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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ELLE | ELLE-main/fairseq_ELLE/fairseq/optim/lr_scheduler/tri_stage_lr_scheduler.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import FairseqLRScheduler, register_lr_scheduler
import math
@register_lr_scheduler('tri_stage')
class TriStageLRSchedule(FairseqLRSc... | 4,993 | 30.018634 | 84 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/optim/lr_scheduler/reduce_lr_on_plateau.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.optim.lr_scheduler
from . import FairseqLRScheduler, register_lr_scheduler
@register_lr_scheduler('reduce_lr_on_plateau')
clas... | 4,411 | 43.565657 | 109 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/optim/lr_scheduler/cosine_lr_scheduler.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
from . import FairseqLRScheduler, register_lr_scheduler
@register_lr_scheduler('cosine')
class CosineSchedule(FairseqLRSchedule... | 4,752 | 38.941176 | 105 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/language_pair_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from . import data_utils, FairseqDataset
def collate(
samples, pad_idx, eos_idx, left_pad_source=True, ... | 12,245 | 41.373702 | 107 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/token_block_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from fairseq.data import FairseqDataset, plasma_utils
class TokenBlockDataset(FairseqDataset):
"""Break... | 6,139 | 34.906433 | 96 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/subsample_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from . import BaseWrapperDataset
class SubsampleDataset(BaseWrapperDataset):
"""Subsamples a given dataset by a spec... | 2,042 | 29.492537 | 101 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/prepend_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from . import BaseWrapperDataset
class PrependDataset(BaseWrapperDataset):
def __init__(self, dataset, ... | 953 | 31.896552 | 83 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/base_wrapper_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from torch.utils.data.dataloader import default_collate
from . import FairseqDataset
class BaseWrapperDataset(FairseqDataset):
def __i... | 1,352 | 24.528302 | 65 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/raw_label_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import FairseqDataset
class RawLabelDataset(FairseqDataset):
def __init__(self, labels):
super().__init__(... | 547 | 20.92 | 65 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/resampling_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
from . import BaseWrapperDataset, plasma_utils
class ResamplingDataset(BaseWrapperDataset):
"""Randomly samples from... | 4,096 | 30.274809 | 78 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/dictionary.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import Counter
from multiprocessing import Pool
import os
import torch
from fairseq.tokenizer import tokenize_line
from fai... | 10,926 | 32.621538 | 109 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/append_token_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from . import BaseWrapperDataset
class AppendTokenDataset(BaseWrapperDataset):
def __init__(self, data... | 1,066 | 23.813953 | 65 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/mask_tokens_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from functools import lru_cache
import numpy as np
import torch
from fairseq.data import data_utils, Dictionary
from . import BaseWrapperDa... | 6,814 | 38.393064 | 87 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/concat_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import bisect
import numpy as np
from torch.utils.data.dataloader import default_collate
from . import FairseqDataset
class ConcatDataset(... | 3,717 | 34.075472 | 86 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/nested_dictionary_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import OrderedDict
import torch
from torch.utils.data.dataloader import default_collate
from . import FairseqDataset
def ... | 3,776 | 31.282051 | 86 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/lm_context_window_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from fairseq.data.monolingual_dataset import MonolingualDataset
from . import FairseqDataset
class LMConte... | 2,910 | 35.848101 | 90 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/colorize_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import BaseWrapperDataset
class ColorizeDataset(BaseWrapperDataset):
""" Adds 'colors' property to net input that i... | 844 | 32.8 | 113 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/iterators.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import itertools
import math
import os
import numpy as np
import torch
from . import data_utils
class CountingIterator(object):
"""Wra... | 11,820 | 32.392655 | 91 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/backtranslation_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from fairseq import utils
from . import FairseqDataset
def backtranslate_samples(samples, collate_fn, generate_fn, cuda=True)... | 6,235 | 36.566265 | 93 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/monolingual_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from . import data_utils, FairseqDataset
def collate(samples, pad_idx, eos_idx):
if len(samples) == 0:
... | 7,859 | 36.251185 | 117 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/roll_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import BaseWrapperDataset
class RollDataset(BaseWrapperDataset):
def __init__(self, dataset, shifts):
supe... | 486 | 23.35 | 65 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/replace_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import BaseWrapperDataset
class ReplaceDataset(BaseWrapperDataset):
"""Replaces tokens found in the dataset by a specified replac... | 1,394 | 36.702703 | 117 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/id_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import FairseqDataset
class IdDataset(FairseqDataset):
def __getitem__(self, index):
return index
def... | 424 | 19.238095 | 65 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/indexed_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from functools import lru_cache
import os
import shutil
import struct
import numpy as np
import torch
from . import FairseqDataset
def __b... | 16,026 | 29.585878 | 105 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/denoising_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import math
from . import data_utils, FairseqDataset
def collate(
samples,
pad_idx,
eos_idx,
... | 14,119 | 35.580311 | 118 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/prepend_token_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from . import BaseWrapperDataset
class PrependTokenDataset(BaseWrapperDataset):
def __init__(self, dat... | 1,431 | 26.018868 | 66 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/numel_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from . import BaseWrapperDataset
class NumelDataset(BaseWrapperDataset):
def __init__(self, dataset, r... | 787 | 22.878788 | 65 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/noising.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import numpy as np
from fairseq.data import data_utils
class WordNoising(object):
"""Generate a noisy version of a sentenc... | 12,177 | 37.537975 | 110 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/concat_sentences_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import FairseqDataset
class ConcatSentencesDataset(FairseqDataset):
def __init__(self, *datasets):
super()... | 1,573 | 26.614035 | 75 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/fairseq_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch.utils.data
class EpochListening:
"""Mixin for receiving updates whenever the epoch increments."""
de... | 2,202 | 29.178082 | 80 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/transform_eos_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from . import FairseqDataset
class TransformEosDataset(FairseqDataset):
"""A :class:`~fairseq.data.FairseqDataset` wrapper... | 4,300 | 35.449153 | 88 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/audio/raw_audio_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
import sys
import torch
import torch.nn.functional as F
from .. import FairseqDataset
class RawAudioDataset(... | 4,571 | 28.121019 | 84 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/encoders/utils.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from fairseq.data import encoders
def get_whole_word_mask(args, dictionary):
bpe = encoders.build_bpe(args)
if bpe is n... | 907 | 30.310345 | 67 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/encoders/hf_bert_bpe.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from fairseq.data.encoders import register_bpe
@register_bpe('bert')
class BertBPE(object):
@staticmethod
def add_args(parser):
... | 1,799 | 33.615385 | 90 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/legacy/block_pair_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import numpy as np
import torch
from fairseq.data import FairseqDataset
class BlockPairDataset(FairseqDataset):
"""Break a... | 12,878 | 40.146965 | 99 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/data/legacy/masked_lm_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import numpy as np
import torch
from typing import Dict, List, Tuple
from fairseq.data import FairseqDataset, data_utils
from ... | 12,468 | 37.603715 | 83 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/tasks/continual_lm.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import torch
from fairseq import utils
from fairseq.data import (
data_utils,
Dictionary,
MonolingualDataset,
Toke... | 14,067 | 37.437158 | 145 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/tasks/language_modeling.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import torch
from fairseq import utils
from fairseq.data import (
data_utils,
Dictionary,
MonolingualDataset,
Toke... | 9,239 | 37.181818 | 98 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/tasks/continual_KI.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
import torch
import fairseq.utils
from fairseq.data import (
data_utils,
Dictionary,
IdDataset,
... | 15,616 | 39.25 | 145 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/tasks/clmb_task.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
from fairseq.data import (
data_utils,
Dictionary,
IdDataset,
MaskTokensDataset,
NestedDict... | 8,792 | 38.78733 | 98 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/tasks/multilingual_masked_lm.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
import torch
from fairseq.data import (
data_utils,
Dictionary,
encoders,
ConcatDataset,
Id... | 12,280 | 39.531353 | 98 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/tasks/continual_KI_no_replay.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
import torch
from fairseq.data import (
data_utils,
Dictionary,
IdDataset,
MaskTokensDataset,
... | 13,724 | 39.014577 | 136 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/tasks/multilingual_translation.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import OrderedDict
import os
import torch
from fairseq import options, utils
from fairseq.data import (
Dictionary,
... | 16,648 | 43.755376 | 116 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/tasks/translation_lev.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from fairseq.utils import new_arange
from fairseq.tasks import register_task
from fairseq.tasks.translation import TranslationTa... | 6,412 | 39.847134 | 87 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/tasks/translation_moe.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from fairseq import modules, utils
from fairseq.tasks import register_task
from fairseq.tasks.translation import TranslationTask... | 8,872 | 41.252381 | 110 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq/tasks/fairseq_task.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
from fairseq import tokenizer
from fairseq.data import (
data_utils,
FairseqDataset,
iterators,
... | 11,175 | 37.143345 | 104 | py |
ELLE | ELLE-main/fairseq_ELLE/docs/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# fairseq documentation build configuration file, created by
# sphinx-quickstart on Fri Aug 17 21:45:30 2018.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# au... | 4,235 | 30.849624 | 80 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq_cli/setup.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
from setuptools import setup, find_packages, Extension
import sys
if sys.version_info < (3, 5):
sys.exi... | 4,357 | 25.736196 | 92 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq_cli/generate.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Translate pre-processed data with a trained model.
"""
import torch
from fairseq import bleu, checkpoint_utils,... | 8,179 | 39.098039 | 110 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq_cli/train_copy2.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Train a new model on one or across multiple GPUs.
"""
import collections
import math
import random
import os
imp... | 31,610 | 41.260695 | 216 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq_cli/eval_lm.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Evaluate the perplexity of a trained language model.
"""
import numpy as np
import torch
from fairseq import c... | 8,132 | 34.671053 | 118 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq_cli/interactive.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Translate raw text with a trained model. Batches data on-the-fly.
"""
from collections import namedtuple
import ... | 6,445 | 32.05641 | 103 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq_cli/eval.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Train a new model on one or across multiple GPUs.
"""
import collections
import math
import random
import numpy... | 14,048 | 37.280654 | 104 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq_cli/train_copy.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Train a new model on one or across multiple GPUs.
"""
import collections
import math
import random
import os
imp... | 33,886 | 41.14801 | 179 | py |
ELLE | ELLE-main/fairseq_ELLE/fairseq_cli/train.py | #!/usr/bin/env python3 -u
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Train a new model on one or across multiple GPUs.
"""
import collections
import math
import random
import os
imp... | 33,608 | 41.06383 | 179 | py |
islplot | islplot-master/islplot/plotter3d.py | from islplot.support import *
# color scheme
# http://colorschemedesigner.com/#01400w0w0w0w0
colors = []
# red
colors.append({'base': '0xff0700', 'light': '0xff7673', 'dark': '0xA60400'})
# blue
colors.append({'base': '0x3B14Af', 'light': '0x886E7D'})
# yellow
colors.append({'base': '0xFFD500', 'light': '0xFFE873'... | 839,440 | 21.081836 | 237 | py |
islplot | islplot-master/doc/conf.py | # -*- coding: utf-8 -*-
#
# islplot documentation build configuration file, created by
# sphinx-quickstart on Sat Jan 4 18:35:53 2014.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All... | 8,022 | 31.481781 | 169 | py |
timematch | timematch-main/dataset.py | from collections import defaultdict
import datetime as dt
import os
import pickle as pkl
from typing import List
import numpy as np
import torch
from torch.utils import data
from torch.utils.data import ConcatDataset, DataLoader, Subset
from torchvision.transforms import transforms
import zarr
from transforms import ... | 14,500 | 32.645012 | 118 | py |
timematch | timematch-main/timematch.py | from torch.utils.data.sampler import WeightedRandomSampler
import sklearn.metrics
from collections import Counter
from copy import deepcopy
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils import data
from torchvision import transforms
from tqdm import tqdm
from dataset import PixelSet... | 16,785 | 42.942408 | 202 | py |
timematch | timematch-main/evaluation.py | import os
import numpy as np
import torch
import torch.backends.cudnn
import torch.nn.functional as F
from tqdm import tqdm
import sklearn.metrics
from utils.train_utils import AverageMeter, to_cuda
def validation(best_f1, best_model_path, config, criterion, device, epoch, model, val_loader, writer, temporal_shift=No... | 3,103 | 44.647059 | 168 | py |
timematch | timematch-main/train.py | import argparse
from collections import defaultdict
from copy import deepcopy
from distutils.util import strtobool
import json
import os
import pickle as pkl
import random
import numpy as np
import torch
import torch.backends.cudnn
from torch.utils.tensorboard import SummaryWriter
from torchvision.transforms import tr... | 20,486 | 52.07513 | 145 | py |
timematch | timematch-main/transforms.py | import random
import numpy as np
import torch
class Identity(object):
def __call__(self, sample):
return sample
class RandomSamplePixels(object):
"""Randomly draw num_pixels from the available pixels in sample.
If the total number of pixels is less than num_pixels, one arbitrary pixel is repeat... | 4,239 | 32.385827 | 119 | py |
timematch | timematch-main/models/stclassifier.py | from copy import deepcopy
import torch.nn as nn
from models.competings import GRU, TempConv
from models.decoder import get_decoder
from models.ltae import LTAE
from models.pse import PixelSetEncoder
from models.tae import TemporalAttentionEncoder
class PseLTae(nn.Module):
"""
Pixel-Set encoder + Lightweight... | 10,050 | 30.117647 | 93 | py |
timematch | timematch-main/models/ltae.py | """
Lightweight Temporal Attention Encoder module
We modify the original LTAE to support variable time series lengths and domain-specific batch normalization
Credits:
The module is heavily inspired by the works of Vaswani et al. on self-attention and their pytorch implementation of
the Transformer served as code base ... | 5,128 | 38.75969 | 152 | py |
timematch | timematch-main/models/layers.py | import math
import torch
import torch.nn as nn
class LinearLayer(nn.Module):
def __init__(self, in_dim, out_dim):
super().__init__()
self.linear = nn.Linear(in_dim, out_dim, bias=False)
self.norm = nn.BatchNorm1d(out_dim)
self.activation = nn.ReLU()
def forward(self, x):
... | 1,112 | 33.78125 | 90 | py |
timematch | timematch-main/models/pse.py | """
Pixel-Set encoder module
author: Vivien Sainte Fare Garnot
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import copy
from models.layers import LinearLayer
class PixelSetEncoder(nn.Module):
def __init__(
self,
input_dim,
mlp1=[10, 32, 64],
pooling="mea... | 4,294 | 29.899281 | 118 | py |
timematch | timematch-main/models/decoder.py | import torch.nn as nn
from models.layers import LinearLayer
def get_decoder(n_neurons, n_classes):
"""Returns an MLP with the layer widths specified in n_neurons.
Every linear layer but the last one is followed by BatchNorm + ReLu
args:
n_neurons (list): List of int that specifies the width and l... | 605 | 30.894737 | 85 | py |
timematch | timematch-main/models/competings.py | import torch.nn as nn
from models.tae import get_positional_encoding
import copy
class GRU(nn.Module):
"""
Gated Recurrent Unit
"""
def __init__(self, in_channels=128, hidden_dim=128, max_position=365, max_temporal_shift=100):
super(GRU, self).__init__()
self.name = 'GRU_h{}'.format(... | 2,650 | 32.556962 | 99 | py |
timematch | timematch-main/models/tae.py | """
Temporal Attention Encoder module
Credits:
The module is heavily inspired by the works of Vaswani et al. on self-attention and their pytorch implementation of
the Transformer served as code base for the present script.
paper: https://arxiv.org/abs/1706.03762
code: github.com/jadore801120/attention-is-all-you-need... | 6,252 | 35.354651 | 116 | py |
timematch | timematch-main/competitors/mmd/kernels.py | from typing import Optional
import torch
import torch.nn as nn
__all__ = ['GaussianKernel']
class GaussianKernel(nn.Module):
r"""Gaussian Kernel Matrix
Gaussian Kernel k is defined by
.. math::
k(x_1, x_2) = \exp \left( - \dfrac{\| x_1 - x_2 \|^2}{2\sigma^2} \right)
where :math:`x_1, x_2 ... | 2,312 | 38.87931 | 139 | py |
timematch | timematch-main/competitors/mmd/dan.py | from typing import Optional, Sequence
import torch
import torch.nn as nn
__all__ = ['MultipleKernelMaximumMeanDiscrepancy']
class MultipleKernelMaximumMeanDiscrepancy(nn.Module):
r"""The Multiple Kernel Maximum Mean Discrepancy (MK-MMD) used in
`Learning Transferable Features with Deep Adaptation Networks (... | 4,968 | 42.587719 | 115 | py |
timematch | timematch-main/competitors/mmd/train_mmd.py | '''
Re-implementation of MMD loss (DAN) from https://github.com/thuml/Transfer-Learning-Library/blob/master/examples/domain_adaptation/classification/dan.py
'''
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import LambdaLR
from torch.optim.sgd import SGD
from torchvision import transforms
from tqdm i... | 5,631 | 38.384615 | 152 | py |
timematch | timematch-main/competitors/jumbot/jumbot.py | """
Dependances :
- python (3.8.0)
- numpy (1.19.2)
- torch (1.7.1)
- POT (0.7.0)
- Cuda
command:
python3 train.py
"""
import numpy as np
import ot
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim.lr_scheduler import LambdaLR
from torch.optim.sgd import SGD
from torch.utils import... | 9,207 | 34.145038 | 139 | py |
timematch | timematch-main/competitors/dann/dann.py | """
Re-implementation of DANN from https://github.com/thuml/Transfer-Learning-Library
"""
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import LambdaLR
from torch.optim.sgd import SGD
from torchvision import transforms
from tqdm import tqdm
from dataset import PixelSetData
from evaluation import val... | 21,427 | 39.737643 | 171 | py |
timematch | timematch-main/competitors/dann/grl.py | from typing import Optional, Any, Tuple
import numpy as np
import torch.nn as nn
from torch.autograd import Function
import torch
class GradientReverseFunction(Function):
@staticmethod
def forward(ctx: Any, input: torch.Tensor, coeff: Optional[float] = 1.) -> torch.Tensor:
ctx.coeff = coeff
o... | 2,645 | 32.923077 | 119 | py |
timematch | timematch-main/competitors/alda/train_alda.py | from tqdm import tqdm
import torch
import torch.nn as nn
import competitors.alda.loss as loss
from dataset import PixelSetData
from evaluation import validation
from transforms import Normalize, RandomSamplePixels, RandomSampleTimeSteps, ToTensor, RandomTemporalShift, Identity
from utils.metrics import accuracy
from ut... | 8,566 | 32.728346 | 127 | py |
timematch | timematch-main/competitors/alda/loss.py | import numpy as np
import torch
import torch.nn as nn
def create_matrix(n):
"""
:param n: matrix size (class num)
:return a matrix with torch.tensor type:
for example n=3:
1 -1/2 -1/2
-1/2 1 -1/2
-1/2 -1/2 1
"""
a = np.zeros((n,n), dtype=np.float32)
for i in range... | 3,238 | 42.77027 | 146 | py |
timematch | timematch-main/utils/train_utils.py | import torch
import argparse
FALSY_STRINGS = {"off", "false", "0"}
TRUTHY_STRINGS = {"on", "true", "1"}
def to_cuda(sample, device, non_blocking=True):
pixels = sample['pixels'].cuda(device=device, non_blocking=non_blocking)
valid_pixels = sample['valid_pixels'].cuda(device=device, non_blocking=non_blocking)... | 1,979 | 28.117647 | 99 | py |
timematch | timematch-main/utils/focal_loss.py | '''
Implementation of Focal Loss.
Reference:
[1] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, Focal loss for dense object detection.
arXiv preprint arXiv:1708.02002, 2017.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, gamma=0,... | 843 | 28.103448 | 99 | py |
timematch | timematch-main/utils/metrics.py | import torch
import numpy as np
from tabulate import tabulate
@torch.no_grad()
def accuracy(outputs, targets):
preds = outputs.argmax(dim=1)
return preds.eq(targets).float().mean().item()
def f1_score(confusion_matrix, reduce_mean=True):
f1_scores = []
for index in range(confusion_matrix.shape[0]):
... | 5,384 | 37.464286 | 169 | py |
timematch | timematch-main/utils/samplers.py | import torch
class VariableSequenceLengthBatchSampler(torch.utils.data.Sampler):
"""
Outputs batches of patch indices where all patches have the same sequence length, so that
they can be torch.stack'ed.
"""
def __init__(self, data_source, batch_size):
self.data_source = data_source
... | 1,365 | 39.176471 | 117 | py |
BPC-Divergences | BPC-Divergences-main/reparam_module.py | import torch
import torch.nn as nn
import warnings
import types
from collections import namedtuple
from contextlib import contextmanager
class ReparamModule(nn.Module):
def _get_module_from_name(self, mn):
if mn == '':
return self
m = self
for p in mn.split('.'):
m ... | 6,718 | 41.257862 | 112 | py |
BPC-Divergences | BPC-Divergences-main/utils.py | # adapted from
# https://github.com/VICO-UoE/DatasetCondensation
import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import tqdm
from torch.utils.data import Dataset
from torchvision import datasets, transforms
from scipy.ndimage.interpolation import rotate as sc... | 27,247 | 42.666667 | 194 | py |
BPC-Divergences | BPC-Divergences-main/networks.py | import torch.nn as nn
import torch.nn.functional as F
import torch
# Acknowledgement to
# https://github.com/kuangliu/pytorch-cifar,
# https://github.com/BIGBALLON/CIFAR-ZOO,
# adapted from
# https://github.com/VICO-UoE/DatasetCondensation
''' MLP '''
class MLP(nn.Module):
def __init__(self, channel, num_classes)... | 22,466 | 40.837989 | 170 | py |
BPC-Divergences | BPC-Divergences-main/buffer.py | import os
import argparse
import torch
import torch.nn as nn
from tqdm import tqdm
from utils import get_dataset, get_network, get_daparam,\
TensorDataset, epoch, ParamDiffAug
import copy
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
def main(args):
args.dsa = True if... | 6,272 | 43.176056 | 130 | py |
BPC-Divergences | BPC-Divergences-main/evaluation.py | import torch
import torch.nn as nn
from reparam_module import ReparamModule
import numpy as np
from utils import DiffAugment
import torch.nn.functional as F
def evaluate_bpc(method, net, images_train, labels_train, test_loader, args, logger, testaug=False):
net = net.to(args.device)
images_train = images... | 6,685 | 39.035928 | 142 | py |
BPC-Divergences | BPC-Divergences-main/train.py | import os
from pickletools import optimize
import time
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.utils import save_image
from tqdm import tqdm
import copy
import random
from utils import get_dataset, get_network, get_eval_pool, get_time, DiffA... | 20,750 | 46.376712 | 154 | py |
DiffDVR | DiffDVR-master/pytests/tests/vis_gui.py | import os
import sys
sys.path.append(os.getcwd())
import numpy as np
import torch
import os
import time
import h5py
import collections
import matplotlib
import matplotlib.pyplot as plt
from abc import ABC, abstractmethod
# import PyQtChart
from PyQt5.QtChart import *
from PyQt5.QtGui import *
from PyQt5.QtCore impor... | 32,594 | 40.949807 | 138 | py |
DiffDVR | DiffDVR-master/pytests/tests/volume/train_volume.py | """
Large hyperparameter training session
"""
import sys
import os
sys.path.insert(0, os.getcwd())
import numpy as np
import torch
import torch.nn.functional as F
import os
import tqdm
import time
import h5py
import argparse
import json
from collections import defaultdict
import subprocess
import contextlib
@context... | 32,479 | 49.356589 | 139 | py |
DiffDVR | DiffDVR-master/pytests/tests/volume/test_volume_optimization2.py | import numpy as np
import torch
import torchvision
import matplotlib.pyplot as plt
from diffdvr import Renderer, VolumeDensities, TfPiecewiseLinear, CameraOnASphere, \
SmoothnessPrior, toCHW
import pyrenderer
def optimize():
np.random.seed(42)
torch.random.manual_seed(42)
pyrenderer.set_cuda_sync_mode(False)... | 7,526 | 35.897059 | 142 | py |
DiffDVR | DiffDVR-master/pytests/tests/volume/test_volume_optimization.py | import numpy as np
import torch
import sys
import os
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
import matplotlib.animation
import tqdm
# load pyrenderer
import diffdvr
import pyrenderer
if __name__=='__main__':
print(pyrenderer.__doc__)
print("Create Marschner Lobb")
volume = pyr... | 7,930 | 33.633188 | 112 | py |
DiffDVR | DiffDVR-master/pytests/tests/volume/compare_reconstruction.py | """
Absorption-only Reconstruction,
comparison with other methods (ASTRA + Mitsuba)
Command lines:
Skull: python3 compare_reconstruction.py results/volume/density/skull7absorption config-files/skull7absorption.json --views 64 --diffdvrL1 --visCropSlice 73:2:64:96 --visCropRendering 62:250:192:128 --visRenderingDiffSca... | 34,505 | 44.164921 | 305 | py |
DiffDVR | DiffDVR-master/pytests/tests/volume/normalize_dataset.py | """
Normalizes a volume from [minDensity,maxDensity] to [0,1]
"""
import sys
import os
sys.path.insert(0, os.getcwd())
import argparse
import numpy as np
import torch.nn.functional as F
import torch
from diffdvr import Renderer, CameraOnASphere, Settings, setup_default_settings, \
renderer_dtype_torch, renderer_... | 3,245 | 36.310345 | 122 | py |
DiffDVR | DiffDVR-master/pytests/tests/volume/vis_volume.py | import os
import sys
sys.path.append(os.getcwd())
import h5py
import tests.vis_gui
import torch
import numpy as np
import skimage.transform
import pyrenderer
class UIVolume(tests.vis_gui.UI):
def __init__(self, folder, preshaded_to_density):
self._preshaded_to_density = preshaded_to_density
if ... | 9,452 | 45.79703 | 119 | py |
DiffDVR | DiffDVR-master/pytests/tests/volume/test_volume_optimization3.py | import numpy as np
import torch
import torchvision
import matplotlib.pyplot as plt
from diffdvr import Renderer, VolumePreshaded, TfPiecewiseLinear, CameraOnASphere, \
SmoothnessPrior, toCHW, renderer_dtype_torch
import pyrenderer
def optimize():
np.random.seed(42)
torch.random.manual_seed(42)
pyren... | 8,046 | 38.446078 | 114 | py |
DiffDVR | DiffDVR-master/pytests/tests/volume/export_trained_volumes.py | """
Export script for the reconstructions from
train_volume.py, which are normally visualized by vis_volume.py
"""
import os
import sys
sys.path.append(os.getcwd())
import h5py
import tests.vis_gui
import torch
import numpy as np
import skimage.transform
import imageio
from typing import List
from collections import... | 11,701 | 43.158491 | 134 | py |
DiffDVR | DiffDVR-master/pytests/tests/volume/preshaded_to_density.py | import numpy as np
import torch
import torch.nn.functional as F
import os
import sys
import tqdm
import time
import h5py
import argparse
import json
import subprocess
from collections import defaultdict
sys.path.append(os.getcwd())
from diffdvr import renderer_dtype_torch, renderer_dtype_np, \
VolumeDensities, Smo... | 15,721 | 45.931343 | 119 | py |
DiffDVR | DiffDVR-master/pytests/tests/volume/compute_image_statistics.py | import sys
import os
import numpy as np
import torch
import torch.nn.functional as F
import imageio
from typing import List
from losses.lossbuilder import LossBuilder
def toCHW(bhwc : torch.Tensor):
"""
Converts a tensor in BxHxWxC to BxCxHxW
"""
return bhwc.movedim((0,1,2,3), (0,2,3,1))
class SSIM():
d... | 1,838 | 26.863636 | 85 | py |
DiffDVR | DiffDVR-master/pytests/tests/volume/mitsuba/optimize_rb2.py | import os
from os.path import join, dirname, realpath
import pytest
import time
import enoki as ek
import mitsuba
from mitsuba.core import Bitmap, Struct, Thread, EColorMode
from mitsuba.optix import OptiXRenderer
from mitsuba.render.autodiff import get_differentiable_parameters, Adam, SGD
import numpy as np
from ren... | 8,777 | 38.013333 | 98 | py |
DiffDVR | DiffDVR-master/pytests/tests/stepsize/train_stepsize.py |
import sys
import os
sys.path.append(os.getcwd())
import numpy as np
import torch
import torch.nn.functional as F
import os
import tqdm
import time
import h5py
import argparse
import json
from collections import defaultdict
import subprocess
from diffdvr import Renderer, CameraOnASphere, Settings, setup_default_sett... | 13,007 | 47.719101 | 150 | py |
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