repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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NMTGMinor | NMTGMinor-master/onmt/reversible_models/transformers.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.models.transformer_layers import PrePostProcessing
from onmt.modules.linear import FeedForward as position_wise_feed_forward
from onmt.modules.attention import MultiHeadAttention
from torch.autograd.function import Function
import sys
from tor... | 20,232 | 35.001779 | 117 | py |
NMTGMinor | NMTGMinor-master/onmt/reversible_models/transformers_testing2.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.models.transformer_layers import PrePostProcessing
from onmt.modules.linear import FeedForward as position_wise_feed_forward
from onmt.modules.attention import MultiHeadAttention
from torch.autograd.function import Function
import sys
from tor... | 20,730 | 34.559177 | 117 | py |
NMTGMinor | NMTGMinor-master/onmt/reversible_models/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/speech/Augmenter.py | import math
import torch
from collections import defaultdict
import onmt
import random
class Augmenter(object):
"""
Implementation of the "Spec Augmentation" method
(Only vertical and horizontal masking)
"""
def __init__(self, F=8, mf=2, T=64, max_t=0.2, mt=2,
input_size=40, conc... | 1,821 | 26.19403 | 97 | py |
NMTGMinor | NMTGMinor-master/onmt/speech/ctc_loss.py | from distutils.version import LooseVersion
import numpy as np
import six
import torch
import torch.nn.functional as F
import onmt
class CTC(torch.nn.Module):
def __init__(self, vocab_size, hidden_size, dropout_rate,
ctc_type="builtin", reduce=True,
padding_idx=-1, blank_idx=0):
... | 3,645 | 30.162393 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/speech/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/data/mmap_indexed_dataset.py | import os
import struct
import numpy as np
import torch
import torch.utils.data
from functools import lru_cache
def read_longs(f, n):
a = np.empty(n, dtype=np.int64)
f.readinto(a)
return a
def write_longs(f, a):
f.write(np.array(a, dtype=np.int64))
dtypes = {
1: np.uint8,
2: np.int8,
... | 6,566 | 27.184549 | 105 | py |
NMTGMinor | NMTGMinor-master/onmt/data/wav_dataset.py | import torch
import torchaudio as taudio
from functools import lru_cache
from onmt.utils import safe_readaudio
import numpy as np
import soundfile
import math
import torchaudio
import os
# this function reads wav file based on the timestamp in seconds
def safe_readaudio_from_cache(file_, wav_path, start=0.0, end=0.0,... | 3,544 | 30.371681 | 102 | py |
NMTGMinor | NMTGMinor-master/onmt/data/multistream_dataset.py | from __future__ import division
import math
import torch
import torch.utils.data
from collections import defaultdict
import onmt
from onmt.speech.Augmenter import Augmenter
from onmt.modules.dropout import switchout
"""
Data management for stream-to-stream models
Two basic classes:
- Batch stores the input / output ... | 21,315 | 35.62543 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/data/whisper_audio.py | import os
from functools import lru_cache
from typing import Optional, Union
import ffmpeg
import numpy as np
import torch
import torch.nn.functional as F
from .utils import exact_div
# hard-coded audio hyperparameters
SAMPLE_RATE = 16000
N_FFT = 400
N_MELS = 80
HOP_LENGTH = 160
CHUNK_LENGTH = 30
N_SAMPLES = CHUNK_L... | 4,767 | 31.435374 | 99 | py |
NMTGMinor | NMTGMinor-master/onmt/data/batch_utils.py | import numpy as np
# from .fast_extensions import
def _is_oversized(cur_batch, new_sent_size, cur_batch_sizes, batch_size_words, batch_size_sents):
# cur_batch_size = sum(cur_batch_sizes)
if len(cur_batch) == 0:
return False
if len(cur_batch) >= batch_size_sents:
return True
if ma... | 8,598 | 36.064655 | 106 | py |
NMTGMinor | NMTGMinor-master/onmt/data/data_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.
try:
from collections.abc import Iterable
except ImportError:
from collections import Iterable
import contextlib
import itertools
imp... | 823 | 22.542857 | 77 | py |
NMTGMinor | NMTGMinor-master/onmt/data/stream_dataset.py | from __future__ import division
import math
import torch
import torch.utils.data
from collections import defaultdict
import onmt
from onmt.speech.Augmenter import Augmenter
from onmt.modules.dropout import switchout
"""
Data management for stream-to-stream models
Two basic classes:
- Batch stores the input / output ... | 17,676 | 35.598344 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/data/dataset.py | from __future__ import division
import math
import torch
import torch.utils.data
from collections import defaultdict
import onmt
from onmt.speech.Augmenter import Augmenter
from onmt.modules.dropout import switchout
import numpy as np
from .batch_utils import allocate_batch, allocate_batch_unbalanced
import dill
"""
... | 32,665 | 38.499395 | 129 | py |
NMTGMinor | NMTGMinor-master/onmt/data/data_iterator.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
import itertools
import logging
import math
import operator
import os
import queue
import time
from threading import Thread
import random
import numpy as np
import torch
from onmt.data.dataset import rewrap
from onmt.data import data_utils
_sentine... | 14,073 | 31.354023 | 112 | py |
NMTGMinor | NMTGMinor-master/onmt/data/binarizer.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
import os
from onmt.utils import safe_readline, safe_readaudio
# from multiprocessing import Pool
import torch... | 20,533 | 40.906122 | 126 | py |
NMTGMinor | NMTGMinor-master/onmt/data/multi_dataset.py | from __future__ import division
import math
import torch
import torch.utils.data
from collections import defaultdict
from .dataset import Dataset
from .mmap_indexed_dataset import MMapIndexedDataset
from .scp_dataset import SCPIndexDataset
| 242 | 21.090909 | 52 | py |
NMTGMinor | NMTGMinor-master/onmt/data/tokenizer.py | import onmt
def split_line_by_char(line, word_list=["<unk>"]):
chars = list()
words = line.strip().split()
for i, word in enumerate(words):
if word in word_list:
chars.append(word)
else:
for c in word:
chars.append(c)
if i < (len(words) - ... | 3,035 | 28.764706 | 115 | py |
NMTGMinor | NMTGMinor-master/onmt/data/multidata_iterator.py | import itertools
import logging
import math
import operator
import os
import queue
import time
from threading import Thread
from .data_iterator import EpochBatchIterating, DataIterator
import numpy as np
import torch
class MultiEpochIterator(object):
# this class stores N epoch iterators for N datasets
# ini... | 9,036 | 34.163424 | 118 | py |
NMTGMinor | NMTGMinor-master/onmt/data/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/data/scp_dataset.py | import torch
from kaldiio import load_mat
from functools import lru_cache
import numpy as np
from .audio_utils import _parse_arkpath, ArkLoader
import warnings
warnings.filterwarnings("ignore", message="The given NumPy array is not writeable ")
class SCPIndexDataset(torch.utils.data.Dataset):
"""
This dataset... | 1,877 | 29.786885 | 92 | py |
NMTGMinor | NMTGMinor-master/onmt/data/indexed_dataset.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import os
import struct
import nu... | 4,543 | 28.128205 | 84 | py |
NMTGMinor | NMTGMinor-master/onmt/data/audio_utils.py | import numpy as np
from contextlib import contextmanager
import io
from io import TextIOBase
import os
import subprocess
import sys
import warnings
from functools import partial
from io import BytesIO
from io import StringIO
import re
import struct
import sys
import warnings
import soundfile
import math
import torch
f... | 12,439 | 27.863109 | 95 | py |
NMTGMinor | NMTGMinor-master/onmt/data/lm_dataset.py | from __future__ import division
import math
import torch
import torch.utils.data
from collections import defaultdict
import onmt
from onmt.data.dataset import Dataset
class LanguageModelBatch(object):
def __init__(self, data, target, lang, **kwargs):
self.data = data
self.target = target
... | 4,860 | 28.822086 | 108 | py |
NMTGMinor | NMTGMinor-master/onmt/data/kaldiio/utils.py | from __future__ import unicode_literals
from contextlib import contextmanager
import io
from io import TextIOBase
import os
import subprocess
import sys
import warnings
PY3 = sys.version_info[0] == 3
if PY3:
from collections.abc import MutableMapping
string_types = str,
text_type = str
else:
from col... | 11,715 | 27.645477 | 78 | py |
NMTGMinor | NMTGMinor-master/onmt/data/kaldiio/compression_header.py | from __future__ import unicode_literals
import struct
import numpy as np
kAutomaticMethod = 1
kSpeechFeature = 2
kTwoByteAuto = 3
kTwoByteSignedInteger = 4
kOneByteAuto = 5
kOneByteUnsignedInteger = 6
kOneByteZeroOne = 7
class GlobalHeader(object):
"""This is a imitation class of the structure "GlobalHeader" ... | 8,165 | 34.04721 | 77 | py |
NMTGMinor | NMTGMinor-master/onmt/data/kaldiio/wavio.py | from __future__ import unicode_literals
import numpy as np
import kaldiio.python_wave as wave
def read_wav(fd, return_size=False):
wd = wave.open(fd)
rate = wd.getframerate()
nchannels = wd.getnchannels()
nbytes = wd.getsampwidth()
if nbytes == 1:
# 8bit-PCM is unsigned
dtype = '... | 1,370 | 23.927273 | 77 | py |
NMTGMinor | NMTGMinor-master/onmt/data/kaldiio/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/data/kaldiio/io.py | import random
import struct
import numpy as np
import os
def write_ark(ark, dic, scp=None, append=False):
# Write ark
mode = 'ab' if append else 'wb'
pos_list = []
with open(ark, mode) as fd:
pos = fd.tell() if append else 0
for key in dic:
encode_key = (key + ' ').encode()... | 2,028 | 27.577465 | 84 | py |
NMTGMinor | NMTGMinor-master/onmt/train_utils/classify_trainer.py | from __future__ import division
import datetime
import gc
import inspect_model
import math
import os
import re
import time
import torch
import copy
import sys
import contextlib
import onmt
import onmt.markdown
import onmt.modules
from onmt.data.data_iterator import DataIterator
from onmt.data.multidata_iterator impor... | 31,146 | 38.576874 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/train_utils/bayes_by_backprop_trainer.py | from __future__ import division
import datetime
import gc
import inspect_model
import math
import os
import re
import time
import torch
from apex import amp
import onmt
import onmt.markdown
import onmt.modules
from onmt.data.data_iterator import DataIterator
from onmt.data.dataset import rewrap
from onmt.model_factor... | 23,966 | 38.35468 | 117 | py |
NMTGMinor | NMTGMinor-master/onmt/train_utils/stats.py | """ Statistics calculation utility """
from __future__ import division
import time
import math
import sys
import datetime
from onmt.train_utils.meters import AverageMeter, TimeMeter
class Logger(object):
def __init__(self, optim, scaler=None):
self.optim = optim
self.meters = dict()
self... | 4,077 | 33.559322 | 93 | py |
NMTGMinor | NMTGMinor-master/onmt/train_utils/accent_gan_trainer.py | from __future__ import division
import datetime
import gc
import inspect_model
import math
import os
import re
import time
import torch
from apex import amp
import onmt
import onmt.markdown
import onmt.modules
from onmt.data.data_iterator import DataIterator
from onmt.data.dataset import rewrap
from onmt.model_factor... | 39,445 | 36.675263 | 121 | py |
NMTGMinor | NMTGMinor-master/onmt/train_utils/evaluator.py | from __future__ import division
import sys, tempfile
import onmt
import onmt.modules
#~ from onmt.metrics.gleu import sentence_gleu
#~ from onmt.metrics.sbleu import sentence_bleu
from onmt.metrics.bleu import moses_multi_bleu
#~ from onmt.utils import compute_score
import torch
import torch.nn as nn
from torch import... | 7,248 | 34.18932 | 95 | py |
NMTGMinor | NMTGMinor-master/onmt/train_utils/gem_trainer.py | from __future__ import division
import datetime
import gc
import math
import os
import re
import time
import torch
import copy
import sys
import contextlib
import numpy as np
import onmt
import onmt.markdown
import onmt.modules
from onmt.data.data_iterator import DataIterator
from onmt.data.multidata_iterator import ... | 30,109 | 40.077763 | 128 | py |
NMTGMinor | NMTGMinor-master/onmt/train_utils/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/train_utils/mp_trainer.py | from __future__ import division
import datetime
import gc
import math
import os
import re
import time
import torch
import copy
import sys
import contextlib
import onmt
import onmt.markdown
import onmt.modules
from onmt.data.data_iterator import DataIterator
from onmt.data.multidata_iterator import MultiDataIterator
f... | 68,654 | 42.452532 | 121 | py |
NMTGMinor | NMTGMinor-master/onmt/train_utils/meters.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import time
class AverageMeter(o... | 1,838 | 22.576923 | 78 | py |
NMTGMinor | NMTGMinor-master/onmt/inference/perplexity_scorer.py | import onmt
import onmt.modules
import torch.nn as nn
import torch
import math
from torch.autograd import Variable
from onmt.model_factory import build_model
import torch.nn.functional as F
from onmt.inference.search import BeamSearch, DiverseBeamSearch
from onmt.inference.translator import Translator
model_list = ['t... | 3,625 | 30.258621 | 80 | py |
NMTGMinor | NMTGMinor-master/onmt/inference/stream_translator.py | import onmt
import onmt.modules
import torch.nn as nn
import torch
import math
from onmt.model_factory import build_model
import torch.nn.functional as F
from onmt.inference.search import BeamSearch, DiverseBeamSearch
from onmt.inference.translator import Translator
from collections import defaultdict
class StreamTra... | 22,096 | 41.250478 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/inference/Beam.py | from __future__ import division
import torch
import onmt
"""
Class for managing the internals of the beam search process.
hyp1-hyp1---hyp1 -hyp1
\ /
hyp2 \-hyp2 /-hyp2hyp2
/ \
hyp3-hyp3---hyp3 -hyp3
================... | 4,071 | 28.085714 | 80 | py |
NMTGMinor | NMTGMinor-master/onmt/inference/predictor.py | import onmt
import onmt.modules
import torch
from onmt.model_factory import build_classifier
from ae.Autoencoder import Autoencoder
import torch.nn.functional as F
import sys
from onmt.constants import add_tokenidx
from options import backward_compatible
model_list = ['transformer', 'stochastic_transformer', 'fusion_n... | 10,872 | 35.609428 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/inference/global_translator.py | import onmt
import onmt.modules
import torch.nn as nn
import torch
import math
from onmt.model_factory import build_model
import torch.nn.functional as F
from onmt.inference.search import BeamSearch, DiverseBeamSearch
from onmt.inference.translator import Translator
from collections import defaultdict
class GlobalStr... | 25,704 | 41.557947 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/inference/fast_translator.py | import sys
import onmt
import onmt.modules
import torch
import math
from onmt.model_factory import build_model, optimize_model
from onmt.inference.search import BeamSearch, Sampling
from onmt.inference.translator import Translator
from onmt.constants import add_tokenidx
from options import backward_compatible
# buggy ... | 48,877 | 42.641071 | 126 | py |
NMTGMinor | NMTGMinor-master/onmt/inference/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/inference/search.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import torch
import onmt
class S... | 11,084 | 37.224138 | 112 | py |
NMTGMinor | NMTGMinor-master/onmt/inference/ColdFusionTranslator.py | import onmt
import onmt.modules
import torch.nn as nn
import torch
import math
from onmt.model_factory import build_model, build_fusion, build_language_model
from ae.Autoencoder import Autoencoder
import torch.nn.functional as F
import sys
model_list = ['transformer', 'stochastic_transformer', 'fusion_network']
clas... | 15,611 | 35.138889 | 124 | py |
NMTGMinor | NMTGMinor-master/onmt/inference/nam_translate.py | import onmt
import onmt.modules
import torch.nn as nn
import torch
import math
from torch.autograd import Variable
from onmt.model_factory import build_model
import torch.nn.functional as F
from onmt.inference.search import BeamSearch, DiverseBeamSearch
from onmt.inference.translator import Translator
model_list = ['t... | 23,188 | 40.483005 | 119 | py |
NMTGMinor | NMTGMinor-master/onmt/inference/translator.py | import onmt
import onmt.modules
import torch
from onmt.model_factory import build_model, build_language_model, optimize_model
from ae.Autoencoder import Autoencoder
import torch.nn.functional as F
import sys
from onmt.constants import add_tokenidx
from options import backward_compatible
model_list = ['transformer', 's... | 19,446 | 35.485929 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/stochastic_transformer_layers.py | import torch
from onmt.models.transformer_layers import EncoderLayer, DecoderLayer
class StochasticEncoderLayer(EncoderLayer):
"""Wraps multi-head attentions and position-wise feed forward into one encoder layer
Args:
h: number of heads
d_model: dimension of model
p: drop... | 4,693 | 30.716216 | 117 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/Stats.py | """ Statistics calculation utility """
from __future__ import division
import time
import math
import sys
import datetime
from onmt.train_utils.Meters import AverageMeter, TimeMeter
class Logger(object):
def __init__(self, optim, scaler=None):
self.optim = optim
self.meters = dict()
self... | 4,077 | 33.559322 | 93 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/legacy/stochastic_transformers.py | import numpy as np
import torch, math
import torch.nn as nn
import onmt
from onmt.models.transformer_layers import PositionalEncoding
from onmt.models.transformer_layers import EncoderLayer, DecoderLayer
from onmt.legacy.stochastic_transformer_layers import StochasticEncoderLayer, StochasticDecoderLayer
from onmt.model... | 3,473 | 32.085714 | 143 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/Meters.py | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import time
class AverageMeter(o... | 1,840 | 22.602564 | 78 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/DynamicTransformer/Dlcl.py | #!/usr/bin/env python
# encoding: utf-8
"""
@author: Wang Qiang
@contact: wangqiangneu@gmail.com
@desc: connection schema between layers
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class DynamicLinearCombination(nn.Module):
"""Implementation of Dynamic Linear Combina... | 6,453 | 35.88 | 118 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/DynamicTransformer/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/legacy/DynamicTransformer/Models.py | import math
import torch
import onmt
from onmt.legacy.DynamicTransformer.Dlcl import DynamicLinearCombination
from onmt.models.transformers import TransformerEncoder, TransformerDecoder
from onmt.modules.dropout import embedded_dropout
from torch.utils.checkpoint import checkpoint
class DlclTransformerEncoder(Transf... | 9,788 | 36.505747 | 114 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/UniversalTransformer/Layers.py | import math
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.init as init
import torch.nn.utils.weight_norm as WeightNorm
import onmt
import torch.nn.functional as F
from onmt.modules.bottle import Bottle
from onmt.models.transformer_layers import XavierLinear, MultiHeadAttention... | 11,195 | 37.740484 | 156 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/UniversalTransformer/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/legacy/UniversalTransformer/Models.py | import numpy as np
import torch, math
import torch.nn as nn
from onmt.models.transformer_layers import PositionalEncoding
from onmt.models.transformer_layers import EncoderLayer, DecoderLayer
from onmt.legacy.UniversalTransformer.Layers import UniversalDecoderLayer, UniversalEncoderLayer
#~ from onmt.modules.ParallelTr... | 13,602 | 38.428986 | 178 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/ParallelTransformer/Layers.py | import math
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.init as init
import torch.nn.utils.weight_norm as WeightNorm
import onmt
import torch.nn.functional as F
from onmt.modules.bottle import Bottle
from onmt.models.transformer_layers import XavierLinear, MultiHeadAttention... | 9,252 | 40.124444 | 123 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/ParallelTransformer/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/legacy/ParallelTransformer/Models.py | import numpy as np
import torch, math
import torch.nn as nn
from onmt.models.transformer_layers import PositionalEncoding
from onmt.models.transformer_layers import EncoderLayer, DecoderLayer
from onmt.legacy.ParallelTransformer.Layers import ParallelEncoderLayer
from onmt.modules.base_seq2seq import NMTModel, Reconstr... | 25,098 | 39.417069 | 175 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/old_models/distance_transformer_layers.py | import torch
import torch.nn as nn
import onmt
from onmt.models.transformer_layers import PrePostProcessing, MultiHeadAttention, Linear
from onmt.modules.relative_attention import RelPartialLearnableMultiHeadAttn
from onmt.utils import flip
from onmt.modules.bottle import Bottle
from onmt.modules.linear import XavierL... | 9,073 | 40.43379 | 116 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/old_models/relative_unified_transformer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.models.transformer_layers import PositionalEncoding, PrePostProcessing
from onmt.models.transformer_layers import EncoderLayer, DecoderLayer
from onmt.models.transformers import TransformerEncoder, TransformerDecoder, TransformerDecodingState
... | 24,270 | 36.982786 | 116 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/old_models/memory_transformer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from onmt.modules.relative_attention import RelPartialLearnableMultiHeadAttn
from onmt.models.transformer_layers import PositionalEncoding, PrePostProcessing
from onmt.models.transformer_layers import EncoderLayer, DecoderLayer
from onmt.models.transfor... | 32,849 | 37.06489 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/old_models/reformer.py | # coding=utf-8
# Copyright 2020 The Trax 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 copy of the License at... | 5,517 | 41.446154 | 122 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/old_models/relative_universal_transformer_layers.py | import torch
import torch.nn as nn
import onmt
from onmt.models.transformer_layers import PrePostProcessing, MultiHeadAttention, Linear
from onmt.modules.relative_attention import RelPartialLearnableMultiHeadAttn
from onmt.modules.optimized.relative_self_attention import RelativeSelfMultiheadAttn
from onmt.utils impor... | 10,170 | 45.231818 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/old_models/distance_transformer.py | import torch
import torch.nn as nn
from onmt.models.transformer_layers import PositionalEncoding, PrePostProcessing
from onmt.models.transformer_layers import EncoderLayer, DecoderLayer
from onmt.models.transformers import TransformerEncoder, TransformerDecoder, Transformer, TransformerDecodingState
import onmt
from on... | 30,203 | 41.721358 | 127 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/old_models/unified_transformer.py | import torch
import torch.nn as nn
from onmt.models.transformer_layers import PositionalEncoding, PrePostProcessing
from onmt.models.transformers import TransformerEncoder, TransformerDecoder, TransformerDecodingState
import onmt
from onmt.modules.dropout import embedded_dropout
from onmt.models.transformer_layers impo... | 19,467 | 40.866667 | 119 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/old_models/universal_transformer.py | import torch
import torch.nn as nn
from onmt.models.transformer_layers import PositionalEncoding, PrePostProcessing
from onmt.models.transformer_layers import EncoderLayer, DecoderLayer
from onmt.models.transformers import TransformerEncoder, TransformerDecoder, Transformer, TransformerDecodingState
import onmt
from on... | 14,946 | 42.074928 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/old_models/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/legacy/old_models/universal_transformer_layers.py | import math
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.init as init
import torch.nn.utils.weight_norm as WeightNorm
import onmt
import torch.nn.functional as F
from onmt.modules.bottle import Bottle
from onmt.modules.static_dropout import StaticDropout
from onmt.modules.linea... | 4,861 | 33.48227 | 87 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/old_models/relative_universal_transformer.py | import torch
import torch.nn as nn
from onmt.models.transformer_layers import PositionalEncoding, PrePostProcessing
from onmt.models.transformer_layers import EncoderLayer, DecoderLayer
from onmt.models.transformers import TransformerEncoder, TransformerDecoder, Transformer, TransformerDecodingState
import onmt
from on... | 16,566 | 41.155216 | 120 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/FCTransformer/Layers.py | import math
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.init as init
import torch.nn.utils.weight_norm as WeightNorm
import onmt
import torch.nn.functional as F
from onmt.modules.bottle import Bottle
from onmt.models.transformer_layers import XavierLinear, MultiHeadAttention... | 21,054 | 38.801512 | 173 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/FCTransformer/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/legacy/FCTransformer/Models.py | import numpy as np
import torch, math
import torch.nn as nn
from onmt.models.transformer_layers import PositionalEncoding
from onmt.legacy.FCTransformer.Layers import FCTEncoderLayer, FCTDecoderLayer
from onmt.modules.base_seq2seq import NMTModel, Reconstructor
import onmt
from onmt.modules.dropout import embedded_drop... | 12,177 | 38.411003 | 170 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/LSTMLM/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/legacy/LSTMLM/Models.py | import numpy as np
import torch, math
import torch.nn as nn
from onmt.models.transformers import TransformerDecodingState
from onmt.modules.base_seq2seq import NMTModel, Reconstructor, DecoderState
import onmt
from onmt.modules.dropout import embedded_dropout
#~ from onmt.modules.Checkpoint import checkpoint
from torch... | 9,163 | 29.751678 | 113 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/FusionNetwork/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/legacy/FusionNetwork/Models.py | import numpy as np
import torch, math
import torch.nn as nn
from onmt.modules.base_seq2seq import DecoderState
from onmt.models.transformers import TransformerDecodingState
from collections import defaultdict
import torch.nn.functional as F
class FusionNetwork(nn.Module):
"""Main model in 'Attention is all you ne... | 6,887 | 33.964467 | 109 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/DynamicConvolution/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/legacy/MixtureModel/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/legacy/MixtureModel/Models.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/legacy/TransformerLM/Layers.py | import math
import torch
import torch.nn as nn
import torch.nn.init as init
import onmt
import torch.nn.functional as F
from onmt.models.transformer_layers import PrePostProcessing, MultiHeadAttention, Bottle, FeedForward
class LMDecoderLayer(nn.Module):
"""Wraps multi-head attentions and position-wise feed fo... | 3,338 | 32.059406 | 113 | py |
NMTGMinor | NMTGMinor-master/onmt/legacy/TransformerLM/__init__.py | 0 | 0 | 0 | py | |
NMTGMinor | NMTGMinor-master/onmt/legacy/TransformerLM/Models.py | import numpy as np
import torch, math
import torch.nn as nn
from onmt.models.transformers import TransformerDecodingState
from onmt.modules.base_seq2seq import NMTModel, Reconstructor, DecoderState
import onmt
from onmt.modules.dropout import embedded_dropout
#~ from onmt.modules.Checkpoint import checkpoint
from torch... | 8,777 | 32.632184 | 112 | py |
pixyz | pixyz-main/setup.py | import io
import os
import re
from setuptools import setup, find_packages
def read(*names, **kwargs):
with io.open(
os.path.join(os.path.dirname(__file__), *names),
encoding=kwargs.get("encoding", "utf8")
) as fp:
return fp.read()
def find_version(*file_paths):
version_file = rea... | 2,028 | 26.053333 | 68 | py |
pixyz | pixyz-main/pixyz/utils.py | import functools
import torch
import sympy
from IPython.display import Math
import pixyz
_EPSILON = 1e-07
_CACHE_MAXSIZE = 2 * 10
def set_epsilon(eps):
"""Set a `epsilon` parameter.
Parameters
----------
eps : int or float
Returns
-------
Examples
--------
>>> from unittest imp... | 10,567 | 24.965602 | 111 | py |
pixyz | pixyz-main/pixyz/__init__.py | name = "pixyz"
__version__ = "0.3.3"
| 37 | 11.666667 | 21 | py |
pixyz | pixyz-main/pixyz/distributions/distributions.py | from __future__ import print_function
import torch
import re
import networkx as nx
from torch import nn
from ..utils import get_dict_values, replace_dict_keys, delete_dict_values,\
tolist, sum_samples, convert_latex_name, lru_cache_for_sample_dict
from ..losses import LogProb, Prob
def _make_prob_text(dist_name,... | 70,384 | 36.800752 | 137 | py |
pixyz | pixyz-main/pixyz/distributions/exponential_distributions.py | import torch
from torch.distributions import Normal as NormalTorch
from torch.distributions import Bernoulli as BernoulliTorch
from torch.distributions import RelaxedBernoulli as RelaxedBernoulliTorch
from torch.distributions import RelaxedOneHotCategorical as RelaxedOneHotCategoricalTorch
from torch.distributions.one_... | 14,788 | 33.154734 | 117 | py |
pixyz | pixyz-main/pixyz/distributions/poe.py | from __future__ import print_function
import torch
from torch import nn
from ..utils import tolist, get_dict_values
from ..distributions import Normal
class ProductOfNormal(Normal):
r"""Product of normal distributions.
.. math::
p(z|x,y) \propto p(z)p(z|x)p(z|y)
In this models, :math:`p(z|x)` and... | 16,619 | 38.856115 | 118 | py |
pixyz | pixyz-main/pixyz/distributions/mixture_distributions.py | import torch
from torch import nn
from ..distributions.distributions import Distribution
from ..utils import convert_latex_name
class MixtureModel(Distribution):
r"""Mixture models.
.. math::
p(x) = \sum_i p(x|z=i)p(z=i)
Examples
--------
>>> from pixyz.distributions import Normal, Cat... | 8,520 | 32.415686 | 116 | py |
pixyz | pixyz-main/pixyz/distributions/custom_distributions.py | from ..utils import get_dict_values, sum_samples
from .distributions import Distribution
class CustomProb(Distribution):
"""This distribution is constructed by user-defined probability density/mass function.
Note that this distribution cannot perform sampling.
Examples
--------
>>> import torch
... | 2,210 | 29.708333 | 90 | py |
pixyz | pixyz-main/pixyz/distributions/moe.py | from __future__ import print_function
import torch
from torch import nn
import numpy as np
from ..utils import tolist, get_dict_values
from ..distributions import Normal
class MixtureOfNormal(Normal):
r"""Mixture of normal distributions.
.. math::
p(z|x,y) = p(z|x) + p(z|y)
In this models, :math:... | 5,758 | 32.876471 | 162 | py |
pixyz | pixyz-main/pixyz/distributions/special_distributions.py | from __future__ import print_function
from .distributions import Distribution
class Deterministic(Distribution):
"""
Deterministic distribution (or degeneration distribution)
Examples
--------
>>> import torch
>>> class Generator(Deterministic):
... def __init__(self):
... ... | 3,517 | 27.836066 | 98 | py |
pixyz | pixyz-main/pixyz/distributions/__init__.py | from .exponential_distributions import (
Normal,
Bernoulli,
RelaxedBernoulli,
FactorizedBernoulli,
Categorical,
RelaxedCategorical,
Multinomial,
Dirichlet,
Beta,
Laplace,
Gamma,
)
from .custom_distributions import (
CustomProb,
)
from .special_distributions import (
... | 1,300 | 19.015385 | 86 | py |
pixyz | pixyz-main/pixyz/distributions/flow_distribution.py | import torch
from ..distributions import Distribution
from ..utils import get_dict_values
class TransformedDistribution(Distribution):
r"""
Convert flow transformations to distributions.
.. math::
p(z=f_{flow}(x)),
where :math:`x \sim p_{prior}(x)`.
Once initializing, it can be handle... | 9,870 | 28.912121 | 119 | py |
pixyz | pixyz-main/pixyz/flows/__init__.py | from .flows import (
Flow,
FlowList,
)
from .normalizing_flows import (
PlanarFlow
)
from .coupling import (
AffineCoupling,
)
from .conv import (
ChannelConv
)
from .operations import (
Squeeze,
Unsqueeze,
Permutation,
Shuffle,
Reverse,
Flatten,
Preprocess,
)
from .... | 666 | 12.078431 | 32 | py |
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