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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DenseCL | DenseCL-main/openselfsup/datasets/loader/build_loader.py | import platform
import random
from functools import partial
import numpy as np
from mmcv.parallel import collate
from mmcv.runner import get_dist_info
from torch.utils.data import DataLoader
#from .sampler import DistributedGroupSampler, DistributedSampler, GroupSampler
from .sampler import DistributedSampler, Distri... | 2,752 | 32.573171 | 79 | py |
DenseCL | DenseCL-main/openselfsup/datasets/pipelines/transforms.py | import inspect
import numpy as np
from PIL import Image, ImageFilter
import torch
from torchvision import transforms as _transforms
from openselfsup.utils import build_from_cfg
from ..registry import PIPELINES
# register all existing transforms in torchvision
_EXCLUDED_TRANSFORMS = ['GaussianBlur']
for m in inspec... | 3,398 | 26.634146 | 80 | py |
DenseCL | DenseCL-main/openselfsup/hooks/extractor.py | import torch.nn as nn
from torch.utils.data import Dataset
from openselfsup.utils import nondist_forward_collect, dist_forward_collect
class Extractor(object):
"""Feature extractor.
Args:
dataset (Dataset | dict): A PyTorch dataset or dict that indicates
the dataset.
imgs_per_gpu... | 2,196 | 34.435484 | 77 | py |
DenseCL | DenseCL-main/openselfsup/hooks/validate_hook.py | from mmcv.runner import Hook
import torch
from torch.utils.data import Dataset
from openselfsup.utils import nondist_forward_collect, dist_forward_collect
from .registry import HOOKS
@HOOKS.register_module
class ValidateHook(Hook):
"""Validation hook.
Args:
dataset (Dataset | dict): A PyTorch datas... | 2,870 | 33.178571 | 77 | py |
DenseCL | DenseCL-main/openselfsup/hooks/deepcluster_hook.py | import numpy as np
from mmcv.runner import Hook
import torch
import torch.distributed as dist
from openselfsup.third_party import clustering as _clustering
from openselfsup.utils import print_log
from .registry import HOOKS
from .extractor import Extractor
@HOOKS.register_module
class DeepClusterHook(Hook):
""... | 4,637 | 36.104 | 79 | py |
DenseCL | DenseCL-main/openselfsup/utils/contextmanagers.py | # coding: utf-8
import asyncio
import contextlib
import logging
import os
import time
from typing import List
import torch
logger = logging.getLogger(__name__)
DEBUG_COMPLETED_TIME = bool(os.environ.get('DEBUG_COMPLETED_TIME', False))
@contextlib.asynccontextmanager
async def completed(trace_name='',
... | 4,103 | 32.365854 | 79 | py |
DenseCL | DenseCL-main/openselfsup/utils/optimizers.py | import torch
from torch.optim.optimizer import Optimizer, required
from torch.optim import *
class LARS(Optimizer):
r"""Implements layer-wise adaptive rate scaling for SGD.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float): b... | 4,327 | 35.991453 | 88 | py |
DenseCL | DenseCL-main/openselfsup/utils/profiling.py | import contextlib
import sys
import time
import torch
if sys.version_info >= (3, 7):
@contextlib.contextmanager
def profile_time(trace_name,
name,
enabled=True,
stream=None,
end_stream=None):
"""Print time spent by CP... | 1,363 | 32.268293 | 74 | py |
DenseCL | DenseCL-main/openselfsup/utils/collect.py | import numpy as np
import mmcv
import torch
from .gather import gather_tensors_batch
def nondist_forward_collect(func, data_loader, length):
"""Forward and collect network outputs.
This function performs forward propagation and collects outputs.
It can be used to collect results, features, losses, etc.... | 2,773 | 32.02381 | 78 | py |
DenseCL | DenseCL-main/openselfsup/utils/alias_multinomial.py | import torch
import numpy as np
class AliasMethod(object):
"""The alias method for sampling.
From: https://hips.seas.harvard.edu/blog/2013/03/03/the-alias-method-efficient-sampling-with-many-discrete-outcomes/
Args:
probs (Tensor): Sampling probabilities.
"""
def __init__(self, probs):
... | 2,132 | 27.065789 | 120 | py |
DenseCL | DenseCL-main/openselfsup/utils/gather.py | import numpy as np
import torch
import torch.distributed as dist
def gather_tensors(input_array):
world_size = dist.get_world_size()
## gather shapes first
myshape = input_array.shape
mycount = input_array.size
shape_tensor = torch.Tensor(np.array(myshape)).cuda()
all_shape = [
torch.... | 2,629 | 36.571429 | 100 | py |
DenseCL | DenseCL-main/openselfsup/utils/collect_env.py | import os.path as osp
import subprocess
import sys
from collections import defaultdict
import cv2
import mmcv
import torch
import torchvision
import openselfsup
def collect_env():
"""Collect the information of the running environments."""
env_info = {}
env_info['sys.platform'] = sys.platform
env_inf... | 2,055 | 30.630769 | 81 | py |
DenseCL | DenseCL-main/openselfsup/utils/flops_counter.py | # Modified from flops-counter.pytorch by Vladislav Sovrasov
# original repo: https://github.com/sovrasov/flops-counter.pytorch
# MIT License
# Copyright (c) 2018 Vladislav Sovrasov
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (th... | 14,304 | 31.146067 | 79 | py |
findiff | findiff-master/docs/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# findiff documentation build configuration file, created by
# sphinx-quickstart on Sun Apr 8 10:18:43 2018.
#
# 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
# au... | 5,719 | 31.5 | 83 | py |
ChRIS_ultron_backEnd | ChRIS_ultron_backEnd-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# chris ultron backend documentation build configuration file, created by
# sphinx-quickstart on Thu Jun 16 09:24:54 2016.
#
# 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
# autogenerate... | 12,140 | 27.039261 | 81 | py |
waymo-motion-prediction-2021 | waymo-motion-prediction-2021-main/submit.py | import argparse
# chage this if you have problem
import sys
sys.path.insert(1, "~/.local/lib/python3.6/site-packages")
import numpy as np
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from submission_proto import motion_submission_pb2
from train import WaymoLoader, Model
def parse_arg... | 4,129 | 31.519685 | 88 | py |
waymo-motion-prediction-2021 | waymo-motion-prediction-2021-main/train.py | import argparse
import os
import numpy as np
import timm
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
IMG_RES = 224
IN_CHANNELS = 25
TL = 80
N_TRAJS = 6
def parse_args():
parser = argparse.ArgumentPars... | 9,943 | 28.247059 | 104 | py |
waymo-motion-prediction-2021 | waymo-motion-prediction-2021-main/visualize.py | import argparse
import os
import numpy as np
import torch
from matplotlib import pyplot as plt
from matplotlib.pyplot import figure
from torch.utils.data import DataLoader
from train import WaymoLoader, pytorch_neg_multi_log_likelihood_batch
def parse_args():
parser = argparse.ArgumentParser()
parser.add_ar... | 3,621 | 30.495652 | 79 | py |
DBPMaN | DBPMaN-main/script/utils.py | import tensorflow as tf
from tensorflow.python.ops.rnn_cell import *
# from tensorflow.python.ops.rnn_cell_impl import _Linear
from tensorflow_core.contrib.rnn.python.ops.core_rnn_cell import _Linear
# from tensorflow import keras
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import init_ops
fr... | 18,432 | 40.329596 | 165 | py |
3D-ANAS | 3D-ANAS-main/tools/infe.py | """
Searching script
"""
import argparse
import os
import h5py
import json
import torch
import sys
import PIL.Image as Image
import numpy as np
sys.path.append('..')
from one_stage_nas.config import cfg
from one_stage_nas.utils.misc import mkdir
from one_stage_nas.modeling.architectures import build_model
from one_sta... | 8,831 | 36.265823 | 119 | py |
3D-ANAS | 3D-ANAS-main/tools/search.py | """
Searching script
"""
import argparse
import torch
import os
import sys
sys.path.append('..')
from one_stage_nas.config import cfg
from one_stage_nas.data import build_dataset
from one_stage_nas.solver import make_lr_scheduler
from one_stage_nas.solver import make_optimizer
from one_stage_nas.engine.searcher import ... | 3,186 | 27.455357 | 113 | py |
3D-ANAS | 3D-ANAS-main/tools/train.py | """
Searching script
"""
import argparse
from tensorboardX import SummaryWriter
import torch
import os
import sys
sys.path.append('..')
from one_stage_nas.config import cfg
from one_stage_nas.data import build_dataset
from one_stage_nas.solver import make_lr_scheduler
from one_stage_nas.solver import make_optimizer
f... | 3,442 | 26.325397 | 87 | py |
ODE4RobustViT | ODE4RobustViT-master/attack.py | import torch
import torch.nn.functional as F
import argparse
from attack_model import *
from attack_model.util import *
from build_model.util import *
def main():
parser = argparse.ArgumentParser(description='attacking the trained neural network')
# choose the model
parser.add_argument('--net_name'... | 5,363 | 46.892857 | 148 | py |
ODE4RobustViT | ODE4RobustViT-master/analyze.py | import torch
import argparse
from build_model.util import *
from analyze_model.util import *
from analyze_model import *
def main():
parser = argparse.ArgumentParser(description='analysis the trained neural network')
# choose the model
parser.add_argument('--net_name', type=str, default='vit',
... | 3,843 | 43.697674 | 99 | py |
ODE4RobustViT | ODE4RobustViT-master/build.py | import torch
import math
import argparse
import torch.nn.functional as F
from build_model.util import *
from build_model import *
def main():
parser = argparse.ArgumentParser(description='Training vits and covits')
# the class of the model, e.g., vit or covit
parser.add_argument('--net_name', type=str... | 5,274 | 50.715686 | 143 | py |
ODE4RobustViT | ODE4RobustViT-master/build_model/learner.py | import torch
from torch.utils.data.dataloader import DataLoader
import pandas as pd
import os
from tqdm import tqdm
from pathlib import Path
from build_model.model_zoo import *
from build_model.optimizers import *
from build_model.util import *
class Learner:
def __init__(self, named_dataset, named_network, \... | 12,332 | 46.434615 | 145 | py |
ODE4RobustViT | ODE4RobustViT-master/build_model/model_zoo/vit_homemade.py | import torch
import torch.nn.functional as F
from torch import nn
from torch import Tensor
from einops import rearrange, repeat
from einops.layers.torch import Rearrange, Reduce
class Embedding(nn.Module):
'''
Embedding:
Input: img(B, C, H, W)
Output: img(B, N+1, Em), Conv2D(in_channel=C, out... | 5,352 | 34.217105 | 118 | py |
ODE4RobustViT | ODE4RobustViT-master/build_model/model_zoo/covit.py | import torch
from torch import nn
from torch import Tensor
from einops import rearrange, repeat
from einops.layers.torch import Rearrange, Reduce
class Embedding(nn.Module):
'''
Embedding:
Input: img(B, C, H, W)
Output: img(B, N+1, Em), Conv2D(in_channel=C, out_channel=Em_size, kernel_size=pa... | 5,541 | 34.525641 | 118 | py |
ODE4RobustViT | ODE4RobustViT-master/build_model/util/utils.py | import torch
from torchvision import transforms, datasets
from build_model.model_zoo import *
from build_model.optimizers import *
def get_dataset(args):
transform_train=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, padding=4),
transforms.ColorJitter(brightness=0.... | 6,310 | 37.248485 | 143 | py |
ODE4RobustViT | ODE4RobustViT-master/build_model/optimizers/opt_sam.py | # take away from https://github.com/davda54/sam
# from the paper "WHEN VISION TRANSFORMERS OUTPERFORM RESNETS WITHOUT PRE-TRAINING OR STRONG DATA AUGMENTATIONS"
import torch
class SAM(torch.optim.Optimizer):
def __init__(self, params, base_optimizer, rho=0.05, adaptive=False, **kwargs):
assert rho >= 0.0... | 2,588 | 38.227273 | 131 | py |
ODE4RobustViT | ODE4RobustViT-master/attack_model/attack_ensembles.py | import torch
from torch.utils.data import random_split
from torch.utils.data.dataloader import DataLoader
from torchvision.utils import save_image
import pandas as pd
import os
import math
from tqdm import tqdm
from pathlib import Path
from attack_model.util import *
class Attack_Ensembles():
def __init__(self... | 4,976 | 38.816 | 134 | py |
ODE4RobustViT | ODE4RobustViT-master/attack_model/util/utils.py | from torchattacks import PGD, FGSM, PGDL2, AutoAttack, CW
# get attack
def get_attack(net, args):
if args.att_method == 'fgsm':
attack = FGSM(net, args.epsilon)
annotation = f'{args.att_method}_eps({args.epsilon:.2f})'
elif args.att_method == 'pgd':
if args.att_norm == 'Linf':
... | 1,555 | 32.826087 | 113 | py |
ODE4RobustViT | ODE4RobustViT-master/analyze_model/analysis.py | import os
import pandas as pd
from tqdm import tqdm
from pathlib import Path
import torch.nn as nn
from torch.utils.data import random_split
from torch.utils.data.dataloader import DataLoader
from analyze_model.util import *
class DictInNode:
def __init__(self, dict_node: dict={}):
self.dict_node =... | 6,785 | 37.556818 | 172 | py |
ODE4RobustViT | ODE4RobustViT-master/analyze_model/util/utils.py | import torch
from tqdm import tqdm
def max_sv_compute(func, x):
_x = x.detach().clone().requires_grad_()
jacob = torch.autograd.functional.jacobian(func, _x)
jacob_dim = _x[0].shape[0]*_x[0].shape[1]
jacob = jacob.reshape([jacob_dim,jacob_dim])
# calculate the singular value
svdvals = t... | 1,082 | 23.613636 | 106 | py |
DRAGONS | DRAGONS-master/geminidr/interactive/interactive.py | import re
from abc import ABC, abstractmethod
from copy import copy
from enum import Enum, auto
from functools import cmp_to_key
from bokeh.core.property.instance import Instance
from bokeh.layouts import column, row
from bokeh.models import (BoxAnnotation, Button, CustomJS, NumeralTickFormatter, Slider, TextInput, Di... | 67,390 | 37.708214 | 163 | py |
DRAGONS | DRAGONS-master/geminidr/doc/tutorials/GMOSImg-DRTutorial/conf.py | #
# 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 --------------------------------------------------------------
# If extensions (or ... | 10,057 | 36.670412 | 177 | py |
DRAGONS | DRAGONS-master/geminidr/doc/tutorials/GSAOIImg-DRTutorial/conf.py | #
# 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 --------------------------------------------------------------
# If extensions (or ... | 9,624 | 36.019231 | 177 | py |
DRAGONS | DRAGONS-master/geminidr/doc/tutorials/F2Img-DRTutorial/conf.py | #
# 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 --------------------------------------------------------------
# If extensions (or ... | 9,937 | 36.360902 | 177 | py |
LALNets | LALNets-master/setup.py | from setuptools import setup
from setuptools import find_packages
setup(name='lalnets',
version='0.1.3',
description='Ozsel Kilinc LALNets',
author='Ozsel Kilinc',
author_email='ozselkilinc@gmail.com',
install_requires = ['keras==1.2.1',
'sklearn', 'pandas', 'nu... | 382 | 28.461538 | 79 | py |
LALNets | LALNets-master/lalnets/acol/initializations.py | from __future__ import absolute_import, division
import numpy as np
from keras import backend as K
from keras.utils.generic_utils import get_from_module
def identity_vstacked(shape, scale=1, name=None, dim_ordering='th'):
scale = shape[1]/float(shape[0])
a = np.identity(shape[1])
for i in range(1, int(1/sc... | 1,948 | 33.803571 | 75 | py |
LALNets | LALNets-master/lalnets/acol/regularizers.py | from __future__ import absolute_import
import numpy as np
from keras import backend as K
from keras.regularizers import Regularizer
from keras.utils.generic_utils import get_from_module
from lalnets.acol.initializations import column_vstacked
import warnings
Tr = K.theano.tensor.nlinalg.trace
Diag = K.theano.tensor.nl... | 7,658 | 31.871245 | 106 | py |
LALNets | LALNets-master/lalnets/acol/models.py | '''
Model generator for ACOL experiments.
'''
from keras.models import Sequential
from keras.engine.topology import InputLayer
from keras.layers.core import Dense, Dropout, Activation, Flatten, Layer
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.constraints import maxnorm
from lalnet... | 5,911 | 42.470588 | 124 | py |
LALNets | LALNets-master/lalnets/acol/trainings.py | import numpy as np
import time
from keras import backend as K
from keras.engine import Model
from keras.utils import np_utils, generic_utils
from lalnets.commons.utils import calculate_cl_acc, cumulate_metrics, choose_samples
'''
Functions to train and evaluate ACOL experiments.
'''
def train_with_parents(nb_parent... | 39,761 | 39.614913 | 123 | py |
LALNets | LALNets-master/lalnets/acol/layers/pooling.py | # -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
import numpy as np
import copy
import inspect
import types as python_types
import warnings
from keras import backend as K
from keras.engine import InputSpec
from keras.engine import Layer
from keras import activations
fro... | 13,687 | 41.116923 | 120 | py |
LALNets | LALNets-master/lalnets/examples/gar_norb.py | from __future__ import print_function
from keras.optimizers import SGD
from lalnets.commons.datasets import load_norb
from lalnets.commons.utils import sample_wise_center_norm
from lalnets.acol.models import define_cnn
from lalnets.acol.trainings import train_semisupervised
import scipy as sc
import numpy as np
imp... | 4,308 | 34.61157 | 98 | py |
LALNets | LALNets-master/lalnets/examples/gar_mnist.py | from __future__ import print_function
from keras.optimizers import SGD
from lalnets.commons.datasets import load_mnist
from lalnets.acol.models import define_cnn
from lalnets.acol.trainings import train_semisupervised
import scipy as sc
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
i... | 3,207 | 32.072165 | 92 | py |
LALNets | LALNets-master/lalnets/examples/pseudo_svhn.py | from __future__ import print_function
from keras.optimizers import SGD
from keras.optimizers import Adam
from keras import backend as K
from lalnets.commons.datasets import load_svhn
from lalnets.commons.visualizations import plot_class_means
from lalnets.commons.utils import *
from la... | 6,555 | 31.616915 | 119 | py |
LALNets | LALNets-master/lalnets/examples/gar_svhn.py | from __future__ import print_function
from keras.optimizers import SGD
from lalnets.commons.datasets import load_svhn
from lalnets.commons.utils import sample_wise_center_norm
from lalnets.acol.models import define_cnn
from lalnets.acol.trainings import train_semisupervised
import scipy as sc
import numpy as np
imp... | 3,543 | 33.745098 | 101 | py |
LALNets | LALNets-master/lalnets/commons/datasets.py | from keras.datasets import mnist
from sklearn.datasets import fetch_rcv1
from lalnets.metagenome.preprocessing import *
import numpy as np
import scipy.io as sio
import pandas as pd
def load_mnist(order='th'):
# input image dimensions
img_rows, img_cols, img_channels, = 28, 28, 1
if order == 'tf':
... | 6,898 | 32.168269 | 121 | py |
LALNets | LALNets-master/lalnets/robustness/models.py | '''
Model generator for robustness experiments.
'''
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten, Layer
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.constraints import maxnorm
def define_cnn(input_shape, nb_classes, cnn_type=... | 2,361 | 37.721311 | 96 | py |
LALNets | LALNets-master/lalnets/robustness/test_robustness.py | '''
Reproduces the results of
"Clustering-based Source-aware Assessment of True Robustness for Learning Models"
for tested neural network models. Reproduced results might be slightly different due to
seleceted seed of random generator.
'''
from __future__ import print_function
from keras.datasets import mnist
from... | 8,169 | 35.968326 | 116 | py |
gopt | gopt-master/src/traintest.py | # -*- coding: utf-8 -*-
# @Time : 9/20/21 12:02 PM
# @Author : Yuan Gong
# @Affiliation : Massachusetts Institute of Technology
# @Email : yuangong@mit.edu
# @File : traintest.py
# train and test the models
import sys
import os
import time
from torch.utils.data import Dataset, DataLoader
sys.path.append(os.... | 17,789 | 44.382653 | 182 | py |
gopt | gopt-master/src/models/gopt.py | # -*- coding: utf-8 -*-
# @Time : 10/22/21 1:23 PM
# @Author : Yuan Gong
# @Affiliation : Massachusetts Institute of Technology
# @Email : yuangong@mit.edu
# @File : gopt.py
# attention part is borrowed from the timm package.
import math
import warnings
import torch
import torch.nn as nn
import numpy as np
... | 13,247 | 41.461538 | 122 | py |
gopt | gopt-master/src/models/baseline.py | # -*- coding: utf-8 -*-
# @Time : 1/8/22 2:27 AM
# @Author : Yuan Gong
# @Affiliation : Massachusetts Institute of Technology
# @Email : yuangong@mit.edu
# @File : baseline.py
import math
import warnings
import torch
import torch.nn as nn
import numpy as np
class BaselineLSTM(nn.Module):
def __init__(se... | 3,556 | 38.966292 | 114 | py |
gopt | gopt-master/pretrained_models/load_model.py | # -*- coding: utf-8 -*-
# @Time : 11/16/21 5:00 PM
# @Author : Yuan Gong
# @Affiliation : Massachusetts Institute of Technology
# @Email : yuangong@mit.edu
# @File : load_model.py
# sample code of loading a pretrained GOPT model
import torch
import sys
import os
sys.path.append(os.path.abspath('../src/'))
f... | 714 | 34.75 | 121 | py |
DWIE | DWIE-master/src/model_train.py | import argparse
import json
import os
import time
from pathlib import Path
import torch
from tensorboard_logger import Logger as TBLogger
from torch.utils.data import DataLoader
from tqdm import tqdm
from model.data.data_reader import DatasetCPN
from model.data.dictionary import create_dictionaries
from model.models ... | 13,824 | 35.381579 | 120 | py |
DWIE | DWIE-master/src/model_predict.py | import argparse
import json
import os
from pathlib import Path
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from model.data.dictionary import create_dictionaries
from model_train import create_datasets, create_model
from model.training import settings
if __name__ == '__main__':
# p... | 2,397 | 31.405405 | 104 | py |
DWIE | DWIE-master/src/model/models/rel_scorer.py | import torch
import torch.nn as nn
from model.models.misc.misc import MyGate, overwrite_spans
from model.models.misc.span_pair_scorers import OptFFpairs
def relation_add_scores(relation_scores, filtered_prune_scores):
scores_left = filtered_prune_scores
scores_right = filtered_prune_scores.squeeze(-1).unsque... | 5,171 | 42.1 | 113 | py |
DWIE | DWIE-master/src/model/models/ner.py | import torch
import torch.nn as nn
from model.metrics.f1 import MetricSpanNER
from model.metrics.objective import MetricObjective
from model.models.misc.nnets import FeedForward
def create_all_spans(batch_size, length, width):
b = torch.arange(length, dtype=torch.long)
w = torch.arange(width, dtype=torch.lon... | 3,548 | 36.357895 | 113 | py |
DWIE | DWIE-master/src/model/models/coref_loss.py | import torch
import torch.nn as nn
from model.metrics.coref import MetricCoref, MetricCorefAverage
from model.metrics.corefx import MetricCorefExternal
from model.metrics.objective import MetricObjective
from model.models.misc.misc import get_mask_from_sequence_lengths, m2i_to_clusters, decode_m2i
def logsumexp(tens... | 7,205 | 38.812155 | 127 | py |
DWIE | DWIE-master/src/model/models/coref_scorer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from model.models.misc.misc import MyGate, overwrite_spans
from model.models.misc.span_pair_scorers import OptFFpairs
def coref_add_scores(coref_scores, filtered_prune_scores):
scores_left = filtered_prune_scores
scores_right = filtered_prune... | 3,308 | 38.86747 | 111 | py |
DWIE | DWIE-master/src/model/models/pruner.py | import torch
import torch.nn as nn
from model.models.misc.misc import batched_index_select, get_mask_from_sequence_lengths
def indices_to_spans(top_indices, span_lengths, max_span_width):
b = top_indices // max_span_width
w = top_indices % max_span_width
e = b + w
return [list(zip(b[i, 0:length].toli... | 6,522 | 42.198675 | 118 | py |
DWIE | DWIE-master/src/model/models/main_model.py | import torch
import torch.nn as nn
from model.data.predictions_serializer import convert_to_json
from model.models.att_prop import ModuleAttentionProp
from model.models.coref_loss import LossCoref
from model.models.coref_scorer import ModuleCorefScorer, ModuleCorefBasicScorer
from model.models.embedders.span_embedder ... | 10,889 | 39.036765 | 119 | py |
DWIE | DWIE-master/src/model/models/rel_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from model.metrics.objective import MetricObjective
from model.metrics.relations import MetricConceptRelationSoftF1, MetricConceptRelationToMentionsF1, MetricSpanRelationF1x
from model.models.misc.misc import get_mask_from_sequence_lengths
def create... | 18,395 | 41.981308 | 121 | py |
DWIE | DWIE-master/src/model/models/att_prop.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from model.models.misc.misc import MyGate, overwrite_spans
from model.models.misc.span_pair_scorers import create_pair_scorer
class ModuleAttentionProp(nn.Module):
def __init__(self, dim_span, coref_pruner, span_pair_generator, config):
... | 1,732 | 37.511111 | 111 | py |
DWIE | DWIE-master/src/model/models/misc/nnets.py | import torch
import torch.nn as nn
import torch.nn.utils.rnn as rnn_utils
class CNNMaxpool(nn.Module):
def __init__(self, dim_input, config):
super(CNNMaxpool, self).__init__()
self.cnns = nn.ModuleList([nn.Conv1d(dim_input, config['dim'], k) for k in config['kernels']])
self.dim_output =... | 7,975 | 32.940426 | 115 | py |
DWIE | DWIE-master/src/model/models/misc/misc.py | from typing import Optional
import torch
import torch.nn as nn
def decode_m2i(scores, lengths):
output = []
for b, length in enumerate(lengths.tolist()):
m2i = list(range(length))
if length > 0:
_, indices = torch.max(scores[b, 0:length, :], -1)
for src, dst in enumera... | 8,718 | 38.098655 | 106 | py |
DWIE | DWIE-master/src/model/models/misc/span_pair_scorers.py | import torch
import torch.nn as nn
def create_pair_scorer(dim_input, dim_output, config, span_pair_generator):
scorer_type = config.get('scorer_type', 'opt-ff-pairs')
if scorer_type == 'ff-pairs':
return FFpairs(dim_input, dim_output, config, span_pair_generator)
elif scorer_type == 'opt-ff-pairs... | 3,485 | 36.085106 | 114 | py |
DWIE | DWIE-master/src/model/models/misc/collate.py | import numpy as np
import torch
import torch.nn.utils.rnn as rnn_utils
def collate_character(batch, maxlen, padding, min_word_len=0):
seqlens = [len(x) for x in batch]
max_word_len = max([len(w) for sentence in batch for w in sentence])
maxlen = min(maxlen, max_word_len)
maxlen = max(maxlen, min_word_... | 3,268 | 37.916667 | 116 | py |
DWIE | DWIE-master/src/model/models/embedders/span_embedder.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from model.models.misc.misc import batched_index_select
from model.models.misc.nnets import FeedForward
def bucket_values(distances: torch.Tensor,
num_identity_buckets: int = 4,
num_total_buckets: int ... | 8,788 | 37.889381 | 121 | py |
DWIE | DWIE-master/src/model/models/embedders/text_embedder.py | import numpy as np
import torch
import torch.nn as nn
from transformers import *
from model.data.embeddings_loader import load_wordembeddings_words, load_wordembeddings_with_random_unknowns, \
load_wordembeddings
from model.models.misc.nnets import CNNMaxpool
from model.training import settings
class TextFieldEm... | 7,987 | 37.403846 | 115 | py |
DWIE | DWIE-master/src/model/data/embeddings_loader.py | import gzip
import numpy as np
import torch
def load_wordembeddings(filename, accept={}, dim=300, out_of_voc_vector=None):
embedding_matrix = np.zeros((len(accept), dim))
if out_of_voc_vector is not None:
print('WARNING: initialize word embeddings with ', out_of_voc_vector)
embedding_matrix =... | 3,477 | 33.78 | 116 | py |
DWIE | DWIE-master/src/model/data/data_reader.py | import json
import os
import random
from collections import Counter
import torch
from torch.utils.data import Dataset
from tqdm import tqdm
from model.data.tokenizer import TokenizerCPN
class DatasetCPN(Dataset):
def __init__(self, name, config, dictionaries):
self.name = name
self.tokenize = c... | 9,291 | 36.01992 | 120 | py |
Guyu | Guyu-master/inference.py | import torch
from torch import nn
import torch.nn.functional as F
import random
import numpy as np
import copy
import time
from biglm import BIGLM
from data import Vocab, DataLoader, s2t
mstime = lambda: int(round(time.time() * 1000))
def init_model(m_path, device, vocab):
ckpt= torch.load(m_path, map_locat... | 9,977 | 28.963964 | 152 | py |
Guyu | Guyu-master/transformer_postln.py | import torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
from utils import gelu, LayerNorm, get_incremental_state, set_incremental_state
import math
class TransformerLayer(nn.Module):
def __init__(self, embed_dim, ff_embed_dim, num_heads, dropout, with_external=False, ... | 13,138 | 38.694864 | 175 | py |
Guyu | Guyu-master/utils.py | import torch
from torch import nn
from torch.nn import Parameter
from collections import defaultdict
import math
def gelu(x):
cdf = 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
return cdf*x
class LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
super(LayerNorm, self).__init__()
... | 1,985 | 33.842105 | 86 | py |
Guyu | Guyu-master/data.py | import random
import torch
import numpy as np
PAD, UNK, BOS, EOS = '<pad>', '<unk>', '<bos>', '<eos>'
LS, RS, SP = '<s>', '</s>', ' '
BUFSIZE = 4096000
def ListsToTensor(xs, vocab=None):
max_len = max(len(x) for x in xs)
ys = []
for x in xs:
if vocab is not None:
y = vocab.token2idx(x)... | 3,838 | 29.468254 | 100 | py |
Guyu | Guyu-master/label_smoothing.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class LabelSmoothing(nn.Module):
"Implement label smoothing."
def __init__(self, device, size, padding_idx, label_smoothing=0.0):
super(LabelSmoothing, self).__init__()
assert 0.0 < label_smoothing <= 1.0
self.padding_id... | 1,257 | 34.942857 | 105 | py |
Guyu | Guyu-master/api.py | import torch
from torch import nn
import torch.nn.functional as F
import random
import numpy as np
import copy
import logging
from inference import *
from flask import Flask,request
app = Flask(__name__)
logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(filename)s[line:%(lineno)d] - %(levelname)s:... | 1,028 | 23.5 | 123 | py |
Guyu | Guyu-master/biglm.py | import torch
from torch import nn
import torch.nn.functional as F
from utils import gelu, LayerNorm
from transformer_postln import TransformerLayer, Embedding, LearnedPositionalEmbedding, SelfAttentionMask
# more than 12 layers
#from transformer_preln import TransformerLayer, Embedding, LearnedPositionalEmbedding, Sel... | 6,286 | 39.56129 | 147 | py |
Guyu | Guyu-master/adam.py | # coding=utf-8
import torch
from torch.optim import Optimizer
class AdamWeightDecayOptimizer(Optimizer):
"""A basic Adam optimizer that includes "correct" L2 weight decay.
https://github.com/google-research/bert/blob/master/optimization.py
https://raw.githubusercontent.com/pytorch/pytorch/v1.0.0/torch/opti... | 4,134 | 45.988636 | 116 | py |
Guyu | Guyu-master/transformer_preln.py | import torch
from torch import nn
from torch.nn import Parameter
import torch.nn.functional as F
from utils import gelu, LayerNorm, get_incremental_state, set_incremental_state
import math
class TransformerLayer(nn.Module):
def __init__(self, embed_dim, ff_embed_dim, num_heads, dropout, with_external=False, ... | 13,212 | 38.324405 | 175 | py |
Guyu | Guyu-master/train.py | # coding=utf-8
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torch.multiprocessing as mp
from biglm import BIGLM
from data import Vocab, DataLoader, s2xy
from adam import AdamWeightDecayOptimizer
from optim import Optim
import argparse, os
import random
de... | 7,395 | 36.734694 | 176 | py |
Guyu | Guyu-master/chat-bot/inference_.py | import sys
sys.path.append("../")
import torch
from torch import nn
import torch.nn.functional as F
import random
import numpy as np
import copy
import time
from inference import *
mstime = lambda: int(round(time.time() * 1000))
device = 0
print("loading...")
device = 0
m_path = "./ckpt/epoch0_batch_3999"
v_path... | 1,319 | 25.4 | 68 | py |
Guyu | Guyu-master/chat-bot/data_.py | import random
import torch
import numpy as np
PAD, UNK, BOS, EOS = '<pad>', '<unk>', '<bos>', '<eos>'
BUFSIZE = 100000
def ListsToTensor(xs, vocab=None):
max_len = max(len(x) for x in xs)
ys = []
for x in xs:
if vocab is not None:
y = vocab.token2idx(x) + [vocab.padding_idx]*(max_len -... | 3,104 | 29.441176 | 102 | py |
Guyu | Guyu-master/chat-bot/train.py | # coding=utf-8
import argparse, os
import random
import sys
sys.path.append("../")
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
import torch.multiprocessing as mp
from biglm import BIGLM
from data import Vocab
from data_ import DataLoader, s2xy
from optim import O... | 7,361 | 37.34375 | 176 | py |
grafter | grafter-main/grafter/train/ppo.py | # docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppopy
import argparse
import os
import random
import time
from distutils.util import strtobool
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.distributions.categorical imp... | 16,770 | 44.327027 | 141 | py |
Hanabi-Map-Elites | Hanabi-Map-Elites-master/bin/starter/src/main/java/NeuralNetworkControllers/neuroevolution.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
import ffnn
def main():
net = ffnn.Net()
net.print_parameters()
# print(net)
# net.print_parameters()
# print("!!!!!!!!!!!!!")
# net.print_parameters()
# inpu... | 2,392 | 17.128788 | 80 | py |
Hanabi-Map-Elites | Hanabi-Map-Elites-master/bin/starter/src/main/java/NeuralNetworkControllers/ffnn.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
from torch.nn.parameter import Parameter
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(5,5)
self.fc1.weight.requires_grad=False
self.fc1.... | 2,588 | 25.418367 | 92 | py |
Hanabi-Map-Elites | Hanabi-Map-Elites-master/bin/starter/src/main/java/NeuralNetworkControllers/ffnn2.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(5,5)
self.fc1.weight.requires_grad=False
self.fc1.bias.requir... | 4,752 | 21.526066 | 92 | py |
Hanabi-Map-Elites | Hanabi-Map-Elites-master/starter/src/main/java/NeuralNetworkControllers/neuroevolution.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
import ffnn
def main():
net = ffnn.Net()
net.print_parameters()
# print(net)
# net.print_parameters()
# print("!!!!!!!!!!!!!")
# net.print_parameters()
# inpu... | 2,392 | 17.128788 | 80 | py |
Hanabi-Map-Elites | Hanabi-Map-Elites-master/starter/src/main/java/NeuralNetworkControllers/ffnn.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
from torch.nn.parameter import Parameter
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(5,5)
self.fc1.weight.requires_grad=False
self.fc1.... | 2,588 | 25.418367 | 92 | py |
Hanabi-Map-Elites | Hanabi-Map-Elites-master/starter/src/main/java/NeuralNetworkControllers/ffnn2.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(5,5)
self.fc1.weight.requires_grad=False
self.fc1.bias.requir... | 4,752 | 21.526066 | 92 | py |
ZenNAS | ZenNAS-main/val.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
'''
Usage:
python val.py --gpu 0 --arch zennet_imagenet1k_latency02ms_res192
'''
import os, sys, argparse, math, PIL
import torch
from torchvision import transforms, datasets
import ZenNet
imagenet_data_dir = os.path.expanduser('~/data/imagenet')
def acc... | 4,581 | 37.504202 | 136 | py |
ZenNAS | ZenNAS-main/ts_train_image_classification.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import os, sys, copy, time, logging, argparse
import torch
import torch.nn.functional as F
from torch import nn
import numpy as np
try:
import horovod.torch as hvd
except ImportError:
print('fail to import hvd.')
try:
from apex.parallel imp... | 38,943 | 38.179074 | 131 | py |
ZenNAS | ZenNAS-main/val_cifar.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
'''
Usage:
python val_cifar.py --dataset cifar10 --gpu 0 --arch zennet_cifar10_model_size05M_res32
'''
import os, sys, argparse, math, PIL
import torch
from torchvision import transforms, datasets
import ZenNet
cifar10_data_dir = '~/data/pytorch_cifar10'
... | 4,648 | 37.106557 | 136 | py |
ZenNAS | ZenNAS-main/Masternet.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import numpy as np
import torch, argparse
from torch import nn
import torch.nn.functional as F
import PlainNet
from PlainNet import parse_cmd_options, _create_netblock_list_from_st... | 6,599 | 35.263736 | 128 | py |
ZenNAS | ZenNAS-main/benchmark_network_latency.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import os,sys, argparse
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import ModelLoader, global_utils
import train_image_classification as tic
import torch, time
import numpy as np
def __get_latency__(model, batch_size, r... | 4,947 | 34.597122 | 116 | py |
ZenNAS | ZenNAS-main/evolution_search.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import os, sys
sys.path.append(os.path.dirname(os.path.abspath(__file__)))
import argparse, random, logging, time
import torch
from torch import nn
import numpy as np
import global_utils
import Masternet
import PlainNet
# from tqdm import tqdm
from Zero... | 13,341 | 45.814035 | 158 | py |
ZenNAS | ZenNAS-main/train_image_classification.py | '''
Copyright (C) 2010-2021 Alibaba Group Holding Limited.
'''
import os, sys, copy, time, logging
import torch
import torch.nn.functional as F
from torch import nn
import numpy as np
try:
import horovod.torch as hvd
except ImportError:
print('fail to import hvd.')
try:
from apex.parallel import Distri... | 30,678 | 37.396746 | 131 | py |
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