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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attention-is-all-you-need-pytorch | attention-is-all-you-need-pytorch-master/transformer/SubLayers.py | ''' Define the sublayers in encoder/decoder layer '''
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from transformer.Modules import ScaledDotProductAttention
__author__ = "Yu-Hsiang Huang"
class MultiHeadAttention(nn.Module):
''' Multi-Head Attention module '''
def __init__(self, n... | 2,606 | 30.409639 | 96 | py |
attention-is-all-you-need-pytorch | attention-is-all-you-need-pytorch-master/transformer/Modules.py | import torch
import torch.nn as nn
import torch.nn.functional as F
__author__ = "Yu-Hsiang Huang"
class ScaledDotProductAttention(nn.Module):
''' Scaled Dot-Product Attention '''
def __init__(self, temperature, attn_dropout=0.1):
super().__init__()
self.temperature = temperature
self.... | 674 | 24.961538 | 68 | py |
attention-is-all-you-need-pytorch | attention-is-all-you-need-pytorch-master/transformer/Models.py | ''' Define the Transformer model '''
import torch
import torch.nn as nn
import numpy as np
from transformer.Layers import EncoderLayer, DecoderLayer
__author__ = "Yu-Hsiang Huang"
def get_pad_mask(seq, pad_idx):
return (seq != pad_idx).unsqueeze(-2)
def get_subsequent_mask(seq):
''' For masking out the su... | 7,678 | 37.58794 | 99 | py |
easyreg | easyreg-master/setup.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Note: To use the 'upload' functionality of this file, you must:
# $ pipenv install twine --dev
import io
import os
import sys
from shutil import rmtree
from setuptools import find_packages, setup, Command
from version import get_git_version
# Package meta-data.
NAME... | 4,359 | 26.080745 | 86 | py |
easyreg | easyreg-master/tools/draw_deformation.py | import numpy as np
import sys,os
os.environ["CUDA_VISIBLE_DEVICES"] = ''
from easyreg.viewers import *
from mermaid.utils import *
from mermaid.data_utils import *
import SimpleITK as sitk
from glob import glob
import os
sz = [160,200,200]
def get_image_list_to_draw(refer_folder,momentum_folder,img_type,source_targ... | 12,626 | 52.731915 | 210 | py |
easyreg | easyreg-master/tools/transform_disp_into_torch_form.py | import nibabel as nib
import numpy as np
def transform_disp_into_torch_form(inv_transform_file):
inv_map = nib.load(inv_transform_file)
inv_map = inv_map.get_fdata()
assert inv_map.shape[0]==3
inv_map = np.transpose(inv_map,[3,2,1,0])
return inv_map
| 271 | 26.2 | 55 | py |
easyreg | easyreg-master/tools/draw_deformation_2d.py | import os
os.environ["CUDA_VISIBLE_DEVICES"] = ''
from tools.draw_deformation_viewers import *
from mermaid.utils import *
from mermaid.data_utils import *
from glob import glob
sz = [160,200,200]
def get_image_list_to_draw(refer_folder,momentum_folder,img_type,source_target_folder,t_list):
"""
we first need ... | 18,322 | 52.110145 | 222 | py |
easyreg | easyreg-master/tools/transform_between_mermaid_and_itk.py | import SimpleITK as sitk
import numpy as np
import os
from easyreg.utils import resample_image
from mermaid.utils import compute_warped_image_multiNC
import torch
from easyreg.net_utils import gen_identity_map
from easyreg.demons_utils import sitk_grid_sampling
import tools.image_rescale as ires
# img_org_path = "/pl... | 5,800 | 50.794643 | 221 | py |
easyreg | easyreg-master/tools/warp_image_label.py | import os
os.environ["CUDA_VISIBLE_DEVICES"] = ''
from easyreg.reg_data_utils import read_txt_into_list, get_file_name
from tools.image_rescale import save_image_with_given_reference
import SimpleITK as sitk
import torch
import numpy as np
from glob import glob
from mermaid.utils import compute_warped_image_multiNC, re... | 2,617 | 49.346154 | 172 | py |
easyreg | easyreg-master/tools/visual_tools.py |
import matplotlib.pyplot as plt
from easyreg import utils
import SimpleITK as sitk
import torch
import numpy as np
import mermaid.finite_differences as fdt
import mermaid.utils as py_utils
import os
from scipy import misc
def read_png_into_numpy(file_path,name=None,visual=False):
image = misc.imread(file_path,fla... | 7,597 | 31.75 | 121 | py |
easyreg | easyreg-master/tools/print_sh.py | import os
def print_txt(txt, output_path):
with open(output_path,"w") as f:
f.write(txt)
key_w_list = [10,20,30,40,60,80,100]
key_w_list2= ["1d","atlas",'rand','bspline','aug']
for key_w2 in key_w_list2:
output_path = '/playpen-raid/zyshen/debug/llf_output/par/oai_seg_{}'.format(key_w2)
os... | 7,499 | 37.265306 | 400 | py |
easyreg | easyreg-master/tools/image_rescale.py | import SimpleITK as sitk
from easyreg.reg_data_utils import write_list_into_txt, generate_pair_name
from easyreg.utils import *
import mermaid.utils as py_utils
from mermaid.data_wrapper import MyTensor
def __read_and_clean_itk_info(input):
if isinstance(input,str):
return sitk.GetImageFromArray(sitk.Get... | 13,185 | 42.375 | 158 | py |
easyreg | easyreg-master/data_pre/fileio.py | """
Helper functions to take care of all the file IO
"""
import itk
import os
import nrrd
import torch
import data_pre.image_manipulations as IM
import numpy as np
from abc import ABCMeta, abstractmethod
class FileIO(object):
"""
Abstract base class for file i/o.
"""
__metaclass__ = ABCMeta
def ... | 17,393 | 36.568035 | 127 | py |
easyreg | easyreg-master/data_pre/partition_multi_channel.py | import numpy as np
import SimpleITK as sitk
def partition_multi(option_p, patch_size,overlap_size, mode=None, img_sz=(-1,-1,-1), flicker_on=False, flicker_mode='rand'):
padding_mode = option_p[('padding_mode', 'reflect', 'padding_mode')]
mode = mode if mode is not None else option_p[('mode', 'pred', 'eval or ... | 9,615 | 53.636364 | 187 | py |
easyreg | easyreg-master/data_pre/transform_pool.py | """ classes of transformations for 3d simpleITK image
"""
import threading
import SimpleITK as sitk
import numpy as np
import torch
import math
import random
import time
from math import floor
class Resample(object):
"""Resample the volume in a sample to a given voxel size
Args:
voxel_size (float or... | 29,747 | 36.513241 | 140 | py |
easyreg | easyreg-master/data_pre/partition.py | import numpy as np
import SimpleITK as sitk
def partition(option_p, patch_size,overlap_size, mode=None, img_sz=(-1,-1,-1), flicker_on=False, flicker_mode='rand'):
padding_mode = option_p[('padding_mode', 'reflect', 'padding_mode')]
mode = mode if mode is not None else option_p[('mode', 'pred', 'eval or pred')... | 13,186 | 54.64135 | 181 | py |
easyreg | easyreg-master/data_pre/reg_preprocess_example/utils.py | import os
from easyreg.reg_data_utils import loading_img_list_from_files,generate_pair_name
local_path = "/playpen-raid/zyshen/oai_data"
server_path = "/pine/scr/z/y/zyshen/data/oai_data"
switcher = (local_path, server_path)
def server_switcher(f_path,switcher=("","")):
if len(switcher[0]):
f_path = f_pa... | 4,748 | 41.026549 | 164 | py |
easyreg | easyreg-master/data_pre/reg_preprocess_example/stik_resize_vs_mermaid_resize.py | import numpy as np
import SimpleITK as sitk
from easyreg.utils import get_resampled_image
import torch
def resize_img(img, is_label=False,img_after_resize=None):
"""
:param img: sitk input, factor is the outputs_ize/patched_sized
:return:
"""
img_sz = img.GetSize()
if img_after_resize is None:
... | 2,843 | 40.823529 | 135 | py |
easyreg | easyreg-master/data_pre/reg_preprocess_example/gen_from_brainstorm.py | """
Input txt for atlas to image , i.e. train txt atlas, image, atlas_label
folder for color image
folder for transformation
"""
import matplotlib as matplt
matplt.use('Agg')
import sys,os
os.environ["CUDA_VISIBLE_DEVICES"] = ''
sys.path.insert(0,os.path.abspath('.'))
sys.path.insert(0,os.path.abspath('..'))
sys.path... | 11,645 | 57.818182 | 170 | py |
easyreg | easyreg-master/data_pre/reg_preprocess_example/get_atlas_label.py | import os
import SimpleITK as sitk
from mermaid.utils import compute_warped_image_multiNC
from easyreg.reg_data_utils import *
from tools.image_rescale import save_image_with_given_reference
from glob import glob
import numpy as np
import torch
from easyreg.metrics import *
from functools import reduce
def make_one_h... | 4,342 | 45.698925 | 128 | py |
easyreg | easyreg-master/data_pre/reg_preprocess_example/dirlab_eval.py | import os
import json
import numpy as np
import torch
import SimpleITK as sitk
import pyvista as pv
import mermaid.utils as py_utils
from data_pre.reg_preprocess_example.vtk_utils import save_vtk
from tools.visual_tools import save_3D_img_from_numpy
COPD_ID=[
"copd_000001",
"copd_000002",
"copd_000003",
"c... | 14,618 | 52.746324 | 195 | py |
easyreg | easyreg-master/demo/hack.py | import os
import numpy as np
import torch
import SimpleITK as sitk
import pyvista as pv
from easyreg.net_utils import gen_identity_map
from tools.image_rescale import permute_trans
from tools.module_parameters import ParameterDict
from easyreg.multiscale_net_improved import Multiscale_FlowNet as model
from easyreg.util... | 21,097 | 44.765727 | 232 | py |
easyreg | easyreg-master/demo/demo_for_data_aug.py | """
A demo for fluid-based data augmentation.
"""
import matplotlib as matplt
import subprocess
matplt.use('Agg')
import os, sys, time
import torch
torch.backends.cudnn.benchmark=True
sys.path.insert(0, os.path.abspath('..'))
sys.path.insert(0, os.path.abspath('.'))
sys.path.insert(0, os.path.abspath('../easy_reg'))
... | 12,841 | 52.066116 | 201 | py |
easyreg | easyreg-master/demo/demo_for_easyreg_train.py | import matplotlib as matplt
matplt.use('Agg')
import os, sys
sys.path.insert(0,os.path.abspath('..'))
sys.path.insert(0,os.path.abspath('.'))
sys.path.insert(0,os.path.abspath('../easy_reg'))
#sys.path.insert(0,os.path.abspath('../mermaid'))
import numpy as np
import torch
import random
torch.backends.cudnn.benchmark=T... | 10,865 | 42.464 | 201 | py |
easyreg | easyreg-master/demo/demo_for_seg_train.py | import matplotlib as matplt
matplt.use('Agg')
import os, sys
sys.path.insert(0,os.path.abspath('..'))
sys.path.insert(0,os.path.abspath('.'))
sys.path.insert(0,os.path.abspath('../easyreg'))
import numpy as np
import torch
import random
import tools.module_parameters as pars
from abc import ABCMeta, abstractmethod
from... | 6,756 | 35.722826 | 152 | py |
easyreg | easyreg-master/demo/hack_v2.py | import os
import numpy as np
import torch
import SimpleITK as sitk
import pyvista as pv
from easyreg.net_utils import gen_identity_map
from tools.image_rescale import permute_trans
from tools.module_parameters import ParameterDict
from easyreg.lin_unpublic_net import model
from easyreg.utils import resample_image, get_... | 21,573 | 43.209016 | 204 | py |
easyreg | easyreg-master/demo/oai_eval.py | import os
import numpy as np
import torch
import SimpleITK as sitk
from tools.module_parameters import ParameterDict
from easyreg.mermaid_net import MermaidNet as model
from easyreg.utils import resample_image
import mermaid.utils as py_utils
def resize_img(img, img_after_resize=None, is_mask=False):
"""
:par... | 7,086 | 48.559441 | 184 | py |
easyreg | easyreg-master/demo/gen_aug_samples.py | import matplotlib as matplt
matplt.use('Agg')
import sys,os
#os.environ["CUDA_VISIBLE_DEVICES"] = ''
sys.path.insert(0,os.path.abspath('..'))
sys.path.insert(0,os.path.abspath('../easyreg'))
sys.path.insert(0,os.path.abspath('.'))
import random
import torch
from mermaid.model_evaluation import evaluate_model
import me... | 47,706 | 54.280417 | 235 | py |
easyreg | easyreg-master/demo/demo_for_seg_eval.py | import matplotlib as matplt
matplt.use('Agg')
import os, sys
sys.path.insert(0, os.path.abspath('..'))
sys.path.insert(0, os.path.abspath('.'))
sys.path.insert(0, os.path.abspath('../easy_reg'))
import tools.module_parameters as pars
from abc import ABCMeta, abstractmethod
from easyreg.piplines import run_one_task
f... | 9,097 | 41.915094 | 152 | py |
easyreg | easyreg-master/easyreg/seg_net.py | from .base_seg_model import SegModelBase
from .net_utils import print_network
from .losses import Loss
import torch.optim.lr_scheduler as lr_scheduler
from .utils import *
from .seg_unet import SegUnet
from .metrics import get_multi_metric
model_pool = {
'seg_unet': SegUnet,
}
class SegNet(SegModelBase):
"""... | 8,036 | 33.943478 | 147 | py |
easyreg | easyreg-master/easyreg/affine_net.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from .modules import *
from .net_utils import Bilinear
from torch.utils.checkpoint import checkpoint
from .utils import sigmoid_decay
from .losses import NCCLoss
class AffineNetSym(nn.Module):
"""
A ... | 19,504 | 43.329545 | 212 | py |
easyreg | easyreg-master/easyreg/losses.py | # coding=utf-8
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import mermaid.finite_differences as fdt
###############################################################################
# Functions
###############################################################################
clas... | 21,306 | 38.826168 | 152 | py |
easyreg | easyreg-master/easyreg/seg_unet.py | from .modules import Seg_resid
from .utils import *
import torch.nn as nn
from data_pre.partition import partition
class SegUnet(nn.Module):
def __init__(self, opt=None):
super(SegUnet, self).__init__()
self.opt = opt
seg_opt = opt['tsk_set'][('seg',{},"settings for seg task")]
sel... | 10,717 | 47.497738 | 190 | py |
easyreg | easyreg-master/easyreg/seg_data_loader_onfly.py | from __future__ import print_function, division
import blosc
import torch
from torch.utils.data import Dataset
from data_pre.seg_data_utils import *
from data_pre.transform import Transform
import SimpleITK as sitk
from multiprocessing import *
blosc.set_nthreads(1)
import progressbar as pb
from copy import deepcopy
im... | 21,752 | 44.413361 | 186 | py |
easyreg | easyreg-master/easyreg/multiscale_net.py | """
"""
import copy
from .losses import NCCLoss, Loss
from .net_utils import gen_identity_map
import mermaid.finite_differences_multi_channel as fdt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from .net_utils import Bilinear
from mermaid.libraries.modules import stn_nd
from .af... | 21,795 | 50.649289 | 178 | py |
easyreg | easyreg-master/easyreg/modules.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import mermaid.utils as py_utils
import torch
from .net_utils import *
class Affine_unet(nn.Module):
def __init__(self):
super(Affine_unet,self).__init__()
#(W−F+2P)/S+1, W - input siz... | 21,891 | 53.593516 | 127 | py |
easyreg | easyreg-master/easyreg/brainstorm.py | """
Framework described in
Data augmentation using learned transformationsfor one-shot medical image segmentation
http://www.mit.edu/~adalca/files/papers/cvpr2019_brainstorm.pdf
"""
from .net_utils import gen_identity_map
import mermaid.finite_differences as fdt
import numpy as np
import torch
import torch.nn as nn
im... | 14,398 | 38.449315 | 120 | py |
easyreg | easyreg-master/easyreg/base_mermaid.py | from time import time
from .base_reg_model import RegModelBase
from .utils import *
import mermaid.finite_differences as fdt
from mermaid.utils import compute_warped_image_multiNC
import tools.image_rescale as ires
from .metrics import get_multi_metric
import SimpleITK as sitk
class MermaidBase(RegModelBase):
""... | 20,184 | 54.30137 | 171 | py |
easyreg | easyreg-master/easyreg/voxel_morph.py | """
registration network described in voxelmorph
An experimental pytorch implemetation, the official tensorflow please refers to https://github.com/voxelmorph/voxelmorph
An Unsupervised Learning Model for Deformable Medical Image Registration
Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John Guttag, Adrian V. Dalca
C... | 25,457 | 40.194175 | 171 | py |
easyreg | easyreg-master/easyreg/mermaid_net.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from .utils import *
from .affine_net import *
from .momentum_net import *
import mermaid.module_parameters as pars
import mermaid.model_factory as py_mf
import mermaid.utils as py_utils
from functools import p... | 34,904 | 47.479167 | 175 | py |
easyreg | easyreg-master/easyreg/reg_net.py | from .base_mermaid import MermaidBase
from .affine_net import *
from .net_utils import print_network
from .losses import Loss
import torch.optim.lr_scheduler as lr_scheduler
from .utils import *
from .mermaid_net import MermaidNet
from .voxel_morph import VoxelMorphCVPR2018, VoxelMorphMICCAI2019
# from .multiscale_net_... | 10,289 | 37.111111 | 147 | py |
easyreg | easyreg-master/easyreg/utils.py | import torch
import numpy as np
import skimage
import os
import torchvision.utils as utils
import SimpleITK as sitk
from skimage import color
import mermaid.image_sampling as py_is
from mermaid.data_wrapper import AdaptVal,MyTensor
from .net_utils import gen_identity_map
from .net_utils import Bilinear
import mermaid.u... | 22,136 | 36.142617 | 157 | py |
easyreg | easyreg-master/easyreg/train_expr.py | from time import time
from .net_utils import *
def train_model(opt,model, dataloaders,writer):
since = time()
print_step = opt['tsk_set'][('print_step', [10,4,4], 'num of steps to print')]
num_epochs = opt['tsk_set'][('epoch', 100, 'num of training epoch')]
continue_train = opt['tsk_set'][('continue... | 10,456 | 48.795238 | 164 | py |
easyreg | easyreg-master/easyreg/base_seg_model.py |
from .utils import *
import SimpleITK as sitk
from tools.visual_tools import save_3D_img_from_numpy
class SegModelBase():
"""
the base class for image segmentation
"""
def name(self):
return 'SegModelBase'
def initialize(self, opt):
"""
:param opt: ParameterDict, task se... | 7,185 | 26.961089 | 152 | py |
easyreg | easyreg-master/easyreg/compare_sym.py | import torch
import numpy as np
import sys,os
import SimpleITK as sitk
from easyreg.net_utils import Bilinear
import ants
from .nifty_reg_utils import nifty_reg_resample
import subprocess
import nibabel as nib
from mermaid.utils import identity_map_multiN
# record_path ='/playpen/zyshen/debugs/compare_sym'
# moving_... | 9,171 | 39.764444 | 142 | py |
easyreg | easyreg-master/easyreg/mermaid_iter.py | from .base_mermaid import MermaidBase
from .utils import *
import mermaid.utils as py_utils
import mermaid.simple_interface as SI
class MermaidIter(MermaidBase):
def name(self):
return 'mermaid-iter'
def initialize(self,opt):
"""
:param opt: ParameterDict, task settings
:return:... | 12,862 | 41.3125 | 189 | py |
easyreg | easyreg-master/easyreg/data_manager.py | from easyreg.reg_data_utils import *
from torchvision import transforms
import torch
from easyreg import reg_data_loader_onfly as reg_loader_of
from easyreg import seg_data_loader_onfly as seg_loader_of
from easyreg.reg_data_loader_onfly import ToTensor
# todo reformat the import style
class DataManager(object):
de... | 8,907 | 39.126126 | 152 | py |
easyreg | easyreg-master/easyreg/reg_data_loader_onfly.py | from __future__ import print_function, division
import blosc
import torch
from torch.utils.data import Dataset
from .reg_data_utils import *
import SimpleITK as sitk
from multiprocessing import *
blosc.set_nthreads(1)
import progressbar as pb
class RegistrationDataset(Dataset):
"""registration dataset."""
def... | 17,753 | 44.175573 | 186 | py |
easyreg | easyreg-master/easyreg/create_model.py | import torch
def create_model(opt):
"""
create registration model object
:param opt: ParameterDict, task setting
:return: model object
"""
model = None
model_name = opt['tsk_set']['model']
gpu_id = opt['tsk_set']['gpu_ids']
sz = opt
# gpu_count = torch.cuda.device_count()
# p... | 1,615 | 33.382979 | 104 | py |
easyreg | easyreg-master/easyreg/net_utils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Module
import numpy as np
import torch.nn.init as init
import os
from easyreg.reproduce_paper_results import reproduce_paper_result
dim = 3
Conv = nn.Conv2d if dim == 2 else nn.Conv3d
MaxPool = nn.MaxPool2d if dim == 2 else nn.MaxP... | 17,530 | 33.107004 | 172 | py |
easyreg | easyreg-master/doc/source/conf.py | # -*- coding: utf-8 -*-
#
# pytorchRegistration documentation build configuration file, created by
# sphinx-quickstart on Sat Jul 29 08:41:36 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... | 13,865 | 30.585421 | 143 | py |
point_clouds_registration_benchmark | point_clouds_registration_benchmark-master/devel/generate_pcd_kaist.py | import argparse
import os
import sys
sys.path.append("..")
sys.path.append(".")
import h5py
import numpy as np
import torch
import open3d as o3
from tqdm import tqdm
def load_cloud(stamp, folder, pose, calib_pose):
pc_file = os.path.join(folder, f'{stamp}.bin')
pc = np.fromfile(pc_file, dtype=np.float32)
... | 5,242 | 29.841176 | 93 | py |
SPARQA | SPARQA-master/code/common/bert_args.py |
class BertArgs():
def __init__(self, root, mode):
# uncased model
self.bert_base_uncased_model = root + '/pre_train_models/bert-base-uncased.tar.gz'
self.bert_base_uncased_tokenization = root + '/pre_train_models/bert-base-uncased-vocab.txt'
# cased model
self.bert_base_cas... | 1,967 | 58.636364 | 118 | py |
SPARQA | SPARQA-master/code/parsing/models/fine_tuning_based_on_bert_interface/token_classifier_interface.py | from parsing.parsing_args import bert_args
from parsing.models.fine_tuning_based_on_bert.run_token_classifier import NodeRecogniationProcessor, convert_example_to_features_for_test
from parsing.models import model_utils
import sys
import os
import random
import numpy as np
import torch
from torch.utils.data import Tens... | 3,666 | 46.623377 | 137 | py |
SPARQA | SPARQA-master/code/parsing/models/fine_tuning_based_on_bert_interface/simplif_classifier_interface.py | import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader, SequentialSampler
from parsing.models import model_utils
from parsing.models.fine_tuning_based_on_bert.run_sequence_classifier import \
SimplificationQuestionProcessor, convert_examples_to_features
from parsing.models.pytorch_pr... | 2,937 | 51.464286 | 122 | py |
SPARQA | SPARQA-master/code/parsing/models/fine_tuning_based_on_bert_interface/paraphrase_classifier_interface.py | import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader, SequentialSampler
from parsing.models import model_utils
from parsing.models.fine_tuning_based_on_bert.run_sequence_classifier import ParaphraseProcess, convert_examples_to_features
from parsing.models.pytorch_pretrained_bert import... | 2,998 | 51.614035 | 133 | py |
SPARQA | SPARQA-master/code/parsing/models/fine_tuning_based_on_bert_interface/redundancy_span_interface.py | import torch
from tqdm import tqdm
from torch.utils.data import TensorDataset, DataLoader, SequentialSampler
from parsing.models.pytorch_pretrained_bert.tokenization import BertTokenizer
from parsing.models.pytorch_pretrained_bert.modeling import BertForQuestionAnswering
from parsing.models.fine_tuning_based_on_bert.r... | 3,128 | 58.037736 | 134 | py |
SPARQA | SPARQA-master/code/parsing/models/fine_tuning_based_on_bert_interface/sequences_classifier_interface.py | import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader, SequentialSampler
from parsing.models import model_utils
from parsing.models.fine_tuning_based_on_bert.run_sequence_classifier import SequencesRelationProcess, ParaphraseProcess,\
SimplificationQuestionProcessor, convert_example... | 4,410 | 52.792683 | 145 | py |
SPARQA | SPARQA-master/code/parsing/models/fine_tuning_based_on_bert_interface/headword_span_interface.py | import torch
from tqdm import tqdm
from torch.utils.data import TensorDataset, DataLoader, SequentialSampler
from parsing.models.pytorch_pretrained_bert.tokenization import BertTokenizer
from parsing.models.pytorch_pretrained_bert.modeling import BertForQuestionAnswering
from parsing.models.fine_tuning_based_on_bert.ru... | 3,612 | 60.237288 | 142 | py |
SPARQA | SPARQA-master/code/parsing/models/fine_tuning_based_on_bert_interface/joint_three_models_interface.py | import torch
from tqdm import tqdm
from torch.utils.data import TensorDataset, DataLoader, SequentialSampler
from parsing.models.pytorch_pretrained_bert.tokenization import BertTokenizer
from parsing.models.pytorch_pretrained_bert.modeling import BertForSpanWithHeadwordWithLabel
from parsing.models import model_utils
... | 4,168 | 56.902778 | 147 | py |
SPARQA | SPARQA-master/code/parsing/models/fine_tuning_based_on_bert/run_sequence_classifier.py | """BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
import random
from tqdm import tqdm, trange
import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from ... | 28,072 | 40.9 | 134 | py |
SPARQA | SPARQA-master/code/parsing/models/fine_tuning_based_on_bert/run_redundancy_span.py | import torch
import collections
import random
import numpy as np
import pickle
import json
import sys
import os
#---------------------------------
from tqdm import tqdm, trange
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from torch.utils.data.distributed import DistributedSa... | 35,296 | 45.321522 | 141 | py |
SPARQA | SPARQA-master/code/parsing/models/fine_tuning_based_on_bert/run_token_classifier.py | """BERT finetuning runner."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import mmap
import csv
import os
import random
from tqdm import tqdm, trange
import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader, Rando... | 21,450 | 42.867076 | 134 | py |
SPARQA | SPARQA-master/code/parsing/models/fine_tuning_based_on_bert/run_headword_span.py | """Run BERT on SQuAD."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import json
import random
import pickle
from tqdm import tqdm, trange
import numpy as np
import torch
from torch.utils.data import TensorDataset, DataLoader, RandomS... | 38,538 | 45.884428 | 141 | py |
SPARQA | SPARQA-master/code/parsing/models/fine_tuning_based_on_bert/run_joint_three_models.py | import logging
import collections
import torch
import random
import numpy as np
import pickle
from torch.utils.data.distributed import DistributedSampler
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from tqdm import tqdm, trange
import json
import sys
import os
#-------------... | 46,532 | 48.293432 | 265 | py |
SPARQA | SPARQA-master/code/parsing/models/fine_tuning_based_on_bert/span_utils.py | import collections
import mmap
from parsing.models.pytorch_pretrained_bert.tokenization import BasicTokenizer
import re
import math
def warmup_linear(x, warmup=0.002):
if x < warmup:
return x/warmup
return 1.0 - x
def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
... | 9,725 | 40.211864 | 109 | py |
SPARQA | SPARQA-master/code/parsing/models/pytorch_pretrained_bert/optimization.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
#
# 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/LICENS... | 6,785 | 41.149068 | 116 | py |
SPARQA | SPARQA-master/code/parsing/models/pytorch_pretrained_bert/__main__.py | # coding: utf8
def main():
import sys
try:
from .convert_tf_checkpoint_to_pytorch import convert_tf_checkpoint_to_pytorch
except ModuleNotFoundError:
print("pytorch_pretrained_bert can only be used from the commandline to convert TensorFlow models in PyTorch, "
"In that case, i... | 932 | 39.565217 | 137 | py |
SPARQA | SPARQA-master/code/parsing/models/pytorch_pretrained_bert/modeling.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy... | 80,774 | 49.962145 | 134 | py |
SPARQA | SPARQA-master/code/parsing/models/pytorch_pretrained_bert/file_utils.py | """
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
import os
import logging
import shutil
import tempfile
import json
from urllib.parse import urlparse
from pathlib import Path
from typing ... | 8,021 | 32.425 | 98 | py |
SPARQA | SPARQA-master/code/parsing/models/pytorch_pretrained_bert/convert_tf_checkpoint_to_pytorch.py | # coding=utf-8
# Copyright 2018 The HugginFace Inc. team.
#
# 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 applicable ... | 4,463 | 38.504425 | 101 | py |
SPARQA | SPARQA-master/code/grounding/grounding_utils.py | from common_structs.graph import Graph
from common_structs.grounded_graph import GrounedGraph
from common_structs.depth_first_paths import DepthFirstPaths
from common_structs.graph import Digragh
from common_structs.cycle import DirectedCycle
import mmap
import torch
def posword_wordlist(posword_list):
'''get word... | 9,563 | 33.652174 | 124 | py |
SPARQA | SPARQA-master/code/grounding/ranking/path_match_nn/path_match_interface.py | import torch
from torch.autograd import Variable
from common.globals_args import fn_graph_file, fn_cwq_file, kb_freebase_latest_file, kb_freebase_en_2013, q_mode as mode
from common.hand_files import read_json
from grounding.ranking.path_match_nn.parameters import get_parameters
from grounding.ranking.path_match_nn.wo... | 6,405 | 52.383333 | 152 | py |
SPARQA | SPARQA-master/code/grounding/ranking/path_match_nn/train_test_path_nn.py | # -*- coding: utf-8 -*-
import torch
import torch.optim as optim
from common.globals_args import root, fn_graph_file
from grounding.ranking.path_match_nn.sequence_loader import SeqRankingLoader
from grounding.ranking.path_match_nn.model import PathRanking
from grounding.ranking.path_match_nn.parameters import get_para... | 4,703 | 45.574257 | 114 | py |
SPARQA | SPARQA-master/code/grounding/ranking/path_match_nn/wordvec.py | import time
import torch
from grounding.grounding_args import glove_file
from grounding.grounding_utils import load_word2vec_format
class WordEmbedding():
def __init__(self):
# self.pretrained = dict()
self.pretrained = load_word2vec_format(glove_file)
self.train_generation_embedding = dic... | 1,100 | 38.321429 | 105 | py |
SPARQA | SPARQA-master/code/grounding/ranking/path_match_nn/model.py | import torch
from torch import nn
class PathRanking(nn.Module):
def __init__(self,model_parameters):
super(PathRanking, self).__init__()
self.model_parameters=model_parameters
self.fc1 = nn.Sequential(nn.Linear(self.model_parameters.max_question_word, 1))
self.fc2 = nn.Sequential(n... | 2,577 | 45.872727 | 89 | py |
SPARQA | SPARQA-master/code/grounding/ranking/path_match_nn/sequence_loader.py | import torch
from torch.autograd import Variable
class SeqRankingLoader():
def __init__(self, infile, model_parameters,device=-1):
self.pos_ques_pathsimmax_list, self.pos_path_quessimmax_list, self.pos_path_len_list,\
self.neg_ques_pathsimmax_list, self.neg_path_quessimmax_list, self.neg_path_len_... | 3,553 | 54.53125 | 141 | py |
SPARQA | SPARQA-master/code/grounding/ranking/path_match_nn/path_match_word_utils.py | import collections
import torch
from sklearn.metrics.pairwise import cosine_similarity
def get_qid_abstractquestion(any_2_1):
print(len(any_2_1))
qid_abstractquestions=collections.defaultdict(set)
for one in any_2_1:
qid=one.qid
question=one.question
for ungrounded_graph in one.ungr... | 2,622 | 37.014493 | 164 | py |
SPARQA | SPARQA-master/code/grounding/ranking/path_match_nn/preproccess_freebase.py | import os
import copy
import torch
from common.globals_args import fn_cwq_file, fn_graph_file, root, argument_parser, kb_freebase_latest_file, kb_freebase_en_2013
from common.hand_files import read_json, write_json, read_structure_file
import random
from grounding.ranking.path_match_nn.wordvec import WordEmbedding
fro... | 20,297 | 49.618454 | 202 | py |
Bleualign | Bleualign-master/bleualign/utils.py | #!/usr/bin/python
# -*- coding: utf-8 -*-
# Copyright: University of Zurich
# Author: Rico Sennrich
# For licensing information, see LICENSE
# Evaluation functions for Bleualign
from __future__ import division
from operator import itemgetter
def evaluate(options, testalign, goldalign, log_function):
goldalign ... | 6,441 | 32.552083 | 129 | py |
acrobat_submission | acrobat_submission-main/utils.py | import pandas as pd
from PIL import ImageOps
from PIL import ImageFilter
import cv2
from torch.utils.data import DataLoader
import numpy as np
from tqdm import tqdm
from torchvision import transforms
from torchvision import models
import torch
from openslide import OpenSlide
from PIL import Image
from lr_utils import W... | 17,079 | 35.340426 | 142 | py |
acrobat_submission | acrobat_submission-main/lr_utils.py | from torch.utils.data import Dataset
class WSIDataset(Dataset):
def __init__(self, df, wsi, transform, level=0, ps=256):
self.wsi = wsi
self.transform = transform
self.df = df
self.level = level
self.ps = ps
def __len__(self):
return len(self.df)
def __geti... | 629 | 27.636364 | 76 | py |
CX_GAN | CX_GAN-master/Cascaded Model/BRATS Data/implementation/Save BRATS data to Numpy Files.py | # -*- coding: utf-8 -*-
"""
Created on Thu Feb 27 09:08:06 2020
@author: ZeeshanNisar
"""
from keras.preprocessing.image import load_img, img_to_array
from tqdm import tqdm as tqdm
import os
import numpy as np
img_rows = 256
img_cols = 256
channels = 1
os.chdir('/content/drive/My Drive/GitHub Repositories')
baseDir... | 1,286 | 31.175 | 86 | py |
FFPerceptron | FFPerceptron-main/FFperceptron_MNIST.py | import torch
import time
from torchvision.datasets import MNIST
from torchvision.transforms import Compose, ToTensor, Normalize, Lambda
from torch.utils.data import DataLoader
def one_hot_encode(img0, lab):
img = img0.clone()
img[:, :10] = img0.min()
img[range(img0.shape[0]), lab] = img0.max()
return ... | 3,528 | 32.932692 | 134 | py |
hyperband | hyperband-master/main.py | #!/usr/bin/env python
"a more polished example of using hyperband"
"includes displaying best results and saving to a file"
import sys
import cPickle as pickle
from pprint import pprint
from hyperband import Hyperband
#from defs.gb import get_params, try_params
#from defs.rf import get_params, try_params
#from defs.... | 1,320 | 26.520833 | 73 | py |
hyperband | hyperband-master/defs/xgb.py | "function (and parameter space) definitions for hyperband"
"binary classification with XGBoost"
from common_defs import *
# a dict with x_train, y_train, x_test, y_test
from load_data import data
from xgboost import XGBClassifier as XGB
#
trees_per_iteration = 5
space = {
'learning_rate': hp.choice( 'lr', [
'd... | 1,710 | 20.123457 | 67 | py |
hyperband | hyperband-master/defs/keras_mlp.py | "function (and parameter space) definitions for hyperband"
"binary classification with Keras (multilayer perceptron)"
from common_defs import *
# a dict with x_train, y_train, x_test, y_test
from load_data import data
from keras.models import Sequential
from keras.layers.core import Dense, Dropout
from keras.layers.... | 4,624 | 30.678082 | 94 | py |
hyperband | hyperband-master/defs/meta.py | # meta classifier
from common_defs import *
models = ( 'xgb', 'gb', 'rf', 'xt', 'sgd', 'polylearn_fm', 'polylearn_pn', 'keras_mlp' )
# import all the functions
for m in models:
exec( "from defs.{} import get_params as get_params_{}" ).format( m, m )
exec( "from defs.{} import try_params as try_params_{}" ).format( ... | 715 | 25.518519 | 88 | py |
hyperband | hyperband-master/defs_regression/xgb.py | "function (and parameter space) definitions for hyperband"
"regression with XGBoost"
from common_defs import *
# a dict with x_train, y_train, x_test, y_test
from load_data_for_regression import data
from xgboost import XGBRegressor as XGB
#
trees_per_iteration = 5
space = {
'learning_rate': hp.choice( 'lr', [
... | 1,716 | 20.197531 | 67 | py |
hyperband | hyperband-master/defs_regression/keras_mlp.py | "function (and parameter space) definitions for hyperband"
"regression with Keras (multilayer perceptron)"
from common_defs import *
# a dict with x_train, y_train, x_test, y_test
from load_data_for_regression import data
from keras.models import Sequential
from keras.layers.core import Dense, Dropout
from keras.lay... | 4,683 | 29.815789 | 90 | py |
hyperband | hyperband-master/defs_regression/meta.py | # meta regressor
from common_defs import *
regressors = ( 'gb', 'rf', 'xt', 'sgd', 'polylearn_fm', 'polylearn_pn', 'keras_mlp' )
# import all the functions
for r in regressors:
exec( "from defs_regression.{} import get_params as get_params_{}".format( r, r ))
exec( "from defs_regression.{} import try_params as try_... | 749 | 25.785714 | 85 | py |
SfSNet-PyTorch | SfSNet-PyTorch-master/main_gen_pseudo-data.py | #
# Experiment Entry point
# 1. Trains model on Syn Data
# 2. Generates CelebA Data
# 3. Trains on Syn + CelebA Data
#
import torch
import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import torch.nn as nn
import argparse
import wandb
from data... | 6,117 | 41.783217 | 164 | py |
SfSNet-PyTorch | SfSNet-PyTorch-master/data_loading.py | import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader, random_split
from torchvision import transforms
import glob
import cv2
from random import randint
import os
from skimage import io
from PIL import Image
import pandas as pd
from utils import save_image, denorm, get_normal_in_range
im... | 10,618 | 32.498423 | 136 | py |
SfSNet-PyTorch | SfSNet-PyTorch-master/utils.py | import matplotlib.pyplot as plt
import torchvision
from PIL import Image
from torch.nn import *
def applyMask(input_img, mask):
if mask is None:
return input_img
return input_img * mask
def denorm(x):
out = (x + 1) / 2
return out.clamp(0, 1)
def get_normal_in_range(normal):
new_normal = n... | 1,681 | 26.57377 | 94 | py |
SfSNet-PyTorch | SfSNet-PyTorch-master/main_mix_training.py | #
# Experiment Entry point
# 1. Trains model on Syn Data
# 2. Generates CelebA Data
# 3. Trains on Syn + CelebA Data
#
import torch
import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import torch.nn as nn
import argparse
import wandb
from data... | 4,960 | 40 | 164 | py |
SfSNet-PyTorch | SfSNet-PyTorch-master/main_gen_synthetic_and_full.py | #
# Experiment Entry point
# 1. Trains model on Syn Data
# 2. Generates CelebA Data
# 3. Trains on Syn + CelebA Data
#
import torch
import torchvision
from torchvision import transforms
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import torch.nn as nn
import argparse
import wandb
from data... | 6,749 | 41.993631 | 164 | py |
SfSNet-PyTorch | SfSNet-PyTorch-master/models.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from utils import denorm
def get_shading(N, L):
c1 = 0.8862269254527579
c2 = 1.0233267079464883
c3 = 0.24770795610037571
c4 = 0.8580855308097834
c5 = 0.4290427654048917
nx = N[:, 0, :, :]
ny = N[:, 1, :, :]
nz = N[:, 2... | 31,228 | 50.279146 | 121 | py |
SfSNet-PyTorch | SfSNet-PyTorch-master/shading.py | import torch
import torchvision
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
from torchvision import transforms
import pandas as pd
from utils import *
from models import sfsNetShading
# def var(x):
# if torch.cuda.is_available():
# x = x.cuda()
# return Variable(x)
# ... | 5,090 | 36.160584 | 114 | py |
SfSNet-PyTorch | SfSNet-PyTorch-master/train.py | import torch
import torch.nn as nn
import numpy as np
import os
from models import *
from utils import *
from data_loading import *
## TODOS:
## 1. Dump SH in file
##
##
## Notes:
## 1. SH is not normalized
## 2. Face is normalized and denormalized - shall we not normalize in the first place?
# Enable WANDB Loggi... | 37,617 | 52.663338 | 213 | py |
SfSNet-PyTorch | SfSNet-PyTorch-master/interpolate.py | from models import *
from utils import save_image
from torch.utils.data import Dataset, DataLoader, random_split
import torchvision
from torchvision import transforms
import numpy as np
import os
import argparse
IMAGE_SIZE = 128
def interpolate(model_dir, input_path, output_path):
use_cuda = torch.cuda.is_availab... | 2,582 | 30.120482 | 89 | py |
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