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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DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/fairseq/data/bucket_pad_length_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.nn.functional as F
from fairseq.data import BaseWrapperDataset
class BucketPadLengthDataset(BaseWrapperDatas... | 2,260 | 28.363636 | 79 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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().... | 1,558 | 27.345455 | 83 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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
from fairseq.data import data_utils
class EpochListening:
"""Mixin for receiving updates when... | 6,638 | 33.578125 | 91 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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... | 4,575 | 36.818182 | 88 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/fairseq/data/multilingual/sampled_multi_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 datetime
import hashlib
import logging
import time
from bisect import bisect_right
from collections import OrderedDict, defaultdict
fro... | 17,925 | 38.054466 | 119 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/fairseq/data/multilingual/multilingual_utils.py | from enum import Enum
from typing import Dict, List, Optional, Sequence
import torch
from fairseq.data import Dictionary
class EncoderLangtok(Enum):
"""
Prepend to the beginning of source sentence either the
source or target language token. (src/tgt).
"""
src = "src"
tgt = "tgt"
class Lang... | 1,623 | 24.375 | 85 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/fairseq/data/multilingual/sampled_multi_epoch_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 hashlib
import logging
import math
import numpy as np
from fairseq.data import SampledMultiDataset
from .sampled_multi_dataset import... | 7,827 | 38.14 | 119 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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 logging
import os
import sys
import numpy as np
import torch
import torch.nn.functional as F
from .. import FairseqDataset
logger ... | 5,296 | 28.592179 | 88 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/fairseq/data/audio/audio_utils.py | import os.path as op
from typing import BinaryIO, Optional, Tuple, Union
import numpy as np
def get_waveform(
path_or_fp: Union[str, BinaryIO], normalization=True
) -> Tuple[np.ndarray, int]:
"""Get the waveform and sample rate of a 16-bit mono-channel WAV or FLAC.
Args:
path_or_fp (str or Binar... | 3,004 | 33.94186 | 84 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/fairseq/data/audio/speech_to_text_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 csv
import io
import logging
import os.path as op
import re
from typing import Dict, List, Optional, Tuple
import numpy as np
import t... | 18,954 | 34.831758 | 87 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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... | 909 | 28.354839 | 67 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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 ... | 12,877 | 40.275641 | 99 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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
from typing import Dict, List, Tuple
import numpy as np
import torch
from fairseq.data import Dictionary, FairseqDataset, data_ut... | 12,168 | 39.029605 | 88 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/fairseq/tasks/translation_from_pretrained_bart.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 fairseq.data import LanguagePairDataset
from . import register_task
from .translation import Tran... | 5,247 | 38.458647 | 108 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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 logging
import os
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from fairseq im... | 11,742 | 34.801829 | 91 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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 logging
import os
import numpy as np
import torch
from fairseq import utils
from fairseq.data import (
ConcatDataset,
Dictiona... | 12,146 | 34.831858 | 87 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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.
import contextlib
import logging
import os
from collections import OrderedDict
import torch
from fairseq import metrics, options, utils
from ... | 17,650 | 38.224444 | 113 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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 os
import torch
from fairseq import utils
from fairseq.data import LanguagePairDataset
from fairseq.tasks import register_task
from fa... | 7,496 | 37.446154 | 103 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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 logging
import os
import warnings
from argparse import Namespace
import torch
from fairseq import metrics, search, tokenizer, utils
fr... | 21,234 | 36.451499 | 87 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/fairseq/tasks/translation_multi_simple_epoch.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 datetime
import logging
import time
import torch
from fairseq.data import (
FairseqDataset,
LanguagePairDataset,
ListDatas... | 16,294 | 38.26506 | 113 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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 ast
import logging
import math
import os
import sy... | 13,923 | 35.260417 | 180 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/fairseq_cli/validate.py | #!/usr/bin/env python3 -u
#!/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.
import logging
import os
import sys
from itertools import chain
import torch
from fairseq... | 4,385 | 31.488889 | 88 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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 logging
import math
import os
import torch
fro... | 9,276 | 32.132143 | 108 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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.
"""
import fileinput
import logging
import mat... | 10,787 | 33.576923 | 88 | py |
DDRS-NAT | DDRS-NAT-master/build/lib.linux-x86_64-3.8/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 pdb
import argparse
import logging
import math
impor... | 12,073 | 32.726257 | 93 | py |
crnn.pytorch | crnn.pytorch-master/utils.py | #!/usr/bin/python
# encoding: utf-8
import torch
import torch.nn as nn
from torch.autograd import Variable
import collections
class strLabelConverter(object):
"""Convert between str and label.
NOTE:
Insert `blank` to the alphabet for CTC.
Args:
alphabet (str): set of the possible charac... | 4,529 | 29.2 | 136 | py |
crnn.pytorch | crnn.pytorch-master/dataset.py | #!/usr/bin/python
# encoding: utf-8
import random
import torch
from torch.utils.data import Dataset
from torch.utils.data import sampler
import torchvision.transforms as transforms
import lmdb
import six
import sys
from PIL import Image
import numpy as np
class lmdbDataset(Dataset):
def __init__(self, root=None... | 3,965 | 27.948905 | 78 | py |
crnn.pytorch | crnn.pytorch-master/demo.py | import torch
from torch.autograd import Variable
import utils
import dataset
from PIL import Image
import models.crnn as crnn
model_path = './data/crnn.pth'
img_path = './data/demo.png'
alphabet = '0123456789abcdefghijklmnopqrstuvwxyz'
model = crnn.CRNN(32, 1, 37, 256)
if torch.cuda.is_available():
model = mode... | 1,063 | 25.6 | 67 | py |
crnn.pytorch | crnn.pytorch-master/train.py | from __future__ import print_function
from __future__ import division
import argparse
import random
import torch
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
from torch.autograd import Variable
import numpy as np
from warpctc_pytorch import CTCLoss
import os
import utils
imp... | 7,841 | 35.64486 | 117 | py |
crnn.pytorch | crnn.pytorch-master/models/crnn.py | import torch.nn as nn
class BidirectionalLSTM(nn.Module):
def __init__(self, nIn, nHidden, nOut):
super(BidirectionalLSTM, self).__init__()
self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True)
self.embedding = nn.Linear(nHidden * 2, nOut)
def forward(self, input):
recurrent,... | 2,554 | 30.9375 | 78 | py |
crnn.pytorch | crnn.pytorch-master/test/test_utils.py | #!/usr/bin/python
# encoding: utf-8
import sys
import unittest
import torch
from torch.autograd import Variable
import collections
origin_path = sys.path
sys.path.append("..")
import utils
sys.path = origin_path
def equal(a, b):
if isinstance(a, torch.Tensor):
return a.equal(b)
elif isinstance(a, str... | 3,151 | 27.917431 | 80 | py |
crnn.pytorch | crnn.pytorch-master/tool/convert_t7.py | import torchfile
import argparse
import torch
from torch.nn.parameter import Parameter
import numpy as np
import models.crnn as crnn
layer_map = {
'SpatialConvolution': 'Conv2d',
'SpatialBatchNormalization': 'BatchNorm2d',
'ReLU': 'ReLU',
'SpatialMaxPooling': 'MaxPool2d',
'SpatialAveragePooling': ... | 5,075 | 29.214286 | 76 | py |
grin | grin-main/scripts/run_synthetic.py | import copy
import datetime
import os
import pathlib
from argparse import ArgumentParser
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
import yaml
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLog... | 9,402 | 41.355856 | 120 | py |
grin | grin-main/scripts/run_imputation.py | import copy
import datetime
import os
import pathlib
from argparse import ArgumentParser
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
import yaml
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.loggers import TensorBoardLog... | 12,130 | 42.636691 | 114 | py |
grin | grin-main/lib/nn/models/rgain.py | import torch
from torch import nn
from .rnn_imputers import BiRNNImputer
from ...utils.parser_utils import str_to_bool
class Generator(nn.Module):
def __init__(self, d_in, d_model, d_z, dropout=0., inject_noise=True):
super(Generator, self).__init__()
self.inject_noise = inject_noise
self... | 2,564 | 36.173913 | 102 | py |
grin | grin-main/lib/nn/models/var.py | import torch
from einops import rearrange
from torch import nn
from lib import epsilon
class VAR(nn.Module):
def __init__(self, order, d_in, d_out=None, steps_ahead=1, bias=True):
super(VAR, self).__init__()
self.order = order
self.d_in = d_in
self.d_out = d_out if d_out is not No... | 2,612 | 35.802817 | 82 | py |
grin | grin-main/lib/nn/models/rnn_imputers.py | import torch
from torch import nn
from ..utils.ops import reverse_tensor
class RNNImputer(nn.Module):
"""Fill the blanks with a 1-step-ahead GRU predictor."""
def __init__(self, d_in, d_model, concat_mask=True, detach_inputs=False, state_init='zero', d_u=0):
super(RNNImputer, self).__init__()
... | 3,972 | 37.572816 | 115 | py |
grin | grin-main/lib/nn/models/grin.py | import torch
from einops import rearrange
from torch import nn
from ..layers import BiGRIL
from ...utils.parser_utils import str_to_bool
class GRINet(nn.Module):
def __init__(self,
adj,
d_in,
d_hidden,
d_ff,
ff_dropout,
... | 3,589 | 42.253012 | 105 | py |
grin | grin-main/lib/nn/models/mpgru.py | import torch
from einops import rearrange
from torch import nn
from ..layers import MPGRUImputer, SpatialConvOrderK
from ..utils.ops import reverse_tensor
from ...utils.parser_utils import str_to_bool
class MPGRUNet(nn.Module):
def __init__(self,
adj,
d_in,
d_hi... | 8,025 | 42.857923 | 120 | py |
grin | grin-main/lib/nn/models/brits.py | import torch
from torch import nn
from ..layers import BRITS
class BRITSNet(nn.Module):
def __init__(self,
d_in,
d_hidden=64):
super(BRITSNet, self).__init__()
self.birits = BRITS(input_size=d_in,
hidden_size=d_hidden)
def forward... | 1,171 | 36.806452 | 110 | py |
grin | grin-main/lib/nn/layers/spatial_conv.py | import torch
from torch import nn
from ... import epsilon
class SpatialConvOrderK(nn.Module):
"""
Spatial convolution of order K with possibly different diffusion matrices (useful for directed graphs)
Efficient implementation inspired from graph-wavenet codebase
"""
def __init__(self, c_in, c_o... | 2,253 | 31.2 | 106 | py |
grin | grin-main/lib/nn/layers/gril.py | import torch
import torch.nn as nn
from einops import rearrange
from .spatial_conv import SpatialConvOrderK
from .gcrnn import GCGRUCell
from .spatial_attention import SpatialAttention
from ..utils.ops import reverse_tensor
class SpatialDecoder(nn.Module):
def __init__(self, d_in, d_model, d_out, support_len, or... | 11,916 | 42.652015 | 133 | py |
grin | grin-main/lib/nn/layers/spatial_attention.py | import torch.nn as nn
from einops import rearrange
class SpatialAttention(nn.Module):
def __init__(self, d_in, d_model, nheads, dropout=0.):
super(SpatialAttention, self).__init__()
self.lin_in = nn.Linear(d_in, d_model)
self.self_attn = nn.MultiheadAttention(d_model, nheads, dropout=drop... | 971 | 32.517241 | 81 | py |
grin | grin-main/lib/nn/layers/imputation.py | import math
import torch
from torch import nn
from torch.nn import functional as F
class ImputationLayer(nn.Module):
def __init__(self, d_in, bias=True):
super(ImputationLayer, self).__init__()
self.W = nn.Parameter(torch.Tensor(d_in, d_in))
if bias:
self.b = nn.Parameter(torc... | 897 | 29.965517 | 69 | py |
grin | grin-main/lib/nn/layers/mpgru.py | import torch
from torch import nn
from .gcrnn import GCGRUCell
class MPGRUImputer(nn.Module):
def __init__(self,
input_size,
hidden_size,
ff_size=None,
u_size=None,
n_layers=1,
dropout=0.,
kerne... | 4,835 | 38.317073 | 116 | py |
grin | grin-main/lib/nn/layers/rits.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.parameter import Parameter
from ..utils.ops import reverse_tensor
class FeatureRegression(nn.Module):
def __init__(self, input_size):
super(FeatureRegression, self).__init__()... | 5,700 | 32.145349 | 108 | py |
grin | grin-main/lib/nn/layers/gcrnn.py | import torch
import torch.nn as nn
from .spatial_conv import SpatialConvOrderK
class GCGRUCell(nn.Module):
"""
Graph Convolution Gated Recurrent Unit Cell.
"""
def __init__(self, d_in, num_units, support_len, order, activation='tanh'):
"""
:param num_units: the hidden dim of rnn
... | 3,184 | 36.470588 | 117 | py |
grin | grin-main/lib/nn/utils/metric_base.py | from functools import partial
import torch
from pytorch_lightning.metrics import Metric
from torchmetrics.utilities.checks import _check_same_shape
class MaskedMetric(Metric):
def __init__(self,
metric_fn,
mask_nans=False,
mask_inf=False,
comput... | 2,660 | 33.115385 | 87 | py |
grin | grin-main/lib/nn/utils/metrics.py | from .metric_base import MaskedMetric
from .ops import mape
from torch.nn import functional as F
import torch
from torchmetrics.utilities.checks import _check_same_shape
from ... import epsilon
class MaskedMAE(MaskedMetric):
def __init__(self,
mask_nans=False,
mask_inf=False,
... | 4,835 | 39.3 | 96 | py |
grin | grin-main/lib/nn/utils/ops.py | import torch
import torch.nn.functional as F
from einops import reduce
from torch.autograd import Variable
from ... import epsilon
def mae(y_hat, y, reduction='none'):
return F.l1_loss(y_hat, y, reduction=reduction)
def mape(y_hat, y):
return torch.abs((y_hat - y) / y)
def wape_loss(y_hat, y):
l = to... | 1,953 | 26.138889 | 88 | py |
grin | grin-main/lib/fillers/rgainfiller.py | import torch
from torch.nn import functional as F
from .multi_imputation_filler import MultiImputationFiller
from ..nn.utils.metric_base import MaskedMetric
class MaskedBCEWithLogits(MaskedMetric):
def __init__(self,
mask_nans=False,
mask_inf=False,
compute_on_s... | 6,340 | 42.136054 | 110 | py |
grin | grin-main/lib/fillers/britsfiller.py | import torch
from . import Filler
from ..nn import BRITS
class BRITSFiller(Filler):
def training_step(self, batch, batch_idx):
# Unpack batch
batch_data, batch_preprocessing = self._unpack_batch(batch)
# Extract mask and target
mask = batch_data['mask'].clone().detach()
... | 3,431 | 39.857143 | 108 | py |
grin | grin-main/lib/fillers/multi_imputation_filler.py | import torch
from pytorch_lightning.core.decorators import auto_move_data
from . import Filler
class MultiImputationFiller(Filler):
"""
Filler with multiple imputation outputs
"""
def __init__(self,
model_class,
model_kwargs,
optim_class,
... | 2,930 | 35.185185 | 104 | py |
grin | grin-main/lib/fillers/filler.py | import inspect
from copy import deepcopy
import pytorch_lightning as pl
import torch
from pytorch_lightning.core.decorators import auto_move_data
from pytorch_lightning.metrics import MetricCollection
from pytorch_lightning.utilities import move_data_to_device
from .. import epsilon
from ..nn.utils.metric_base import... | 12,351 | 40.589226 | 146 | py |
grin | grin-main/lib/fillers/graphfiller.py | import torch
from . import Filler
from ..nn.models import MPGRUNet, GRINet, BiMPGRUNet
class GraphFiller(Filler):
def __init__(self,
model_class,
model_kwargs,
optim_class,
optim_kwargs,
loss_fn,
scaled_target=... | 5,454 | 40.015038 | 108 | py |
grin | grin-main/lib/datasets/synthetic.py | import os.path
import numpy as np
import torch
from einops import rearrange
from torch.utils.data import Dataset, DataLoader, Subset
from lib import datasets_path
def generate_mask(shape, p_block=0.01, p_point=0.01, max_seq=1, min_seq=1, rng=None):
"""Generate mask in which 1 denotes valid values, 0 missing one... | 7,408 | 36.231156 | 112 | py |
grin | grin-main/lib/datasets/pd_dataset.py | import numpy as np
import pandas as pd
import torch
class PandasDataset:
def __init__(self, dataframe: pd.DataFrame, u: pd.DataFrame = None, name='pd-dataset', mask=None, freq=None,
aggr='sum', **kwargs):
"""
Initialize a tsl dataset from a pandas dataframe.
:param dataf... | 3,435 | 27.163934 | 112 | py |
grin | grin-main/lib/data/temporal_dataset.py | import numpy as np
import pandas as pd
import torch
from einops import rearrange
from pandas import DatetimeIndex
from torch.utils.data import Dataset
from .preprocessing import AbstractScaler
class TemporalDataset(Dataset):
def __init__(self, data,
index=None,
freq=None,
... | 12,077 | 36.981132 | 110 | py |
grin | grin-main/lib/data/spatiotemporal_dataset.py | import numpy as np
import pandas as pd
from einops import rearrange
from .temporal_dataset import TemporalDataset
class SpatioTemporalDataset(TemporalDataset):
def __init__(self, data,
index=None,
trend=None,
scaler=None,
freq=None,
... | 3,006 | 38.051948 | 102 | py |
grin | grin-main/lib/data/imputation_dataset.py | import numpy as np
import torch
from . import TemporalDataset, SpatioTemporalDataset
class ImputationDataset(TemporalDataset):
def __init__(self, data,
index=None,
mask=None,
eval_mask=None,
freq=None,
trend=None,
... | 1,615 | 34.911111 | 81 | py |
grin | grin-main/lib/data/datamodule/spatiotemporal.py | import pytorch_lightning as pl
from torch.utils.data import DataLoader, Subset, RandomSampler
from .. import TemporalDataset, SpatioTemporalDataset
from ..preprocessing import StandardScaler, MinMaxScaler
from ...utils import ensure_list
from ...utils.parser_utils import str_to_bool
class SpatioTemporalDataModule(pl... | 5,840 | 39.006849 | 116 | py |
grin | grin-main/lib/data/preprocessing/scalers.py | from abc import ABC, abstractmethod
import numpy as np
class AbstractScaler(ABC):
def __init__(self, **kwargs):
for k, v in kwargs.items():
setattr(self, k, v)
def __repr__(self):
params = ", ".join([f"{k}={str(v)}" for k, v in self.params().items()])
return "{}({})".form... | 2,851 | 27.52 | 101 | py |
performant | performant-main/src/performant_live/train_tacotron2.py | # -*- coding: utf-8 -*-
# Copyright 2020 Minh Nguyen (@dathudeptrai)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ... | 26,602 | 36.521862 | 145 | py |
irj-neural-april | irj-neural-april-master/summariser/rl/deep_td.py | import sys
import os
sys.path.append(os.path.abspath('..'))
sys.path.append(os.path.abspath('./'))
from summariser.utils.corpus_reader import CorpusReader
from summariser.vector.state_type import State
#from summariser.utils.summary_samples_reader import *
from summariser.utils.misc import softmaxSample
from resource... | 6,849 | 36.637363 | 168 | py |
MSAU | MSAU-master/data_generator_funsd_bert.py | from __future__ import division, print_function
from queue import Queue
import pickle
import threading
from random import uniform
from random import shuffle
import numpy as np
import math
import torch
from scipy import ndimage
import json
import random
import time
import glob
from utils.path_util import read_image_lis... | 11,630 | 38.832192 | 106 | py |
MSAU | MSAU-master/train_chargrid_funsd_msau.py | from __future__ import print_function, division, unicode_literals
import torch, pickle, time, matplotlib, os, json
import torch.nn as nn
from torch.autograd import Variable
from data_generator_funsd_bert import FUNSDCharGridDataLoaderBoxMaskBoxLabel
import utils.io_utils as io_utils
import numpy as np
import sklearn.me... | 9,582 | 35.576336 | 107 | py |
MSAU | MSAU-master/data_generator/data_generator_text.py | import torch
from queue import Queue
import threading
import os
from random import uniform
from random import shuffle
import numpy as np
from scipy import ndimage
import json
import random
from utils.path_util import read_image_list
from utils.image_util import affine_transform, elastic_transform, \
to_categorical... | 15,582 | 39.162371 | 120 | py |
MSAU | MSAU-master/utils/io_utils.py | """ io_utils.py
Utilities for reading and writing logs.
"""
import os
import statistics
import re
import csv
import numpy as np
import pandas as pd
import scipy as sc
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import torch
import networkx as nx
import tensorboardX
import cv2
import ... | 24,746 | 30.890464 | 112 | py |
MSAU | MSAU-master/model/model.py | from __future__ import print_function, division
import torch
from .layers import layers
from .layers import attention
from collections import OrderedDict
class MultiConvResidualBlock(torch.nn.Module):
def __init__(self, res_depth, filter_size, channels,
use_sparse_conv, activation):
super... | 20,089 | 42.673913 | 111 | py |
MSAU | MSAU-master/model/model_box.py | from __future__ import print_function, division
import torch
from .layers import layers
from .layers import attention
from collections import OrderedDict
from box_convolution import BoxConv2d
class MultiBoxConvBlock(torch.nn.Module):
'''
Args:
channels: number of in channels (also out channels)
... | 17,011 | 40.798526 | 140 | py |
MSAU | MSAU-master/model/training/cost.py | import torch
import torch.nn.functional as F
import numpy as np
class UNetLoss(torch.nn.Module):
"""
Constructs the cost function, either cross_entropy, weighted cross_entropy or dice_coefficient.
Optional arguments are:
class_weights: weights for the different classes in case of multi-class imbalance... | 2,704 | 35.554054 | 99 | py |
MSAU | MSAU-master/model/training/trainer.py | from __future__ import print_function, division
import torch
import os
import time
from .cost import UNetLoss
from .optimizer import get_optimizer
device = torch.device("cuda:0")
class Trainer(object):
"""
Trains a MSAU-net instance
:param net: the arunet instance to train
:param opt_kwargs: (optio... | 9,309 | 40.19469 | 92 | py |
MSAU | MSAU-master/model/training/optimizer.py | import torch
def get_optimizer(model, kwargs={}):
params = model.parameters()
optimizer_name = kwargs.get("optimizer", "rmsprop")
learning_rate = kwargs.get("learning_rate", 0.001)
lr_decay_rate = kwargs.get("lr_decay_rate", 0.0)
if optimizer_name == "momentum":
momentum = kwargs.get("mom... | 1,062 | 33.290323 | 68 | py |
MSAU | MSAU-master/model/layers/cspn.py | # -*- coding: utf-8 -*-
"""
Created on Sun Feb 4 15:37:41 2018
@author: Xinjing Cheng
@email : chengxinjing@baidu.com
"""
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch
from torch.autograd import Variable
class Affinity_Propagate(nn.Module):
def __init__(self, spn=False... | 4,861 | 46.666667 | 121 | py |
MSAU | MSAU-master/model/layers/utils.py | import torch.nn.functional as F
import numpy as np
def pad_2d(inp, padding, kind, k_h, k_w, s_h=1, s_w=1, dilation=1):
if padding == 'VALID':
return inp
elif padding == 'SAME' and kind in ('conv2d', 'pool2d'):
in_height, in_width = inp.size(2), inp.size(3)
out_height = int(np.ceil(floa... | 1,173 | 36.870968 | 75 | py |
MSAU | MSAU-master/model/layers/layers.py | from __future__ import print_function, division
import torch
from torch.nn.parameter import Parameter
import torch.nn.functional as F
import numpy as np
from .utils import pad_2d
from .sparse_conv import SparseConv
class Conv2dBnLrnDrop(torch.nn.Module):
"""
Conv2d with optional BatchNorm, Local Response Norm... | 10,466 | 39.103448 | 107 | py |
MSAU | MSAU-master/model/layers/sparse_conv.py | import torch
from torch.nn.parameter import Parameter
from .utils import pad_2d
class SparseConv(torch.nn.Module):
"""
Arguments
tensor: Tensor input.
binary_mask: Tensor, a mask with the same size as tensor,
channel size = 1
out_channels: Integer, the dimensionali... | 2,838 | 40.144928 | 77 | py |
MSAU | MSAU-master/model/layers/attention.py | from __future__ import absolute_import, division, print_function
import torch
import numpy as np
import math
import torch.nn.functional as F
class CustomAttentConv(torch.nn.Module):
def __init__(self, in_channels, channels, kernel=4, stride=2, pad=0,
pad_type='zero', use_bias=True, sn=False):
... | 9,312 | 37.168033 | 94 | py |
MSAU | MSAU-master/inference/kv_model.py | import json
import torch
import cv2
import os
import numpy as np
from .morph_util import r_closing, area, intersect_boxes, union_boxes, r_dilation, \
connected_components, xcenter, ycenter, max_dim, filter_overlap_boxes, filter_overlap_boxes_bigger, scale_rect
from .generic_util import sort_box_reading_order, to_... | 15,207 | 38.195876 | 120 | py |
RGB-N | RGB-N-master/lib/roi_data_layer/minibatch.py | # --------------------------------------------------------
# Fast R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Peng Zhou
# --------------------------------------------------------
"""Compute minibatch blobs for training a Fast R-CNN netw... | 3,878 | 35.252336 | 115 | py |
RGB-N | RGB-N-master/lib/nets/network.py | # --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import ... | 18,601 | 45.044554 | 124 | py |
RGB-N | RGB-N-master/lib/nets/network_fusion.py | # --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import ... | 22,237 | 46.214437 | 124 | py |
RGB-N | RGB-N-master/lib/nets/network_noise.py | # --------------------------------------------------------
# Tensorflow Faster R-CNN
# Licensed under The MIT License [see LICENSE for details]
# Written by Peng Zhou
# --------------------------------------------------------
from __future__ import absolute_import
from __future__ import division
from __future__ import ... | 19,890 | 44.62156 | 124 | py |
RGB-N | RGB-N-master/lib/model/config.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import os.path as osp
import numpy as np
# `pip install easydict` if you don't have it
from easydict import EasyDict as edict
__C = edict()
# Consumers can get config by:
# from fast_rcnn_config im... | 11,267 | 29.209115 | 91 | py |
nat-acl2020 | nat-acl2020-master/main.py | from torch.optim.sgd import SGD
import os.path
import sys, csv, random, logging
import numpy as np
FIXED_RANDOM_SEEDS = False
if FIXED_RANDOM_SEEDS:
random.seed(0)
np.random.seed(0)
EXIT_SUCCESS=0
EXIT_FAILURE=-1
def evaluate(model_path, corpus, mini_batch_size=256, misspelling_rate=0.0,
cmx_f... | 22,210 | 44.144309 | 133 | py |
nat-acl2020 | nat-acl2020-master/robust_ner/embeddings.py | import logging
import torch
from typing import List
from flair.data import Sentence
log = logging.getLogger("flair")
def check_embeddings(sentList1: List[Sentence], sentList2: List[Sentence], embed1: torch.tensor, embed2: torch.tensor):
"""
Checks embeddings of the original and perturbed sentences.
Retu... | 1,019 | 33 | 119 | py |
nat-acl2020 | nat-acl2020-master/flair_ext/nn.py | import warnings
from pathlib import Path
import torch.nn
from abc import abstractmethod
from typing import Union, List
import flair
from flair.data import Sentence
from flair.training_utils import Result
from flair.nn import Model
class ParameterizedModel(Model):
"""Abstract base class for all downstream task... | 711 | 28.666667 | 119 | py |
nat-acl2020 | nat-acl2020-master/flair_ext/models/nat_sequence_tagger_model.py | import logging
import sys
import numpy as np
from pathlib import Path
import torch.nn
import torch.nn.functional as F
from torch.utils.data.dataset import Dataset
import flair.nn
import torch
import flair.embeddings
from flair.data import Dictionary, Sentence, Token, Label
from flair.datasets import DataLoader
fro... | 21,916 | 39.362799 | 133 | py |
nat-acl2020 | nat-acl2020-master/flair_ext/trainers/trainer.py | from pathlib import Path
from typing import List, Union
import datetime
from torch.optim.sgd import SGD
from torch.utils.data.dataset import ConcatDataset
import flair
import flair.nn
from flair.data import Sentence, MultiCorpus, Corpus
from flair.datasets import DataLoader
from flair.training_utils import (
ini... | 22,609 | 39.30303 | 173 | py |
Beholder-GAN | Beholder-GAN-master/beauty_prediction/execute_beauty_prediction.py | from __future__ import print_function, division
import argparse
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torchvision import transforms, models
from torch.autograd import Variable
import os
import numpy as np
from PIL import Image
import csv
parser = argparse.ArgumentParser()
parser.... | 3,738 | 35.300971 | 142 | py |
Beholder-GAN | Beholder-GAN-master/beauty_prediction/train_beauty_prediction.py | from __future__ import print_function, division
import argparse
import os
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torchvision import transforms, models
from torch.autograd import Variable
import time
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot... | 8,255 | 35.052402 | 127 | py |
Beholder-GAN | Beholder-GAN-master/beauty_prediction/execute_beauty_prediction_single.py | from __future__ import print_function, division
import argparse
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torchvision import transforms, models
from torch.autograd import Variable
import os
import numpy as np
from PIL import Image
import csv
parser = argparse.ArgumentParser()
parser.... | 2,908 | 34.47561 | 142 | py |
Beholder-GAN | Beholder-GAN-master/beauty_prediction/faces_dataset.py | import csv
import numpy as np
import torch
from torch.utils.data.dataset import Dataset
from PIL import Image
import matplotlib.pyplot as plt
##### Dataset for Face images with beauty rates #####
# Each entry will contain: #
# Face image #
# L... | 2,953 | 35.02439 | 100 | py |
OLD3S | OLD3S-main/model/loaddatasets.py | import numpy as np
import pandas as pd
import torch
import torchvision
from sklearn import preprocessing
from torchvision.transforms import transforms
from sklearn.utils import shuffle
Newfeature = transforms.Compose([
transforms.RandomHorizontalFlip(p=0.5),
transforms.ColorJitter(hue=0.3),
tor... | 5,243 | 33.051948 | 92 | py |
OLD3S | OLD3S-main/model/model_vae.py | import torch
import torch.nn as nn
import math
import copy
from torch.nn.parameter import Parameter
from cnn import Dynamic_ResNet18
from autoencoder import *
from loaddatasets import *
from mlp import MLP
from vae import *
from test import DCVAE
def normal(t):
mean, std, var = torch.mean(t), torch.std(t), torch.v... | 45,436 | 40.761949 | 118 | py |
OLD3S | OLD3S-main/model/cnn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class BasicBlock(nn.Module):
EXPANSION = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(
in_planes, planes, ker... | 6,471 | 37.070588 | 104 | py |
OLD3S | OLD3S-main/model/mlp.py | import torch
from torch import nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, planes):
super(BasicBlock, self).__init__()
self.Linear1 = nn.Linear(
in_planes, planes)
self.relu = nn.ReLU()
def forward(self, x):
out = ... | 2,956 | 29.484536 | 84 | py |
OLD3S | OLD3S-main/model/model.py | import torch
import torch.nn as nn
import math
import copy
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from cnn import Dynamic_ResNet18
from autoencoder import *
from mlp import MLP
def normal(t):
mean, std, var = torch.mean(t), torch.std(t), torch.var(t)
t = (t - mean) / std
... | 42,413 | 40.258755 | 118 | py |
OLD3S | OLD3S-main/model/vae.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from loaddatasets import *
from torch import nn
from torch import optim
import torch.nn.functional as F
import torchvision
from torchvision.transforms import transforms
from torch.utils.data import DataLoader, Dataset
import os
import random
import matp... | 6,408 | 25.593361 | 60 | py |
OLD3S | OLD3S-main/model/autoencoder.py | import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module): # BasicBlock from ResNet [He et al.2016]
EXPANSION = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(
in_planes, planes, kernel_size=3,... | 4,793 | 35.318182 | 113 | py |
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