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
value |
|---|---|---|---|---|---|---|
gunpowder | gunpowder-master/docs/build/conf.py | # -*- coding: utf-8 -*-
#
# gunpowder documentation build configuration file, created by
# sphinx-quickstart on Fri Jun 30 12:59:21 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
#... | 5,592 | 30.24581 | 245 | py |
gunpowder | gunpowder-master/docs/build/_themes/sphinx_rtd_theme/__init__.py | """Sphinx ReadTheDocs theme.
From https://github.com/ryan-roemer/sphinx-bootstrap-theme.
"""
from os import path
__version__ = '0.2.5b2'
__version_full__ = __version__
def get_html_theme_path():
"""Return list of HTML theme paths."""
cur_dir = path.abspath(path.dirname(path.dirname(__file__)))
return c... | 522 | 25.15 | 96 | py |
treelstm.pytorch | treelstm.pytorch-master/main.py | from __future__ import division
from __future__ import print_function
import os
import random
import logging
import torch
import torch.nn as nn
import torch.optim as optim
# IMPORT CONSTANTS
from treelstm import Constants
# NEURAL NETWORK MODULES/LAYERS
from treelstm import SimilarityTreeLSTM
# DATA HANDLING CLASSES... | 7,610 | 39.484043 | 99 | py |
treelstm.pytorch | treelstm.pytorch-master/config.py | import argparse
def parse_args():
parser = argparse.ArgumentParser(
description='PyTorch TreeLSTM for Sentence Similarity on Dependency Trees')
# data arguments
parser.add_argument('--data', default='data/sick/',
help='path to dataset')
parser.add_argument('--glove', de... | 2,532 | 48.666667 | 83 | py |
treelstm.pytorch | treelstm.pytorch-master/scripts/download.py | """
Downloads the following:
- Stanford parser
- Stanford POS tagger
- Glove vectors
- SICK dataset (semantic relatedness task)
"""
from __future__ import print_function
import urllib2
import sys
import os
import zipfile
def download(url, dirpath):
filename = url.split('/')[-1]
filepath = os.path.join(dirpat... | 3,938 | 28.177778 | 91 | py |
treelstm.pytorch | treelstm.pytorch-master/scripts/preprocess-sick.py | """
Preprocessing script for SICK data.
"""
import os
import glob
def make_dirs(dirs):
for d in dirs:
if not os.path.exists(d):
os.makedirs(d)
def dependency_parse(filepath, cp='', tokenize=True):
print('\nDependency parsing ' + filepath)
dirpath = os.path.dirname(filepath)
fil... | 3,975 | 34.185841 | 87 | py |
treelstm.pytorch | treelstm.pytorch-master/treelstm/tree.py | # tree object from stanfordnlp/treelstm
class Tree(object):
def __init__(self):
self.parent = None
self.num_children = 0
self.children = list()
def add_child(self, child):
child.parent = self
self.num_children += 1
self.children.append(child)
def size(self):... | 946 | 26.852941 | 54 | py |
treelstm.pytorch | treelstm.pytorch-master/treelstm/Constants.py | PAD = 0
UNK = 1
BOS = 2
EOS = 3
PAD_WORD = '<blank>'
UNK_WORD = '<unk>'
BOS_WORD = '<s>'
EOS_WORD = '</s>'
| 108 | 9.9 | 20 | py |
treelstm.pytorch | treelstm.pytorch-master/treelstm/utils.py | from __future__ import division
from __future__ import print_function
import os
import math
import torch
from .vocab import Vocab
# loading GLOVE word vectors
# if .pth file is found, will load that
# else will load from .txt file & save
def load_word_vectors(path):
if os.path.isfile(path + '.pth') and os.path... | 2,376 | 32.957143 | 89 | py |
treelstm.pytorch | treelstm.pytorch-master/treelstm/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from . import Constants
# module for childsumtreelstm
class ChildSumTreeLSTM(nn.Module):
def __init__(self, in_dim, mem_dim):
super(ChildSumTreeLSTM, self).__init__()
self.in_dim = in_dim
self.mem_dim = mem_dim
sel... | 3,286 | 36.352273 | 95 | py |
treelstm.pytorch | treelstm.pytorch-master/treelstm/dataset.py | import os
from tqdm import tqdm
from copy import deepcopy
import torch
import torch.utils.data as data
from . import Constants
from .tree import Tree
# Dataset class for SICK dataset
class SICKDataset(data.Dataset):
def __init__(self, path, vocab, num_classes):
super(SICKDataset, self).__init__()
... | 2,927 | 32.655172 | 82 | py |
treelstm.pytorch | treelstm.pytorch-master/treelstm/vocab.py | # vocab object from harvardnlp/opennmt-py
class Vocab(object):
def __init__(self, filename=None, data=None, lower=False):
self.idxToLabel = {}
self.labelToIdx = {}
self.lower = lower
# Special entries will not be pruned.
self.special = []
if data is not None:
... | 2,537 | 28.172414 | 81 | py |
treelstm.pytorch | treelstm.pytorch-master/treelstm/metrics.py | from copy import deepcopy
import torch
class Metrics():
def __init__(self, num_classes):
self.num_classes = num_classes
def pearson(self, predictions, labels):
x = deepcopy(predictions)
y = deepcopy(labels)
x = (x - x.mean()) / x.std()
y = (y - y.mean()) / y.std()
... | 504 | 23.047619 | 43 | py |
treelstm.pytorch | treelstm.pytorch-master/treelstm/__init__.py | from . import Constants
from .dataset import SICKDataset
from .metrics import Metrics
from .model import SimilarityTreeLSTM
from .trainer import Trainer
from .tree import Tree
from . import utils
from .vocab import Vocab
__all__ = [Constants, SICKDataset, Metrics, SimilarityTreeLSTM, Trainer, Tree, Vocab, utils]
| 315 | 27.727273 | 92 | py |
treelstm.pytorch | treelstm.pytorch-master/treelstm/trainer.py | from tqdm import tqdm
import torch
from . import utils
class Trainer(object):
def __init__(self, args, model, criterion, optimizer, device):
super(Trainer, self).__init__()
self.args = args
self.model = model
self.criterion = criterion
self.optimizer = optimizer
s... | 2,384 | 40.842105 | 96 | py |
FastVae_Gpu | FastVae_Gpu-main/run_mm.py | from dataloader import RecData, UserItemData
from sampler_gpu_mm import SamplerBase, PopularSampler, MidxUniform, MidxUniPop
import torch
import torch.optim
from torch.optim.lr_scheduler import StepLR
from torch.utils.data import DataLoader
from vae_models import BaseVAE, VAE_Sampler
import argparse
import numpy as np
... | 10,590 | 45.862832 | 172 | py |
FastVae_Gpu | FastVae_Gpu-main/vae_models.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import time
class BaseVAE(nn.Module):
def __init__(self, num_item, dims, active='relu', dropout=0.5):
"""
dims is a list for latent dims
"""
super(BaseVAE, self).__init__()
self.num_item = num_item
... | 6,596 | 38.740964 | 115 | py |
FastVae_Gpu | FastVae_Gpu-main/dataloader.py | import pandas as pd
from torch.utils.data import IterableDataset, Dataset
import torch
from torch.utils.data import Dataset, IterableDataset, DataLoader
import scipy.io as sci
import scipy as sp
import random
import numpy as np
import math
import os
class RecData(object):
def __init__(self, dir, file_name):
... | 3,526 | 35.739583 | 96 | py |
FastVae_Gpu | FastVae_Gpu-main/utils.py | import scipy as sp
import scipy.sparse as ss
import scipy.io as sio
import random
import numpy as np
from typing import List
import logging
import torch
import math
from torch.nn.utils.rnn import pad_sequence
def get_logger(filename, verbosity=1, name=None):
filename = filename + '.txt'
level_dict = {0: loggi... | 10,977 | 41.550388 | 166 | py |
FastVae_Gpu | FastVae_Gpu-main/sampler_gpu_mm.py | # The cluster algorithmn(K-means) is implemented on the GPU
from operator import imod, neg
from numpy.core.numeric import indices
import scipy.sparse as sps
from sklearn import cluster
from sklearn.cluster import KMeans
import torch
import numpy as np
import torch.nn as nn
from torch._C import device, dtype
def kmean... | 12,583 | 40.668874 | 163 | py |
KSTER | KSTER-main/setup.py | #!/usr/bin/env python
from setuptools import setup, find_packages
with open("requirements.txt", encoding="utf-8") as req_fp:
install_requires = req_fp.readlines()
setup(
name='joeynmt',
version='1.2',
description='Minimalist NMT for educational purposes',
author='Jasmijn Bastings and Julia Kreutze... | 790 | 28.296296 | 68 | py |
KSTER | KSTER-main/test/__init__.py | 0 | 0 | 0 | py | |
KSTER | KSTER-main/test/unit/test_vocabulary.py | import unittest
import os
from joeynmt.vocabulary import Vocabulary
class TestVocabulary(unittest.TestCase):
def setUp(self):
self.file = "test/data/toy/train.de"
sent = "Die Wahrheit ist, dass die Titanic – obwohl sie alle " \
"Kinokassenrekorde bricht – nicht gerade die aufregend... | 2,599 | 47.148148 | 80 | py |
KSTER | KSTER-main/test/unit/test_data.py | import unittest
import numpy as np
from joeynmt.data import MonoDataset, TranslationDataset, load_data, \
make_data_iter
class TestData(unittest.TestCase):
def setUp(self):
self.train_path = "test/data/toy/train"
self.dev_path = "test/data/toy/dev"
self.test_path = "test/data/toy/tes... | 7,422 | 45.685535 | 80 | py |
KSTER | KSTER-main/test/unit/test_decoder.py | from torch.nn import GRU, LSTM
import torch
from joeynmt.decoders import RecurrentDecoder
from joeynmt.encoders import RecurrentEncoder
from .test_helpers import TensorTestCase
class TestRecurrentDecoder(TensorTestCase):
def setUp(self):
self.emb_size = 10
self.num_layers = 3
self.hidden... | 12,263 | 51.187234 | 80 | py |
KSTER | KSTER-main/test/unit/test_loss.py | import torch
from joeynmt.loss import XentLoss
from .test_helpers import TensorTestCase
class TestTransformerUtils(TensorTestCase):
def setUp(self):
seed = 42
torch.manual_seed(seed)
def test_label_smoothing(self):
pad_index = 0
smoothing = 0.4
criterion = XentLoss(p... | 3,037 | 34.325581 | 78 | py |
KSTER | KSTER-main/test/unit/test_weight_tying.py | from torch.nn import GRU, LSTM
import torch
import numpy as np
from joeynmt.encoders import RecurrentEncoder
from .test_helpers import TensorTestCase
from joeynmt.model import build_model
from joeynmt.vocabulary import Vocabulary
import copy
class TestWeightTying(TensorTestCase):
def setUp(self):
self.s... | 3,861 | 34.759259 | 77 | py |
KSTER | KSTER-main/test/unit/test_transformer_utils.py | import torch
from joeynmt.transformer_layers import PositionalEncoding
from .test_helpers import TensorTestCase
class TestTransformerUtils(TensorTestCase):
def setUp(self):
seed = 42
torch.manual_seed(seed)
def test_position_encoding(self):
batch_size = 2
max_time = 3
... | 595 | 23.833333 | 66 | py |
KSTER | KSTER-main/test/unit/test_batch.py | import torch
import random
from torchtext.data.batch import Batch as TorchTBatch
from joeynmt.batch import Batch
from joeynmt.data import load_data, make_data_iter
from joeynmt.constants import PAD_TOKEN
from .test_helpers import TensorTestCase
class TestData(TensorTestCase):
def setUp(self):
self.trai... | 5,692 | 40.554745 | 80 | py |
KSTER | KSTER-main/test/unit/test_model_init.py | from torch.nn import GRU, LSTM
import torch
from torch import nn
import numpy as np
from joeynmt.encoders import RecurrentEncoder
from .test_helpers import TensorTestCase
from joeynmt.model import build_model
from joeynmt.vocabulary import Vocabulary
import copy
class TestModelInit(TensorTestCase):
def setUp(se... | 1,962 | 31.180328 | 75 | py |
KSTER | KSTER-main/test/unit/test_encoder.py | from torch.nn import GRU, LSTM
import torch
from joeynmt.encoders import RecurrentEncoder
from .test_helpers import TensorTestCase
class TestRecurrentEncoder(TensorTestCase):
def setUp(self):
self.emb_size = 10
self.num_layers = 3
self.hidden_size = 7
seed = 42
torch.manu... | 5,086 | 45.669725 | 79 | py |
KSTER | KSTER-main/test/unit/test_search.py | import torch
import numpy as np
from joeynmt.search import greedy, recurrent_greedy, transformer_greedy
from joeynmt.search import beam_search
from joeynmt.decoders import RecurrentDecoder, TransformerDecoder
from joeynmt.encoders import RecurrentEncoder
from joeynmt.embeddings import Embeddings
from joeynmt.model imp... | 8,723 | 39.018349 | 83 | py |
KSTER | KSTER-main/test/unit/test_knn.py | import sys
sys.path.append("../..")
from joeynmt.knn import KNNElasticSearch, KNNFaissSearch
import time
import numpy as np
embeddings_path = "embeddings_4.npy"
embeddings = np.load(embeddings_path)
m_embeddings = np.load(embeddings_path, mmap_mode="r")
batch_size = 32
d = 512
n_run = 100
# es_knn = KNNElasticSearc... | 1,210 | 33.6 | 102 | py |
KSTER | KSTER-main/test/unit/__init__.py | 0 | 0 | 0 | py | |
KSTER | KSTER-main/test/unit/test_transformer_decoder.py | import torch
from joeynmt.decoders import TransformerDecoder, TransformerDecoderLayer
from .test_helpers import TensorTestCase
class TestTransformerDecoder(TensorTestCase):
def setUp(self):
self.emb_size = 12
self.num_layers = 3
self.hidden_size = 12
self.ff_size = 24
sel... | 6,322 | 42.909722 | 79 | py |
KSTER | KSTER-main/test/unit/test_transformer_encoder.py | import torch
from joeynmt.encoders import TransformerEncoder
from .test_helpers import TensorTestCase
class TestTransformerEncoder(TensorTestCase):
def setUp(self):
self.emb_size = 12
self.num_layers = 3
self.hidden_size = 12
self.ff_size = 24
self.num_heads = 4
s... | 3,144 | 40.381579 | 78 | py |
KSTER | KSTER-main/test/unit/test_metric.py | import unittest
from test.unit.test_helpers import TensorTestCase
from joeynmt.metrics import chrf, bleu, token_accuracy
class TestMetrics(TensorTestCase):
def test_chrf_without_whitespace(self):
hyp1 = ["t est"]
ref1 = ["tez t"]
score1 = chrf(hyp1, ref1, remove_whitespace=True)
... | 1,674 | 30.018519 | 59 | py |
KSTER | KSTER-main/test/unit/test_embeddings.py | import torch
from joeynmt.embeddings import Embeddings
from .test_helpers import TensorTestCase
class TestEmbeddings(TensorTestCase):
def setUp(self):
self.emb_size = 10
self.vocab_size = 11
self.pad_idx = 1
seed = 42
torch.manual_seed(seed)
def test_size(self):
... | 3,091 | 38.139241 | 78 | py |
KSTER | KSTER-main/test/unit/test_attention.py | import torch
from joeynmt.attention import BahdanauAttention, LuongAttention
from .test_helpers import TensorTestCase
class TestBahdanauAttention(TensorTestCase):
def setUp(self):
self.key_size = 3
self.query_size = 5
self.hidden_size = 7
seed = 42
torch.manual_seed(seed)... | 16,419 | 42.786667 | 81 | py |
KSTER | KSTER-main/test/unit/test_helpers.py | import unittest
import torch
class TensorTestCase(unittest.TestCase):
def assertTensorNotEqual(self, expected, actual):
equal = torch.equal(expected, actual)
if equal:
self.fail("Tensors did match but weren't supposed to: expected {},"
" actual {}.".format(expect... | 879 | 34.2 | 79 | py |
KSTER | KSTER-main/scripts/average_checkpoints_launcher.py | import os
import glob
import subprocess
subfolder = os.listdir("models")[0]
folder = os.path.join("models", subfolder)
inputs_str = " ".join(glob.glob("%s/[0-9]*.ckpt" % folder))
output_str = "%s/averaged.ckpt" % folder
subprocess.call("python3 scripts/average_checkpoints.py --inputs %s --output %s" % (inputs_str, ou... | 342 | 33.3 | 120 | py |
KSTER | KSTER-main/scripts/preprocess_jparacrawl.py | # coding: utf-8
"""
Preprocess JParaCrawl
"""
import os
import argparse
import pandas as pd
import numpy as np
import unicodedata
from collections import OrderedDict
def prepare(data_dir, size, seed=None):
dtype = OrderedDict({'source': str, 'probability': float, 'en': str, 'ja': str})
df = pd.read_csv(os.pa... | 1,849 | 36.755102 | 111 | py |
KSTER | KSTER-main/scripts/post_process_hypothesis.py | from joeynmt.vocabulary import Vocabulary
import os
import subprocess
import yaml
import glob
from sacremoses import MosesTokenizer, MosesDetokenizer
import spacy
from collections import Counter
config_path = glob.glob("*.yaml")[0]
config = yaml.safe_load(open(config_path, "r", encoding="utf-8"))
src_lang = config["d... | 6,629 | 41.774194 | 168 | py |
KSTER | KSTER-main/scripts/average_checkpoints.py | #!/usr/bin/env python3
# coding: utf-8
"""
Checkpoint averaging
Mainly follows:
https://github.com/pytorch/fairseq/blob/master/scripts/average_checkpoints.py
"""
import argparse
import collections
import torch
from typing import List
def average_checkpoints(inputs: List[str]) -> dict:
"""Loads checkpoints fro... | 3,018 | 30.123711 | 79 | py |
KSTER | KSTER-main/scripts/plot_validations.py | # coding: utf-8
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import argparse
import numpy as np
def read_vfiles(vfiles):
"""
Parse validation report files
:param vfiles: list of files
:return:
"""
models = {}
... | 3,130 | 32.308511 | 79 | py |
KSTER | KSTER-main/scripts/combiner_average_checkpoints_launcher.py | import os
import glob
import subprocess
import yaml
subfolder = os.listdir("models")[0]
folder = os.path.join("models", subfolder)
files = glob.glob("%s/[0-9]*.ckpt" % folder)
ids = sorted([int(f[len(folder)+1:-5]) for f in files])
config_path = glob.glob("*.yaml")[0]
config = yaml.safe_load(open(config_path, "r", en... | 802 | 35.5 | 120 | py |
KSTER | KSTER-main/scripts/build_vocab.py | #!/usr/bin/env python3
import argparse
from collections import OrderedDict
import numpy as np
def build_vocab(train_paths, output_path):
"""
Builds the vocabulary.
Compatible with Nematus build_dict function, but does not
output frequencies and special symbols.
:param train_paths:
:param outp... | 2,034 | 31.301587 | 79 | py |
KSTER | KSTER-main/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 5,375 | 28.377049 | 79 | py |
KSTER | KSTER-main/joeynmt/vocabulary.py | # coding: utf-8
"""
Vocabulary module
"""
from collections import defaultdict, Counter
from typing import List
import numpy as np
from torchtext.data import Dataset
from joeynmt.constants import UNK_TOKEN, DEFAULT_UNK_ID, \
EOS_TOKEN, BOS_TOKEN, PAD_TOKEN
class Vocabulary:
""" Vocabulary represents mapping... | 6,887 | 33.09901 | 79 | py |
KSTER | KSTER-main/joeynmt/__main__.py | import argparse
from joeynmt.training import train
from joeynmt.combiner_training import combiner_train
from joeynmt.prediction import test
from joeynmt.prediction import translate
from joeynmt.prediction import analyze
def main():
ap = argparse.ArgumentParser("KSTER")
ap.add_argument("mode", choices=["train... | 4,404 | 41.76699 | 136 | py |
KSTER | KSTER-main/joeynmt/build_database.py | import torch
import numpy as np
import logging
from hashlib import md5
from joeynmt.prediction import parse_test_args
from joeynmt.helpers import load_config, load_checkpoint, get_latest_checkpoint
from joeynmt.data import load_data, Dataset, make_data_iter
from joeynmt.model import build_model, _DataParallel, Model
... | 7,739 | 39.52356 | 119 | py |
KSTER | KSTER-main/joeynmt/prediction.py | # coding: utf-8
"""
This modules holds methods for generating predictions from a model.
"""
import os
import sys
from typing import List, Optional
import logging
import numpy as np
import json
import torch
from torchtext.data import Dataset, Field
from joeynmt.helpers import bpe_postprocess, check_combiner_cfg, load_... | 32,482 | 41.406005 | 239 | py |
KSTER | KSTER-main/joeynmt/constants.py | # coding: utf-8
"""
Defining global constants
"""
UNK_TOKEN = '<unk>'
PAD_TOKEN = '<pad>'
BOS_TOKEN = '<s>'
EOS_TOKEN = '</s>'
DEFAULT_UNK_ID = lambda: 0
| 156 | 12.083333 | 26 | py |
KSTER | KSTER-main/joeynmt/plotting.py | #!/usr/bin/env python
from typing import List, Optional
import numpy as np
# pylint: disable=wrong-import-position
import matplotlib
matplotlib.use('Agg')
from matplotlib import rcParams
from matplotlib.figure import Figure
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
def pl... | 2,476 | 30.75641 | 78 | py |
KSTER | KSTER-main/joeynmt/batch.py | # coding: utf-8
"""
Implementation of a mini-batch.
"""
import torch
class Batch:
"""Object for holding a batch of data with mask during training.
Input is a batch from a torch text iterator.
"""
# pylint: disable=too-many-instance-attributes
def __init__(self, torch_batch, pad_index, use_cuda=Fa... | 3,327 | 32.616162 | 73 | py |
KSTER | KSTER-main/joeynmt/loss.py | # coding: utf-8
"""
Module to implement training loss
"""
import torch
from torch import nn, Tensor
from torch.autograd import Variable
class XentLoss(nn.Module):
"""
Cross-Entropy Loss with optional label smoothing
"""
def __init__(self, pad_index: int, smoothing: float = 0.0):
super().__in... | 3,120 | 38.506329 | 78 | py |
KSTER | KSTER-main/joeynmt/embeddings.py | # coding: utf-8
"""
Embedding module
"""
import io
import math
import logging
import torch
from torch import nn, Tensor
from joeynmt.helpers import freeze_params
from joeynmt.vocabulary import Vocabulary
logger = logging.getLogger(__name__)
class Embeddings(nn.Module):
"""
Simple embeddings class
"""
... | 4,157 | 33.363636 | 80 | py |
KSTER | KSTER-main/joeynmt/training.py | # coding: utf-8
"""
Training module
"""
import argparse
import time
import shutil
from typing import List
import logging
import os
import sys
import collections
import pathlib
import numpy as np
import torch
from torch import Tensor
from torch.utils.tensorboard import SummaryWriter
from torchtext.data import Dataset... | 33,649 | 40.389914 | 80 | py |
KSTER | KSTER-main/joeynmt/model.py | # coding: utf-8
"""
Module to represents whole models
"""
from typing import Callable
import logging
import torch.nn as nn
from torch import Tensor
import torch.nn.functional as F
from joeynmt.initialization import initialize_model
from joeynmt.embeddings import Embeddings
from joeynmt.encoders import Encoder, Recurr... | 12,260 | 37.800633 | 80 | py |
KSTER | KSTER-main/joeynmt/data.py | # coding: utf-8
"""
Data module
"""
import sys
import random
import os
import os.path
from typing import Optional
import logging
from torchtext.datasets import TranslationDataset
from torchtext import data
from torchtext.data import Dataset, Iterator, Field
from joeynmt.constants import UNK_TOKEN, EOS_TOKEN, BOS_TOKE... | 9,111 | 37.774468 | 79 | py |
KSTER | KSTER-main/joeynmt/transformer_layers.py | # -*- coding: utf-8 -*-
import math
import torch
import torch.nn as nn
from torch import Tensor
# pylint: disable=arguments-differ
class MultiHeadedAttention(nn.Module):
"""
Multi-Head Attention module from "Attention is All You Need"
Implementation modified from OpenNMT-py.
https://github.com/OpenN... | 10,131 | 32.66113 | 80 | py |
KSTER | KSTER-main/joeynmt/faiss_index.py | # -*- coding: utf-8 -*-
# create@ 2021-02-04 13:50
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import faiss
import numpy as np
from typing import Tuple
import re
class FaissIndex(object):
def __init__(self, f... | 4,631 | 39.631579 | 145 | py |
KSTER | KSTER-main/joeynmt/initialization.py | # coding: utf-8
"""
Implements custom initialization
"""
import math
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn.init import _calculate_fan_in_and_fan_out
def orthogonal_rnn_init_(cell: nn.RNNBase, gain: float = 1.):
"""
Orthogonal initialization of recurrent weights
RNN p... | 6,419 | 35.271186 | 79 | py |
KSTER | KSTER-main/joeynmt/builders.py | # coding: utf-8
"""
Collection of builder functions
"""
from typing import Callable, Optional, Generator
import torch
from torch import nn
from torch.optim.lr_scheduler import _LRScheduler, ReduceLROnPlateau, \
StepLR, ExponentialLR
from torch.optim import Optimizer
from joeynmt.helpers import ConfigurationError
... | 12,622 | 38.446875 | 80 | py |
KSTER | KSTER-main/joeynmt/database.py | # -*- coding: utf-8 -*-
# create@ 2021-01-26 18:02
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from typing import Tuple
import numpy as np
from joeynmt.faiss_index import FaissIndex
class Database(object):
""... | 3,496 | 37.01087 | 149 | py |
KSTER | KSTER-main/joeynmt/combiners.py | import torch
from torch import nn
import torch.nn.functional as F
from torch.nn import init
import numpy as np
import math
from typing import Tuple
from joeynmt.database import Database, EnhancedDatabase
from joeynmt.kernel import Kernel, GaussianKernel, LaplacianKernel
class Combiner(nn.Module):
def __init__(se... | 11,897 | 46.214286 | 140 | py |
KSTER | KSTER-main/joeynmt/metrics.py | # coding: utf-8
"""
This module holds various MT evaluation metrics.
"""
from typing import List
import sacrebleu
def chrf(hypotheses, references, remove_whitespace=True):
"""
Character F-score from sacrebleu
:param hypotheses: list of hypotheses (strings)
:param references: list of references (stri... | 2,355 | 32.657143 | 83 | py |
KSTER | KSTER-main/joeynmt/__init__.py | 0 | 0 | 0 | py | |
KSTER | KSTER-main/joeynmt/search.py | # coding: utf-8
import torch
import torch.nn.functional as F
from torch import Tensor
import numpy as np
from joeynmt.decoders import TransformerDecoder
from joeynmt.model import Model
from joeynmt.batch import Batch
from joeynmt.helpers import tile
__all__ = ["greedy", "transformer_greedy", "beam_search", "run_batch... | 18,708 | 39.321121 | 88 | py |
KSTER | KSTER-main/joeynmt/attention.py | # coding: utf-8
"""
Attention modules
"""
import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
class AttentionMechanism(nn.Module):
"""
Base attention class
"""
def forward(self, *inputs):
raise NotImplementedError("Implement this.")
class BahdanauAtt... | 7,824 | 33.933036 | 80 | py |
KSTER | KSTER-main/joeynmt/helpers.py | # coding: utf-8
"""
Collection of helper functions
"""
import copy
import glob
import os
import os.path
import errno
import shutil
import random
import logging
from typing import Optional, List
import pathlib
import numpy as np
import pkg_resources
import torch
from torch import nn, Tensor
from torch.utils.tensorboard... | 14,240 | 32.587264 | 122 | py |
KSTER | KSTER-main/joeynmt/combiner_training.py | # coding: utf-8
"""
Training module
"""
import argparse
import time
import shutil
from typing import List
import logging
import os
import sys
import collections
import pathlib
import numpy as np
import torch
from torch import Tensor
from torch.utils.tensorboard import SummaryWriter
from torchtext.data import Dataset... | 34,477 | 40.893074 | 89 | py |
KSTER | KSTER-main/joeynmt/decoders.py | # coding: utf-8
"""
Various decoders
"""
from typing import Optional
import torch
import torch.nn as nn
from torch import Tensor
from joeynmt.attention import BahdanauAttention, LuongAttention
from joeynmt.encoders import Encoder
from joeynmt.helpers import freeze_params, ConfigurationError, subsequent_mask
from joey... | 23,155 | 40.647482 | 80 | py |
KSTER | KSTER-main/joeynmt/encoders.py | # coding: utf-8
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from joeynmt.helpers import freeze_params
from joeynmt.transformer_layers import \
TransformerEncoderLayer, PositionalEncoding
#pylint: disable=abstract-method
cla... | 8,571 | 36.432314 | 80 | py |
KSTER | KSTER-main/joeynmt/kernel.py | import torch
from typing import Tuple, Union
class Kernel(object):
def __init__(self) -> None:
super(Kernel, self).__init__()
def similarity(self, distances: torch.Tensor, bandwidth: Union[float, torch.Tensor]) -> torch.Tensor:
raise NotImplementedError
def compute_example_based_dist... | 1,423 | 40.882353 | 143 | py |
wsireg | wsireg-master/setup.py | #!/usr/bin/env python
"""The setup script."""
from setuptools import find_packages, setup
with open('README.rst') as readme_file:
readme = readme_file.read()
with open('HISTORY.rst') as history_file:
history = history_file.read()
with open('requirements.txt') as f:
requirements = f.read().splitlines()
... | 1,572 | 26.12069 | 90 | py |
wsireg | wsireg-master/wsireg/wsireg2d.py | import json
import tempfile
import time
from copy import copy, deepcopy
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union
from warnings import warn
import shutil
import numpy as np
import yaml
from wsireg.parameter_maps.preprocessing import ImagePreproParams
from wsireg.parameter_map... | 62,774 | 34.526316 | 133 | py |
wsireg | wsireg-master/wsireg/__init__.py | # flake8: noqa
from .wsireg2d import WsiReg2D
"""wsireg."""
__author__ = """Nathan Heath Patterson"""
__email__ = 'heath.patterson@vanderbilt.edu'
__version__ = '0.3.8'
| 171 | 18.111111 | 44 | py |
wsireg | wsireg-master/wsireg/reg_transforms/reg_transform.py | from warnings import warn
from typing import Optional
import numpy as np
import SimpleITK as sitk
from wsireg.utils.tform_conversion import convert_to_itk
class RegTransform:
"""Container for elastix transform that manages inversion and other metadata.
Converts elastix transformation dict to it's SimpleITK r... | 6,013 | 35.448485 | 94 | py |
wsireg | wsireg-master/wsireg/reg_transforms/reg_transform_seq.py | import json
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import SimpleITK as sitk
from wsireg.reg_transforms.reg_transform import RegTransform
from wsireg.utils.tform_utils import ELX_TO_ITK_INTERPOLATORS
class RegTransformSeq:
"""Class to concatenate an... | 10,422 | 33.39934 | 112 | py |
wsireg | wsireg-master/wsireg/reg_transforms/__init__.py | from .reg_transform import RegTransform # noqa: F401
from .reg_transform_seq import RegTransformSeq # noqa: F401
| 115 | 37.666667 | 60 | py |
wsireg | wsireg-master/wsireg/writers/merge_ome_tiff_writer.py | from pathlib import Path
from typing import List, Optional, Tuple, Union
import cv2
import numpy as np
import SimpleITK as sitk
from tifffile import TiffWriter
from wsireg.reg_images.reg_image import RegImage
from wsireg.reg_images.merge_reg_image import MergeRegImage
from wsireg.reg_transforms.reg_transform_seq impo... | 13,686 | 35.596257 | 106 | py |
wsireg | wsireg-master/wsireg/writers/__init__.py | 0 | 0 | 0 | py | |
wsireg | wsireg-master/wsireg/writers/ome_tiff_writer.py | from pathlib import Path
from typing import List, Optional, Tuple, Union
import cv2
import numpy as np
import SimpleITK as sitk
from tifffile import TiffWriter
from wsireg.reg_images.reg_image import RegImage
from wsireg.reg_transforms.reg_transform_seq import RegTransformSeq
from wsireg.utils.im_utils import (
f... | 10,814 | 35.785714 | 112 | py |
wsireg | wsireg-master/wsireg/writers/tiled_ome_tiff_writer.py | import multiprocessing
import random
import string
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from typing import List, Optional, Tuple, Union
import dask.array as da
import numpy as np
import SimpleITK as sitk
import zarr
from tifffile import TiffWriter
from tiler import Tiler
from tqdm... | 29,657 | 34.433692 | 92 | py |
wsireg | wsireg-master/wsireg/reg_images/czi_reg_image.py | import warnings
from typing import Tuple
import dask.array as da
import numpy as np
import SimpleITK as sitk
from wsireg.reg_images.reg_image import RegImage
from wsireg.utils.im_utils import CziRegImageReader, guess_rgb
class CziRegImage(RegImage):
def __init__(
self,
image,
image_res,
... | 4,216 | 28.697183 | 93 | py |
wsireg | wsireg-master/wsireg/reg_images/sitk_reg_image.py | import warnings
import SimpleITK as sitk
from wsireg.reg_images.reg_image import RegImage
from wsireg.utils.im_utils import (
ensure_dask_array,
get_sitk_image_info,
guess_rgb,
)
class SitkRegImage(RegImage):
def __init__(
self,
image,
image_res,
mask=None,
pr... | 2,748 | 25.432692 | 93 | py |
wsireg | wsireg-master/wsireg/reg_images/np_reg_image.py | import warnings
import numpy as np
import SimpleITK as sitk
from wsireg.reg_images.reg_image import RegImage
from wsireg.utils.im_utils import (
ensure_dask_array,
guess_rgb,
preprocess_dask_array,
)
class NumpyRegImage(RegImage):
def __init__(
self,
image,
image_res,
... | 2,969 | 27.285714 | 93 | py |
wsireg | wsireg-master/wsireg/reg_images/reg_image.py | import json
from abc import ABC
from copy import deepcopy
from pathlib import Path
from typing import Dict, List, Optional, Tuple, Union
import dask.array as da
import itk
import numpy as np
import SimpleITK as sitk
from wsireg.parameter_maps.preprocessing import ImagePreproParams
from wsireg.reg_shapes import RegSha... | 19,721 | 29.960754 | 87 | py |
wsireg | wsireg-master/wsireg/reg_images/merge_reg_image.py | from pathlib import Path
from typing import List, Optional, Union
from warnings import warn
import numpy as np
from wsireg.reg_images.loader import reg_image_loader
class MergeRegImage:
def __init__(
self,
image_fp: List[Union[Path, str]],
image_res: List[Union[int, float]],
chan... | 2,466 | 31.038961 | 92 | py |
wsireg | wsireg-master/wsireg/reg_images/aics_reg_image.py | import warnings
import dask.array as da
import numpy as np
import SimpleITK as sitk
from aicsimageio import AICSImage
from wsireg.reg_images.reg_image import RegImage
from wsireg.utils.im_utils import (
ensure_dask_array,
guess_rgb,
preprocess_dask_array,
)
class AICSRegImage(RegImage):
def __init__... | 2,908 | 25.935185 | 93 | py |
wsireg | wsireg-master/wsireg/reg_images/tifffile_reg_image.py | import warnings
from typing import List, Tuple
import dask.array as da
import numpy as np
import SimpleITK as sitk
from ome_types import from_xml
from tifffile import TiffFile
from wsireg.reg_images.reg_image import RegImage
from wsireg.utils.im_utils import (
get_tifffile_info,
guess_rgb,
preprocess_dask... | 4,762 | 29.33758 | 93 | py |
wsireg | wsireg-master/wsireg/reg_images/__init__.py | from .np_reg_image import NumpyRegImage # noqa: F401
from .sitk_reg_image import SitkRegImage # noqa: F401
from .tifffile_reg_image import TiffFileRegImage # noqa: F401
from .aics_reg_image import AICSRegImage # noqa: F401
from .czi_reg_image import CziRegImage # noqa: F401
from .merge_reg_image import MergeRegIma... | 337 | 47.285714 | 62 | py |
wsireg | wsireg-master/wsireg/reg_images/loader.py | from pathlib import Path
from typing import Union, Optional, List
import numpy as np
import dask.array as da
import zarr
from wsireg.utils.im_utils import ARRAYLIKE_CLASSES, TIFFFILE_EXTS
from wsireg.parameter_maps.preprocessing import ImagePreproParams
from . import CziRegImage # AICSRegImage,
from . import NumpyRegI... | 3,482 | 35.28125 | 115 | py |
wsireg | wsireg-master/wsireg/utils/output_utils.py | from typing import Dict, Union
from pathlib import Path
import re
import numpy as np
import matplotlib.pyplot as plt
def _natural_sort(list_to_sort: list) -> list:
"""
Sort list account for lack of leading zeroes.
"""
convert = (
lambda text: int(text) if text.isdigit() else text.lower()
)... | 7,574 | 29.668016 | 117 | py |
wsireg | wsireg-master/wsireg/utils/tform_utils.py | import json
from pathlib import Path
from typing import Tuple, Union
import itk
import numpy as np
import SimpleITK as sitk
from wsireg.parameter_maps.transformations import (
BASE_AFF_TFORM,
BASE_RIG_TFORM,
)
from wsireg.reg_transforms.reg_transform import RegTransform
from wsireg.utils.itk_im_conversions im... | 18,878 | 30.998305 | 125 | py |
wsireg | wsireg-master/wsireg/utils/itk_im_conversions.py | import itk
import SimpleITK as sitk
def itk_image_to_sitk_image(image):
origin = tuple(image.GetOrigin())
spacing = tuple(image.GetSpacing())
direction = itk.GetArrayFromMatrix(image.GetDirection()).flatten()
image = sitk.GetImageFromArray(
itk.GetArrayFromImage(image),
isVector=image.... | 1,053 | 29.114286 | 70 | py |
wsireg | wsireg-master/wsireg/utils/shape_utils.py | import json
import zipfile
from copy import deepcopy
from pathlib import Path
import cv2
import geojson
import numpy as np
import SimpleITK as sitk
from wsireg.reg_transforms.reg_transform import RegTransform
from wsireg.utils.tform_utils import wsireg_transforms_to_itk_composite
GJ_SHAPE_TYPE = {
"polygon": geo... | 20,475 | 30.213415 | 112 | py |
wsireg | wsireg-master/wsireg/utils/config_utils.py | import yaml
def parse_check_reg_config(yaml_filepath):
with open(yaml_filepath, "r") as file:
reg_config = yaml.full_load(file)
def check_for_key(top_key, check_dict, check_key):
if check_dict.get(check_key) is None:
raise ValueError(f"{top_key} does not contain an {check_key}")
... | 1,985 | 29.553846 | 78 | py |
wsireg | wsireg-master/wsireg/utils/tform_conversion.py | from copy import deepcopy
import SimpleITK as sitk
def euler_elx_to_itk2d(tform, is_translation=False):
euler2d = sitk.Euler2DTransform()
if is_translation:
elx_parameters = [0]
elx_parameters_trans = [float(p) for p in tform['TransformParameters']]
elx_parameters.extend(elx_paramete... | 3,303 | 32.714286 | 79 | py |
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