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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DB | DB-master/training/optimizer_scheduler.py | import torch
from concern.config import Configurable, State
class OptimizerScheduler(Configurable):
optimizer = State()
optimizer_args = State(default={})
learning_rate = State(autoload=False)
def __init__(self, cmd={}, **kwargs):
self.load_all(**kwargs)
self.load('learning_rate', cm... | 691 | 29.086957 | 57 | py |
DB | DB-master/training/model_saver.py | import os
import torch
from concern.config import Configurable, State
from concern.signal_monitor import SignalMonitor
class ModelSaver(Configurable):
dir_path = State()
save_interval = State(default=1000)
signal_path = State()
def __init__(self, **kwargs):
self.load_all(**kwargs)
... | 1,525 | 32.911111 | 83 | py |
DB | DB-master/training/learning_rate.py | from bisect import bisect_right
import numpy as np
import torch.optim.lr_scheduler as lr_scheduler
from concern.config import Configurable, State
from concern.signal_monitor import SignalMonitor
class ConstantLearningRate(Configurable):
lr = State(default=0.0001)
def __init__(self, **kwargs):
self.l... | 3,561 | 27.496 | 75 | py |
DB | DB-master/structure/model.py | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import backbones
import decoders
class BasicModel(nn.Module):
def __init__(self, args):
nn.Module.__init__(self)
self.backbone = getattr(backbones, args['backbone'])(**args.get('backbone_args', {}))
self.decode... | 2,251 | 33.121212 | 98 | py |
DB | DB-master/structure/builder.py | from collections import OrderedDict
import torch
import structure.model
from concern.config import Configurable, State
class Builder(Configurable):
model = State()
model_args = State()
def __init__(self, cmd={}, **kwargs):
self.load_all(**kwargs)
if 'backbone' in cmd:
self.m... | 761 | 26.214286 | 98 | py |
DB | DB-master/structure/visualizers/seg_detector_visualizer.py | import cv2
import concern.webcv2 as webcv2
import numpy as np
import torch
from concern.config import Configurable, State
from data.processes.make_icdar_data import MakeICDARData
class SegDetectorVisualizer(Configurable):
vis_num = State(default=4)
eager_show = State(default=False)
def __init__(self, **... | 4,177 | 38.790476 | 99 | py |
DB | DB-master/concern/visualizer.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# File : visualizer.py
# Author : Zhaoyi Wan <wanzhaoyi@megvii.com>
# Date : 08.01.2019
# Last Modified Date: 02.12.2019
# Last Modified By : Minghui Liao
import torch
import numpy as np
import cv2
class Visualize:
@classmethod
... | 3,623 | 35.606061 | 153 | py |
DB | DB-master/decoders/dice_loss.py | import torch
import torch.nn as nn
import numpy as np
import cv2
from scipy import ndimage
class DiceLoss(nn.Module):
'''
Loss function from https://arxiv.org/abs/1707.03237,
where iou computation is introduced heatmap manner to measure the
diversity bwtween tow heatmaps.
'''
def __init__(self... | 6,965 | 36.251337 | 114 | py |
DB | DB-master/decoders/seg_detector_loss.py | import sys
import torch
import torch.nn as nn
class SegDetectorLossBuilder():
'''
Build loss functions for SegDetector.
Details about the built functions:
Input:
pred: A dict which contains predictions.
thresh: The threshold prediction
binary: The text ... | 9,529 | 34.962264 | 104 | py |
DB | DB-master/decoders/l1_loss.py | import torch
import torch.nn as nn
class MaskL1Loss(nn.Module):
def __init__(self):
super(MaskL1Loss, self).__init__()
def forward(self, pred: torch.Tensor, gt, mask):
mask_sum = mask.sum()
if mask_sum.item() == 0:
return mask_sum, dict(l1_loss=mask_sum)
else:
... | 1,363 | 31.47619 | 72 | py |
DB | DB-master/decoders/balance_cross_entropy_loss.py | import torch
import torch.nn as nn
class BalanceCrossEntropyLoss(nn.Module):
'''
Balanced cross entropy loss.
Shape:
- Input: :math:`(N, 1, H, W)`
- GT: :math:`(N, 1, H, W)`, same shape as the input
- Mask: :math:`(N, H, W)`, same spatial shape as the input
- Output: scalar... | 1,954 | 33.298246 | 78 | py |
DB | DB-master/decoders/simple_detection.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from backbones.upsample_head import SimpleUpsampleHead
class SimpleDetectionDecoder(nn.Module):
def __init__(self, feature_channel=256):
nn.Module.__init__(self)
self.feature_channel = feature_channel
self.head_layer = s... | 6,383 | 32.25 | 107 | py |
DB | DB-master/decoders/feature_attention.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class ScaleChannelAttention(nn.Module):
def __init__(self, in_planes, out_planes, num_features, init_weight=True):
super(ScaleChannelAttention, self).__init__()
self.avgpool = nn.AdaptiveAvgPool2d(1)
print(self.avgpool)
... | 5,925 | 39.868966 | 121 | py |
DB | DB-master/decoders/seg_detector.py | from collections import OrderedDict
import torch
import torch.nn as nn
BatchNorm2d = nn.BatchNorm2d
class SegDetector(nn.Module):
def __init__(self,
in_channels=[64, 128, 256, 512],
inner_channels=256, k=10,
bias=False, adaptive=False, smooth=False, serial=False,... | 6,112 | 38.954248 | 98 | py |
DB | DB-master/decoders/pss_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class PSS_Loss(nn.Module):
def __init__(self, cls_loss):
super(PSS_Loss, self).__init__()
self.eps = 1e-6
self.criterion = eval('self.' + cls_loss + '_loss')
def dice_loss(self, pred, gt, m):
intersection = tor... | 4,467 | 37.517241 | 84 | py |
DB | DB-master/decoders/seg_detector_asf.py | from collections import OrderedDict
import pdb
import torch
import torch.nn as nn
from .feature_attention import ScaleFeatureSelection
BatchNorm2d = nn.BatchNorm2d
class SegSpatialScaleDetector(nn.Module):
def __init__(self,
in_channels=[64, 128, 256, 512],
inner_channels=256, k=... | 7,048 | 42.245399 | 123 | py |
DB | DB-master/data/quad.py | import torch
import numpy as np
class Quad:
def __init__(self, points, format='NP2'):
self._rect = None
self.tensorized = False
self._points = None
self.set_points(points, format)
@property
def points(self):
return self._points
def set_points(self, new_points,... | 2,539 | 28.195402 | 73 | py |
DB | DB-master/data/data_loader.py | import math
import bisect
import imgaug
import numpy as np
import torch
import torch.distributed as dist
from torch.utils.data import Sampler, ConcatDataset, BatchSampler
from concern.config import Configurable, State
def default_worker_init_fn(worker_id):
np.random.seed(worker_id)
imgaug.seed(worker_id)
... | 8,321 | 32.155378 | 98 | py |
DB | DB-master/data/simple_detection.py | import pickle
import cv2
import skimage
import numpy as np
from shapely.geometry import Polygon
from concern.config import Configurable, State
def binary_search_smallest_width(poly):
if len(poly) < 3:
return 0
poly = Polygon(poly)
low = 0
high = 65536
while high - low > 0.1:
mid ... | 9,427 | 34.443609 | 120 | py |
DB | DB-master/data/image_dataset.py | import functools
import logging
import bisect
import torch.utils.data as data
import cv2
import numpy as np
import glob
from concern.config import Configurable, State
import math
class ImageDataset(data.Dataset, Configurable):
r'''Dataset reading from images.
Args:
Processes: A series of Callable obje... | 3,935 | 37.970297 | 122 | py |
DB | DB-master/data/dataset.py | from torch.utils.data import Dataset as TorchDataset
from concern.config import Configurable, State
class SliceDataset(TorchDataset, Configurable):
dataset = State()
start = State()
end = State()
def __init__(self, **kwargs):
self.load_all(**kwargs)
if self.start is None:
... | 547 | 21.833333 | 52 | py |
DB | DB-master/data/transform_data.py | import numpy as np
import torch
from concern.config import Configurable
class TransformData(Configurable):
'''
this transformation is inplcae, which means that the input
will be modified.
'''
mean = np.array([0.485, 0.456, 0.406])
std = np.array([0.229, 0.224, 0.225])
def __init__(se... | 641 | 25.75 | 76 | py |
DB | DB-master/data/processes/normalize_image.py | import numpy as np
import torch
from .data_process import DataProcess
class NormalizeImage(DataProcess):
RGB_MEAN = np.array([122.67891434, 116.66876762, 104.00698793])
def process(self, data):
assert 'image' in data, '`image` in data is required by this process'
image = data['image']
... | 707 | 26.230769 | 77 | py |
DB | DB-master/data/processes/make_icdar_data.py | from collections import OrderedDict
import torch
import numpy as np
from concern.config import Configurable, State
from .data_process import DataProcess
import cv2
class MakeICDARData(DataProcess):
shrink_ratio = State(default=0.4)
def __init__(self, debug=False, cmd={}, **kwargs):
self.load_all(**... | 2,284 | 31.642857 | 72 | py |
DB | DB-master/backbones/resnet.py | import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
BatchNorm2d = nn.BatchNorm2d
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://d... | 11,842 | 34.142433 | 82 | py |
DB | DB-master/backbones/mobilenetv3.py | # https://github.com/kuan-wang/pytorch-mobilenet-v3
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['MobileNetV3', 'mobilenetv3']
def conv_bn(inp, oup, stride, conv_layer=nn.Conv2d, norm_layer=nn.BatchNorm2d, nlin_layer=nn.ReLU):
return nn.Sequential(
conv_layer(inp, oup, 3... | 8,930 | 34.300395 | 198 | py |
MultiScanner_SCC | MultiScanner_SCC-main/inference.py | from slide.slide_helper import *
from slide.process_slides import *
import hydra
from omegaconf import DictConfig
from torchvision import models
from torchvision import transforms
from einops import rearrange
from torchmetrics import ConfusionMatrix
import os
from fastai.vision import *
def stitch_output_mask(filenam... | 4,863 | 48.131313 | 177 | py |
MultiScanner_SCC | MultiScanner_SCC-main/training.py | from slide.process_slides import *
import hydra
from omegaconf import DictConfig
from torchvision import models
from utils.combo_loss import ComboLoss
from utils.callbacks import ResetDataloaders, IoU, LossComponents
from slide.slide_helper import *
def random_seed(seed_value, use_cuda):
'''
Sets the random seed ... | 2,681 | 40.90625 | 206 | py |
MultiScanner_SCC | MultiScanner_SCC-main/slide/slide_helper.py | from fastai.vision.all import *
from matplotlib import cm
from matplotlib.colors import ListedColormap
from einops import rearrange, reduce, repeat
from collections import defaultdict
COLORS = np.array([[128, 128, 128], # Excluded
[255, 255, 255], # BG
[0, 0, 255], # Normal
[255, 128, ... | 2,972 | 48.55 | 161 | py |
MultiScanner_SCC | MultiScanner_SCC-main/utils/callbacks.py | import torch
from fastai.metrics import Metric, AvgMetric
from fastai.callback.core import Callback
from slide.slide_helper import generate_dataloaders
from torchmetrics import JaccardIndex
class IoU(AvgMetric):
def __init__(self):
super().__init__(func=JaccardIndex(num_classes=4, ignore_index=0))
... | 1,725 | 34.22449 | 129 | py |
MultiScanner_SCC | MultiScanner_SCC-main/utils/combo_loss.py | import torch
from torch import nn
import torch.nn.functional as F
class CELoss(nn.modules.loss._WeightedLoss):
def __init__(self, weight=None, gamma=2,reduction='mean', ignore_index=-1):
super(CELoss, self).__init__(weight,reduction=reduction)
self.gamma = gamma
self.ignore_index = ignore_... | 3,008 | 36.6125 | 118 | py |
ELLE | ELLE-main/fairseq-0.9.0/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-0.9.0/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-0.9.0/hubconf.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 functools
from fairseq.hub_utils import BPEHubInterface as bpe # noqa
from fairseq.hub_utils import TokenizerHubInterface as tokenize... | 1,432 | 28.244898 | 78 | py |
ELLE | ELLE-main/fairseq-0.9.0/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 torch
from fairseq import checkpoint_utils, options, progress_bar, utils
def mai... | 3,163 | 30.64 | 88 | py |
ELLE | ELLE-main/fairseq-0.9.0/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-0.9.0/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-0.9.0/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 numpy... | 12,771 | 36.786982 | 92 | py |
ELLE | ELLE-main/fairseq-0.9.0/examples/roberta/double_height_width_stack_addnoise.py | import collections
import sys
import torch.nn as nn
import torch
import math
def main():
ckpt = torch.load(sys.argv[1])
whether_stack = False
enlarge_n_times = 2
emb_num = ckpt['model']['decoder.sentence_encoder.embed_tokens.weight'].size()[0]
height = 6
width = ckpt['model']['decoder.sentence... | 10,292 | 52.056701 | 147 | py |
ELLE | ELLE-main/fairseq-0.9.0/examples/roberta/bert2BERT_FPI_new.py | """
preprocessing script before training distillBert
specific to bert->distillbert
"""
import argparse
import os
import math
from typing import NewType, NoReturn
import torch
import numpy as np
from transformer.modeling import BertForPreTraining
def wider3d(w,dim,new_width,choices,div=False):
old_width = w.size(dim)... | 11,647 | 37.190164 | 136 | py |
ELLE | ELLE-main/fairseq-0.9.0/examples/roberta/double_enlarge_general.py | import collections
import sys
import torch.nn as nn
import torch
import math
def main():
print(sys.argv[1])
ckpt = torch.load(sys.argv[1])
width_enlarge = True
layer_enlarge = True
enlarge_layer_num = 6
enlarge_dim = 384
attention_head = 12
emb_num = ckpt['model']['decoder.sentence_enco... | 10,371 | 55.369565 | 272 | py |
ELLE | ELLE-main/fairseq-0.9.0/examples/roberta/bert2BERT_AKI_new.py | """
preprocessing script before training distillBert
specific to bert->distillbert
"""
'''
sm path: dir contain pytorch_model.bin, config.json, vocab.txt of small model
bm path: config.json, vocab.txt of big model
-'is_always_left': Taking the parameters of all the left neurons is also a way of randomly selecting neur... | 14,500 | 38.620219 | 219 | py |
ELLE | ELLE-main/fairseq-0.9.0/examples/roberta/commonsense_qa/commonsense_qa_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 json
import os
import numpy as np
import torch
from fairseq.data import (
data_utils,
Dictionary,
encoders,
IdDataset... | 5,921 | 32.84 | 103 | py |
ELLE | ELLE-main/fairseq-0.9.0/examples/roberta/wsc/wsc_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 json
import os
import tempfile
import numpy as np
import torch
import torch.nn.functional as F
from fairseq import utils
from fairseq... | 13,149 | 33.973404 | 103 | py |
ELLE | ELLE-main/fairseq-0.9.0/examples/roberta/wsc/wsc_criterion.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 torch
import torch.nn.functional as F
from fairseq import utils
from fairseq.data import encoders
from fairseq.criterions... | 6,022 | 35.065868 | 88 | py |
ELLE | ELLE-main/fairseq-0.9.0/scripts/average_checkpoints.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 argparse
import collections
import torch
import os
import re
def average_checkpoints(inputs):
"""Loads che... | 5,292 | 36.539007 | 134 | py |
ELLE | ELLE-main/fairseq-0.9.0/scripts/wav2vec_featurize.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.
"""
Helper script to pre-compute embeddings for a wav2letter++ dataset
"""
import argparse
import glob
import os
from ... | 7,102 | 28.970464 | 135 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_train.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
from io import StringIO
import unittest
from unittest.mock import MagicMock, patch
import torch
from fairseq import data, ... | 4,691 | 35.092308 | 94 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_average_checkpoints.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 collections
import os
import tempfile
import unittest
import shutil
import numpy as np
import torch
from torch import nn
from script... | 4,494 | 30.215278 | 80 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_sequence_scorer.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 argparse
import unittest
import torch
from fairseq.sequence_scorer import SequenceScorer
import tests.utils as test_utils
class Te... | 3,949 | 33.051724 | 75 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_memory_efficient_fp16.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 argparse
import unittest
import torch
from fairseq.optim.adam import FairseqAdam
from fairseq.optim.fp16_optimizer import MemoryEffic... | 1,787 | 28.311475 | 69 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_multihead_attention.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 unittest
from fairseq.modules.multihead_attention import MultiheadAttention
class TestMultiheadAttention(unittest.TestCa... | 1,904 | 30.229508 | 80 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/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 argparse
import torch
from fairseq import utils
from fairseq.data import Dictionary
from fairseq.data.language_pair_dataset import col... | 7,442 | 30.67234 | 101 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_binaries.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
from io import StringIO
import os
import random
import sys
import tempfile
import unittest
import torch
from fairseq impor... | 31,257 | 40.183136 | 115 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_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 unittest
import torch
from fairseq.data import LanguagePairDataset, TokenBlockDataset
from fairseq.data.concat_dataset import ConcatDa... | 1,943 | 28.907692 | 66 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_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 unittest
from typing import Dict, List
import tests.utils as test_utils
import torch
from fairseq import utils
from fairseq.data impor... | 19,779 | 36.533207 | 87 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_sparse_multihead_attention.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 unittest
from fairseq.modules.sparse_multihead_attention import SparseMultiheadAttention
class TestSparseMultiheadAttent... | 2,545 | 50.959184 | 114 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_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 unittest
import torch
from fairseq.data import (
BacktranslationDataset,
LanguagePairDataset,
TransformEosDataset,
)
from... | 4,032 | 33.470085 | 90 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_sequence_generator.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 argparse
import unittest
import torch
from fairseq.sequence_generator import SequenceGenerator
import tests.utils as test_utils
cl... | 14,876 | 38.884718 | 96 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_label_smoothing.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 argparse
import copy
import unittest
import torch
from fairseq.criterions.cross_entropy import CrossEntropyCriterion
from fairseq.cri... | 4,139 | 40.4 | 101 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_convtbc.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 unittest
from fairseq.modules import ConvTBC
import torch.nn as nn
class TestConvTBC(unittest.TestCase):
def test_c... | 1,679 | 33.285714 | 102 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_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 unittest
import torch
from fairseq.data import TokenBlockDataset
import tests.utils as test_utils
class TestTokenBlockDataset(unit... | 2,970 | 36.607595 | 89 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_multi_corpus_sampled_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 unittest
from collections import OrderedDict
import numpy as np
import torch
from fairseq.data import LanguagePairDataset, TokenBlockD... | 3,105 | 31.354167 | 79 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_bmuf.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 argparse
from multiprocessing import Manager
import random
import unittest
import torch
import torch.nn as nn
from fairseq import dis... | 4,554 | 28.198718 | 88 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_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.
import tempfile
import unittest
import torch
from fairseq.data import Dictionary
class TestDictionary(unittest.TestCase):
def test_fi... | 1,863 | 25.253521 | 80 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_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 unittest
import torch
from fairseq import utils
class TestUtils(unittest.TestCase):
def test_convert_padding_direction(self):
... | 2,131 | 24.380952 | 65 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/test_character_token_embedder.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 unittest
from fairseq.data import Dictionary
from fairseq.modules import CharacterTokenEmbedder
class TestCharacterToke... | 1,656 | 34.255319 | 96 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/speech_recognition/asr_test_base.py | #!/usr/bin/env python3
import argparse
import os
import unittest
from inspect import currentframe, getframeinfo
import numpy as np
import torch
from fairseq.data import data_utils as fairseq_data_utils
from fairseq.data.dictionary import Dictionary
from fairseq.models import (
BaseFairseqModel,
FairseqDecoder... | 19,247 | 33.80651 | 87 | py |
ELLE | ELLE-main/fairseq-0.9.0/tests/speech_recognition/test_collaters.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 unittest
import numpy as np
import torch
from examples.speech_recognition.data.collaters import Seq2SeqCollater... | 2,048 | 33.728814 | 87 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/checkpoint_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 collections
import logging
import os
import re
import shutil
import traceback
from collections import OrderedDict
from typing import Un... | 17,850 | 35.655031 | 116 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/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.
from collections import defaultdict
import contextlib
import copy
import importlib.util
import math
import os
import sys
from typing import Ca... | 13,882 | 31.665882 | 111 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/hub_utils.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.
import argparse
import copy
import os
import torch
from torch import nn
from fairseq import utils
from fairseq.dat... | 8,171 | 33.627119 | 117 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/sequence_scorer.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 sys
from fairseq import utils
class SequenceScorer(object):
"""Scores the target for a given source sentence."""
... | 4,508 | 36.575 | 107 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/distributed_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 os
import pickle
import socket
import subprocess
import warnings
import torch
import torch.distributed as dist
from fairseq import ut... | 6,952 | 35.984043 | 97 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/sequence_generator.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 torch
from fairseq import search, utils
from fairseq.data import data_utils
from fairseq.models import FairseqIncremental... | 30,353 | 41.512605 | 118 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/legacy_distributed_data_parallel.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.
"""
A modified version of the legacy DistributedDataParallel module that uses c10d
communication primitives. This version is simpler than the ... | 6,724 | 36.154696 | 88 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/options.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 argparse
import torch
import sys
from fairseq import utils
from fairseq.data.indexed_dataset import get_available_dataset_impl
def ... | 28,856 | 51.755027 | 120 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/bleu.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 ctypes
import math
import torch
try:
from fairseq import libbleu
except ImportError as e:
import sys
sys.stderr.write('ERR... | 3,955 | 29.430769 | 83 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/file_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.
"""
Utilities for working with the local dataset cache.
This file is adapted from `AllenNLP <https://github.com/allenai/allennlp>`_.
and `hugg... | 10,466 | 31.811912 | 98 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/search.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 torch
class Search(object):
def __init__(self, tgt_dict):
self.pad = tgt_dict.pad()
self.unk = tgt_... | 11,223 | 36.918919 | 104 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/iterative_refinement_generator.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 namedtuple
import torch
from fairseq import utils
DecoderOut = namedtuple('IterativeRefinementDecoderOut', [
'... | 9,288 | 36.007968 | 117 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/trainer.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.
"""
Train a network across multiple GPUs.
"""
import contextlib
import math
import os
import sys
from collections import OrderedDict
from ite... | 25,419 | 37.225564 | 93 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/transformer_sentence_encoder_layer.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 torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.modules import (
LayerNorm,
... | 2,952 | 30.414894 | 80 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/learned_positional_embedding.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.nn as nn
from fairseq import utils
class LearnedPositionalEmbedding(nn.Embedding):
"""
This module learns positional e... | 1,881 | 35.901961 | 94 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/sparse_multihead_attention.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 torch
from .multihead_attention import MultiheadAttention
class SparseMultiheadAttention(MultiheadAttention):
""" Spa... | 4,525 | 42.104762 | 100 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/multihead_attention.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 torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
from fairseq import utils
clas... | 15,904 | 42.220109 | 116 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/highway.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 torch import nn
class Highway(torch.nn.Module):
"""
A `Highway layer <https://arxiv.org/abs/1505.00387>`_.
Ad... | 1,745 | 31.943396 | 97 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/linearized_convolution.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 torch.nn.functional as F
from fairseq import utils
from .conv_tbc import ConvTBC
class LinearizedConvolution(ConvTBC):... | 3,598 | 39.897727 | 95 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/downsampled_multihead_attention.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 torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.modules.scalar_bias import scalar_bias
class ... | 9,815 | 37.194553 | 106 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/gelu.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.
"""
See "Gaussian Error Linear Units (GELUs)" by Dan Hendrycks and Kevin Gimpel with
the corresponding GitHub repo: https://github.com/hendryck... | 790 | 29.423077 | 90 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/positional_embedding.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.nn as nn
from .learned_positional_embedding import LearnedPositionalEmbedding
from .sinusoidal_positional_embedding import Sinus... | 1,287 | 36.882353 | 83 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/adaptive_input.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 torch import nn
from typing import List
class AdaptiveInput(nn.Module):
def __init__(
self,
vocab_s... | 2,283 | 30.287671 | 80 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/vggblock.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 __future__ import absolute_import, division, print_function, unicode_literals
from collections.abc import Iterable
from itertools import... | 4,057 | 33.683761 | 88 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/character_token_embedder.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 torch.nn.functional as F
from torch import nn
from typing import List, Tuple
from .highway import Highway
from fairseq.... | 5,298 | 32.327044 | 106 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/unfold.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.nn.functional as F
def unfold1d(x, kernel_size, padding_l, pad_value=0):
'''unfold T x B x C to T x B x C x K'''
if ker... | 570 | 30.722222 | 91 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/adaptive_softmax.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 operator
import functools
import torch
import torch.nn.functional as F
from torch import nn
class TiedLinear(nn.Module):
def __i... | 7,207 | 33.821256 | 112 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/conv_tbc.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 torch.nn.modules.utils import _single
class ConvTBC(torch.nn.Module):
"""1D convolution over an input of shape (time x... | 1,356 | 35.675676 | 90 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/transformer_layer.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 torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.modules import LayerNorm, MultiheadA... | 13,496 | 42.259615 | 147 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/mean_pool_gating_network.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 torch.nn.functional as F
class MeanPoolGatingNetwork(torch.nn.Module):
"""A simple mean-pooling gating network for s... | 2,007 | 38.372549 | 84 | py |
ELLE | ELLE-main/fairseq-0.9.0/fairseq/modules/logsumexp_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
class LogSumExpMoE(torch.autograd.Function):
"""Standard LogSumExp forward pass, but use *posterior* for the backward.
... | 835 | 29.962963 | 78 | py |
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