_GLOBS'] = 'lan1/*,lan2/*'
os.environ["OMP_NU... | coocmap-main | experiments/test_accvsize.py |
import os
import subprocess
from dataclasses import dataclass
import lzma
import wandb
import argparse
import shutil
import pandas as pd
import numpy as np
import data
import match
import evaluation
import embeddings
# from baselines import VecMap
os.environ['WANDB_IGNORE_GLOBS'] = 'lan1/*,lan2/*'
os.environ["OMP_NU... | coocmap-main | experiments/test_dropclip.py |
import os
import subprocess
from dataclasses import dataclass
import lzma
import wandb
import argparse
import shutil
import pandas as pd
import numpy as np
import data
import match
import evaluation
import embeddings
# from baselines import VecMap
os.environ['WANDB_IGNORE_GLOBS'] = 'lan1/*,lan2/*'
os.environ["OMP_NU... | coocmap-main | experiments/test_accvdim.py |
import os
from dataclasses import dataclass
import wandb
import shutil
import pandas as pd
import numpy as np
import data
import match
import evaluation
import embeddings
# experimental parameters
defaults = dict(
lan1='./europarl-v7.hu-en.en',
lan2='./europarl-v7.hu-en.hu',
eval='en-hu',
size1=20,
... | coocmap-main | experiments/test_coocmap.py |
import os
import subprocess
from dataclasses import dataclass
import lzma
import wandb
import argparse
import shutil
import pandas as pd
import numpy as np
import data
import match
import evaluation
import embeddings
# from baselines import VecMap
os.environ['WANDB_IGNORE_GLOBS'] = 'lan1/*,lan2/*'
os.environ["OMP_NU... | coocmap-main | experiments/test_matching.py |
from setuptools import setup
from Cython.Build import cythonize
import numpy
# python setup.py build_ext --inplace
setup(
ext_modules=cythonize(
['cooc_count.pyx'],
annotate=True),
include_dirs=[numpy.get_include()]
) | coocmap-main | fast/setup.py |
#!/usr/bin/env python3
#
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import math
import torch
import random
import numpy as np
from tqdm import tqdm
... | bounding_data_reconstruction-main | mnist_logistic_reconstruction.py |
#!/usr/bin/env python3
#
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import array
import gzip
import logging
import os
from os import path
import struct
import math
... | bounding_data_reconstruction-main | datasets.py |
#!/usr/bin/env python3
#
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import math
import torch
import numpy as np
import os
import matplotlib.pyplot a... | bounding_data_reconstruction-main | mnist_logistic_regression.py |
#!/usr/bin/env python3
#
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import jax
import jax.numpy as jnp
from jax.experimental import stax
DTYPE_MAPPING = {
"flo... | bounding_data_reconstruction-main | utils.py |
#!/usr/bin/env python3
#
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import jax.numpy as jnp
import jax.random as jnr
from jax import jit, grad, vmap, nn
from jax.tr... | bounding_data_reconstruction-main | trainer.py |
#!/usr/bin/env python3
#
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import itertools
import logging
import jax
import jax.numpy as jnp
import jax.random as jnr
imp... | bounding_data_reconstruction-main | train_classifier.py |
#!/usr/bin/env python3
#
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import jax.numpy as jnp
import jax.random as jnr
from jax import jit, jvp, vjp, jacrev, vmap, n... | bounding_data_reconstruction-main | accountant.py |
import numpy as np
from utils.constant import NEGATIVE, POSITIVE
from utils.utils import mean_to_canonical
import networkx as nx
from collections import Counter
from sklearn.linear_model import LinearRegression, LogisticRegression
import random
from utils.utils import numberToBase
from utils.utils import multi_index_to... | ivy-tutorial-master | methods/ivy.py |
from __future__ import division, print_function
import numpy as np
try:
from pylab import plt
except ImportError:
print('Unable to import pylab. R_pca.plot_fit() will not work.')
try:
# Python 2: 'xrange' is the iterative version
range = xrange
except NameError:
# Python 3: 'range' is iterative -... | ivy-tutorial-master | utils/r_pca.py |
import numpy as np
from .constant import NEGATIVE, POSITIVE
import pandas as pd
from collections import Counter
from pgmpy.models import MarkovModel
from pgmpy.factors.discrete import DiscreteFactor
from pgmpy.inference import BeliefPropagation
import itertools as it
import random
from statsmodels import robust
class... | ivy-tutorial-master | utils/utils.py |
POSITIVE = 1
NEGATIVE = -1
| ivy-tutorial-master | utils/constant.py |
import numpy as np
import networkx as nx
import itertools as it
import pandas as pd
from pgmpy.models import MarkovModel
from pgmpy.factors.discrete import DiscreteFactor
import pylab as plt
from utils.utils import IsingModel, factor2Df, sampling
from collections import Counter
from pgmpy.models import BayesianModel
fr... | ivy-tutorial-master | utils/data_simulator.py |
import numpy as np
from sklearn.linear_model import LinearRegression, LogisticRegression
from utils.constant import NEGATIVE, POSITIVE
def ProbWaldEstimator(X, Y, Zprob, mode="bxby", **kwargs):
if set(Zprob)=={-1,1}:
Zprob = (Zprob+1)/2
sample_weight_ZX = np.array([[sum(Zprob[X==x]), sum(1-Zprob[... | ivy-tutorial-master | estimators/prob_wald_estimator.py |
from sklearn.linear_model import LinearRegression, LogisticRegression
import numpy as np
from sklearn.metrics import r2_score
def WaldEstimator(X, Y, Z, mode="bxby",return_predictive_score=False):
# # adjust the probability range of Z to real
# # if Z is a probability not binary label
# # this will avoid... | ivy-tutorial-master | estimators/wald_estimator.py |
import multiprocessing
import numpy as np
from joblib import Parallel, delayed
from sklearn import preprocessing
from tqdm import tqdm
from methods.ivy import Ivy
def ComputeCausalitySingle(
X,
Y,
IVs,
IV_Model_list,
estimator_list,
is_soft_label=True,
ablation_train=1,
ablation_t... | ivy-tutorial-master | estimators/compute_causality.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import argparse
import csv
import logging
import pickle
import numpy as np
import torch
import transformers
i... | contriever-main | generate_passage_embeddings.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import sys
import argparse
import torch
import logging
import json
import numpy as np
import os
import src.slurm
import sr... | contriever-main | eval_beir.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import argparse
import torch
import transformers
from src.normalize_text import normalize
def save(tensor, split_path):
if not os.path.exists(os.path.dirname(split_path)):
os.makedirs(os.path.dirname(split_path))
with o... | contriever-main | preprocess.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import json
import logging
import glob
import numpy as np
import torch
import src.utils
from src.evaluat... | contriever-main | evaluate_retrieved_passages.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import pdb
import os
import time
import sys
import torch
from torch.utils.tensorboard import SummaryWriter
import logging
import json
import numpy as np
import torch.distributed as dist
from torch.utils.data import DataLoader, RandomSampler, Sequen... | contriever-main | finetuning.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import time
import sys
import torch
import logging
import json
import numpy as np
import random
import pickle
import torch.distributed as dist
from torch.utils.data import DataLoader, RandomSampler
from src.options import Options
from s... | contriever-main | train.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import argparse
import csv
import json
import logging
import pickle
import time
import glob
from pathlib import P... | contriever-main | passage_retrieval.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import sys
import os
import csv
import json
def convert2beir(data_path, output_path):
splits = ['test', 'dev', 'train']
queries_path = os.path.join(output_path, "queries.jsonl")
corpus_path = os.path.join(output_path, "corpus.jsonl")
... | contriever-main | data_scripts/convertmrtydi2beir.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import sys
import os
import json
from collections import defaultdict
def preprocess_xmkqa(input_path, output_dir):
os.makedirs(output_dir, exist_ok=True)
mkqa = []
with open(input_path, 'r') as fin:
for line in fin:
... | contriever-main | data_scripts/preprocess_xmkqa.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import torch
import transformers
from transformers import BertModel, XLMRobertaModel
from src import utils
class Contriever(BertModel):
def __init__(self, config, pooling="average", **kwargs):
super().__init__(config, add_p... | contriever-main | src/contriever.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import os
class Options:
def __init__(self):
self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
self.initialize()
def initialize(self):
# basic parameters... | contriever-main | src/options.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.nn as nn
import numpy as np
import math
import random
import transformers
import logging
import torch.distributed as dist
from src import contriever, dist_utils, utils
logger = logging.getLogger(__name__)
class InBatch... | contriever-main | src/inbatch.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import pickle
from typing import List, Tuple
import faiss
import numpy as np
from tqdm import tqdm
class Index... | contriever-main | src/index.py |
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from logging import getLogger
import os
import sys
import torch
import socket
import signal
import subprocess
logger = ge... | contriever-main | src/slurm.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import collections
import logging
import regex
import string
import unicodedata
from functools impor... | contriever-main | src/evaluation.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import random
import json
import sys
import numpy as np
from src import normalize_text
class Dataset(torch.utils.data.Dataset):
def __init__(
self,
datapaths,
negative_ctxs=1,
negative_hard_ratio=0... | contriever-main | src/finetuning_data.py |
contriever-main | src/__init__.py | |
"""
adapted from chemdataextractor.text.normalize
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Tools for normalizing text.
https://github.com/mcs07/ChemDataExtractor
:copyright: Copyright 2016 by Matt Swain.
:license: MIT
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated ... | contriever-main | src/normalize_text.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import logging
import torch
import errno
from typing import Union, Tuple, List, Dict
from collections import defaultdict
from src import dist_utils
Number = Union[float, int]
logger = logging.getLogger(__name__)
def init_l... | contriever-main | src/utils.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.nn as nn
import logging
import copy
import transformers
from src import contriever, dist_utils, utils
logger = logging.getLogger(__name__)
class MoCo(nn.Module):
def __init__(self, opt):
super(MoCo, self)._... | contriever-main | src/moco.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import glob
import torch
import random
import json
import csv
import numpy as np
import numpy.random
import logging
from collections import defaultdict
import torch.distributed as dist
from src import dist_utils
logger = logging.getLogg... | contriever-main | src/data.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.distributed as dist
class Gather(torch.autograd.Function):
@staticmethod
def forward(ctx, x: torch.tensor):
output = [torch.zeros_like(x) for _ in range(dist.get_world_size())]
dist.all_gather(out... | contriever-main | src/dist_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
from collections import defaultdict
from typing import List, Dict
import numpy as np
import torch
import torch.distributed as dist
import beir.util
from beir.datasets.data_loader import GenericDataLoader
from beir.retrieval.evaluation im... | contriever-main | src/beir_utils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
import cv2
import numpy as np
from utils.meshutils import read_mesh, process_head_model
from utils.strandsuti... | CT2Hair-main | CT2Hair/interp.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import os
from utils.pcutils import load_pc
from utils.strandsutils import strandspc2strands, smooth_strands
from datau... | CT2Hair-main | CT2Hair/optim.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import torch
import numpy as np
import torch.utils.data as th_data
from utils.strandsutils import spline_strand, pad_st... | CT2Hair-main | CT2Hair/datautils/dataloaders.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import time
import pathlib
import struct
import numpy as np
from utils.pcutils import pc_voxelization, save_pc
def loa... | CT2Hair-main | CT2Hair/datautils/datautils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import splines
import torch
import numpy as np
from tqdm import tqdm
from scipy.sparse import coo_matrix
fr... | CT2Hair-main | CT2Hair/utils/strandsutils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import copy
import igl
import trimesh
import numpy as np
from scipy.spatial.transform import Rotation as R
... | CT2Hair-main | CT2Hair/utils/meshutils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import time
import numpy as np
import open3d as o3d
from copy import deepcopy
from matplotlib import cm
def volume2pc(v... | CT2Hair-main | CT2Hair/utils/pcutils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import cv2
import math
import torch
import torch.nn as nn
import numpy as np
from matplotlib import cm
def polar2vector... | CT2Hair-main | CT2Hair/utils/utils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv3dGaussian(nn.Module):
'''... | CT2Hair-main | CT2Hair/utils/kernels.py |
from .chamfer_distance import ChamferDistance
| CT2Hair-main | CT2Hair/libs/chamfer_distance/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# https://github.com/chrdiller/pyTorchChamferDistance/tree/master
import torch
from torch.utils.cpp_extension import l... | CT2Hair-main | CT2Hair/libs/chamfer_distance/chamfer_distance.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import copy
import math
import torch
import numpy as np
from tqdm import tqdm
from sklearn.cluster import MeanShift
fro... | CT2Hair-main | CT2Hair/modules/neural_strands.py |
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