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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|---|---|---|---|---|---|---|
CD-SGD | CD-SGD-master/example/fcn-xs/init_fcnxs.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 4,639 | 42.364486 | 97 | py |
CD-SGD | CD-SGD-master/example/fcn-xs/image_segmentaion.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 4,504 | 35.04 | 97 | py |
CD-SGD | CD-SGD-master/example/fcn-xs/data.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 6,068 | 42.35 | 139 | py |
CD-SGD | CD-SGD-master/example/fcn-xs/solver.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 7,192 | 48.951389 | 116 | py |
CD-SGD | CD-SGD-master/example/fcn-xs/fcn_xs.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 3,773 | 40.472527 | 99 | py |
CD-SGD | CD-SGD-master/example/restricted-boltzmann-machine/binary_rbm_gluon.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 7,113 | 49.098592 | 170 | py |
CD-SGD | CD-SGD-master/example/restricted-boltzmann-machine/binary_rbm_module.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 7,894 | 44.901163 | 170 | py |
CD-SGD | CD-SGD-master/example/restricted-boltzmann-machine/binary_rbm.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 12,873 | 49.685039 | 172 | py |
CD-SGD | CD-SGD-master/example/numpy-ops/custom_sparse_sqr.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 4,291 | 34.471074 | 100 | py |
CD-SGD | CD-SGD-master/example/numpy-ops/weighted_logistic_regression.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 3,461 | 38.793103 | 103 | py |
CD-SGD | CD-SGD-master/example/numpy-ops/custom_softmax_rtc.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 6,868 | 38.028409 | 137 | py |
CD-SGD | CD-SGD-master/example/numpy-ops/custom_softmax.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 3,190 | 34.455556 | 79 | py |
CD-SGD | CD-SGD-master/example/vae-gan/convert_data.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 3,581 | 33.776699 | 187 | py |
CD-SGD | CD-SGD-master/example/vae-gan/vaegan_mxnet.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 31,039 | 40.945946 | 334 | py |
CD-SGD | CD-SGD-master/benchmark/python/sparse/dot.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 20,888 | 43.730193 | 123 | py |
CD-SGD | CD-SGD-master/benchmark/python/sparse/memory_benchmark.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 3,792 | 39.351064 | 100 | py |
CD-SGD | CD-SGD-master/benchmark/python/sparse/updater.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 2,695 | 33.126582 | 88 | py |
CD-SGD | CD-SGD-master/benchmark/python/sparse/sparse_end2end.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 12,675 | 40.155844 | 128 | py |
CD-SGD | CD-SGD-master/benchmark/python/sparse/cast_storage.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 3,691 | 35.92 | 107 | py |
CD-SGD | CD-SGD-master/benchmark/python/sparse/sparse_op.py | from __future__ import print_function
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Versio... | 9,244 | 36.278226 | 117 | py |
CD-SGD | CD-SGD-master/benchmark/python/control_flow/rnn.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 5,160 | 34.593103 | 113 | py |
CD-SGD | CD-SGD-master/benchmark/python/gluon/benchmark_gluon.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 7,754 | 46 | 113 | py |
CD-SGD | CD-SGD-master/benchmark/python/quantization/benchmark_op.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 5,279 | 57.021978 | 117 | py |
CD-SGD | CD-SGD-master/ci/build_windows.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache Licen... | 8,328 | 33.995798 | 118 | py |
CD-SGD | CD-SGD-master/ci/util.py | # Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not u... | 3,862 | 32.301724 | 100 | py |
CD-SGD | CD-SGD-master/ci/build.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache Licen... | 22,504 | 38.344406 | 135 | py |
CD-SGD | CD-SGD-master/ci/docker/qemu/vmcontrol.py | #!/usr/bin/env python3
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "... | 10,989 | 33.451411 | 149 | py |
CD-SGD | CD-SGD-master/ci/docker/qemu/runtime_functions.py | #!/usr/bin/env python3
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "... | 4,672 | 33.614815 | 185 | py |
gnn-matlang | gnn-matlang-main/enzymes_contfeat.py |
from torch_geometric.data import DataLoader,InMemoryDataset
import torch
import scipy.io as sio
from torch_geometric.data.data import Data
import numpy as np
from sklearn.metrics import r2_score
from sklearn.model_selection import StratifiedKFold
import os.path as osp
import torch.nn as nn
import torch.nn.functional a... | 15,187 | 33.361991 | 161 | py |
gnn-matlang | gnn-matlang-main/proteins.py |
from torch_geometric.data import DataLoader,InMemoryDataset
import torch
import scipy.io as sio
from torch_geometric.data.data import Data
import numpy as np
from sklearn.metrics import r2_score
from sklearn.model_selection import StratifiedKFold
import os.path as osp
import torch.nn as nn
import torch.nn.functional a... | 12,181 | 31.747312 | 155 | py |
gnn-matlang | gnn-matlang-main/freqclass.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
import torch_geometric.transforms as T
from torch_geometric.data import DataLoader
from torch_geometric.nn import (GINConv,global_mean_pool,GATConv,ChebConv,GCNConv)
from libs.spect_conv import SpectConv,ML... | 13,770 | 31.866348 | 217 | py |
gnn-matlang | gnn-matlang-main/sr25.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear
import torch_geometric.transforms as T
from torch_geometric.data import DataLoader
from torch_geometric.nn import (GINConv,global_add_pool,GATConv,ChebConv,GCNConv)
import numpy as np
from libs.spect_conv impor... | 10,210 | 32.811258 | 143 | py |
gnn-matlang | gnn-matlang-main/mutag.py |
from torch_geometric.data import DataLoader
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import (GINConv,global_mean_pool,GATConv,ChebConv,GCNConv)
from libs.spect_conv import SpectConv,ML3Layer
from libs.uti... | 12,475 | 30.826531 | 155 | py |
gnn-matlang | gnn-matlang-main/ptc.py |
from torch_geometric.data import DataLoader
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import (GINConv,global_add_pool,global_max_pool,global_mean_pool,GATConv,ChebConv,GCNConv)
from libs.spect_conv import ... | 15,068 | 32.118681 | 157 | py |
gnn-matlang | gnn-matlang-main/counting.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.data import DataLoader
from torch_geometric.nn import (GINConv,global_add_pool,GATConv,ChebConv,GCNConv)
import numpy as npi
from libs.spect_conv import SpectConv,ML3Layer
from libs.uti... | 17,046 | 36.301969 | 173 | py |
gnn-matlang | gnn-matlang-main/Zinc12k.py |
from torch_geometric.data import DataLoader
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import (GINConv,global_add_pool,GATConv,ChebConv,GCNConv)
from libs.spect_conv import SpectConv,ML3Layer
from libs.utils import Zinc12KDa... | 13,746 | 32.859606 | 148 | py |
gnn-matlang | gnn-matlang-main/prepareMnist_gnnml3_tf.py | import numpy as np
from torch_geometric.datasets import MNISTSuperpixels
from libs.utils_tf import *
from libs.utils import DegreeMaxEigTransform
#select if node degree and location of superpixel region would be used by model or not.
#after any chnageing please remove MNIST/processed folder in order to preprocess c... | 2,913 | 25.490909 | 97 | py |
gnn-matlang | gnn-matlang-main/mnist75.py | from torch_geometric.data import DataLoader
import torch
import scipy.io as sio
from torch_geometric.data.data import Data
import numpy as np
import os.path as osp
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.nn import (NNConv, graclus, max_p... | 13,080 | 32.284987 | 171 | py |
gnn-matlang | gnn-matlang-main/exp_iso.py |
import os.path as osp
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear
import torch_geometric.transforms as T
from torch_geometric.data import DataLoader
from torch_geometric.nn import (GINConv,global_add_pool,GATConv,ChebConv,GCNConv)
import numpy as np
fro... | 10,253 | 32.509804 | 143 | py |
gnn-matlang | gnn-matlang-main/exp_classify.py |
import os.path as osp
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
import torch_geometric.transforms as T
from torch_geometric.data import DataLoader
from torch_geometric.nn import (GINConv,global_mean_pool,GATConv,ChebConv,GCNConv)
import numpy as n... | 13,017 | 32.901042 | 217 | py |
gnn-matlang | gnn-matlang-main/graph8c.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear
import torch_geometric.transforms as T
from torch_geometric.data import DataLoader
from torch_geometric.nn import (global_add_pool,GATConv,ChebConv,GCNConv,GINConv)
import numpy as np
from libs.spect_conv impor... | 10,363 | 33.317881 | 143 | py |
gnn-matlang | gnn-matlang-main/filtering.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Sequential, Linear, ReLU
from torch_geometric.data import DataLoader
from torch_geometric.nn import (GATConv,ChebConv,GCNConv,GINConv)
import numpy as np
import matplotlib.pyplot as plt
from libs.spect_conv import SpectConv,ML3Lay... | 12,079 | 31.213333 | 183 | py |
gnn-matlang | gnn-matlang-main/enzymes.py |
from torch_geometric.data import DataLoader,InMemoryDataset
import torch
import scipy.io as sio
from torch_geometric.data.data import Data
import numpy as np
from sklearn.metrics import r2_score
from sklearn.model_selection import StratifiedKFold
import os.path as osp
import torch.nn as nn
import torch.nn.functional a... | 16,522 | 33.85865 | 155 | py |
gnn-matlang | gnn-matlang-main/libs/utils.py | import torch
from torch_geometric.data import InMemoryDataset
from torch_geometric.data.data import Data
from torch_geometric.utils import to_networkx
from torch_geometric.utils import to_undirected
import numpy as np
import networkx as nx
import pickle
import os
import scipy.io as sio
from math import comb
def get_n... | 23,338 | 32.389127 | 108 | py |
gnn-matlang | gnn-matlang-main/libs/layers_tf.py | from inits_tf import *
import tensorflow as tf
flags = tf.app.flags
FLAGS = flags.FLAGS
# global unique layer ID dictionary for layer name assignment
_LAYER_UIDS = {}
def get_layer_uid(layer_name=''):
"""Helper function, assigns unique layer IDs."""
if layer_name not in _LAYER_UIDS:
_LAYER_UIDS[laye... | 10,941 | 30.085227 | 118 | py |
gnn-matlang | gnn-matlang-main/libs/spect_conv.py | from typing import Optional
from torch_geometric.typing import OptTensor
import math
import torch
from torch.nn import Parameter
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.utils import remove_self_loops, add_self_loops
from torch_geometric.utils import get_laplacian
import torch.nn.function... | 6,649 | 30.367925 | 129 | py |
Game-Theoretic-Deep-Reinforcement-Learning | Game-Theoretic-Deep-Reinforcement-Learning-main/Agents/MAD4PG/gradient.py | # Copyright 2017 The TensorFlow Authors. 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 of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 54,279 | 38.794721 | 93 | py |
Game-Theoretic-Deep-Reinforcement-Learning | Game-Theoretic-Deep-Reinforcement-Learning-main/Agents/MADRL/gradient.py | # Copyright 2017 The TensorFlow Authors. 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 of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 54,279 | 38.794721 | 93 | py |
Game-Theoretic-Deep-Reinforcement-Learning | Game-Theoretic-Deep-Reinforcement-Learning-main/Agents/MAD5PG/gradient.py | # Copyright 2017 The TensorFlow Authors. 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 of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 54,279 | 38.794721 | 93 | py |
Game-Theoretic-Deep-Reinforcement-Learning | Game-Theoretic-Deep-Reinforcement-Learning-main/Agents/MAHD5PG/gradient.py | # Copyright 2017 The TensorFlow Authors. 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 of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 54,279 | 38.794721 | 93 | py |
remix | remix-main/tests/test_rule_extractors.py | """
Very minimal test that verifies that we can call all of our rule extractors
using a simple XOR dataset with 10 features of which 8 are pure noise.
"""
import numpy as np
import pandas as pd
import pytest
import sklearn
import tensorflow as tf
from remix import deep_red_c5
from remix import eclaire
from remix impo... | 8,456 | 29.978022 | 80 | py |
remix | remix-main/experiments/experiment_runners/cross_validation.py | from prettytable import PrettyTable
import logging
import numpy as np
import pandas as pd
import pickle
import sklearn
import tensorflow as tf
from model_training.train import load_model
from remix.evaluate_rules.evaluate import evaluate, evaluate_estimator
from remix.utils.resources import resource_compute
from remix... | 12,737 | 37.835366 | 83 | py |
remix | remix-main/experiments/model_training/train_dnns.py | """
Generates neural networks for each of the n folds using the procedure
specified to locate optimal neural network hyper-parameters and neural network
initialization.
Used mostly for experimentation.
"""
from collections import OrderedDict
from tqdm import tqdm
import logging
import numpy as np
import os
import pan... | 5,710 | 36.326797 | 79 | py |
remix | remix-main/experiments/model_training/grid_search.py | """
Runs a grid search over hyper-parameters of our model to try and find the
best hyper-parameterization.
"""
from tensorflow.keras.wrappers.scikit_learn import KerasClassifier
import itertools
import json
import logging
import numpy as np
import sklearn
import tensorflow as tf
from .train import model_fn
def dese... | 6,292 | 31.107143 | 77 | py |
remix | remix-main/experiments/model_training/train.py | """
Build neural network models given number of nodes in each hidden layer
"""
import logging
import numpy as np
import os
import sklearn
import tensorflow as tf
################################################################################
## Metric Functions
######################################################... | 12,974 | 34.067568 | 80 | py |
remix | remix-main/remix/extract_rules/deep_red_c5.py | """
Implementation of DeepRED algorithm using C5.0 rather than C4.5 for intermediate
rule extraction.
"""
import dill
import logging
import numpy as np
from multiprocessing import Pool
from remix.logic_manipulator.substitute_rules import substitute
from remix.rules.C5 import C5
from remix.rules.cart import cart_rules... | 15,414 | 43.423631 | 80 | py |
remix | remix-main/remix/extract_rules/rem_d.py | """
Main implementation of the vanilla REM-D rule extraction algorithm for DNNs.
"""
import dill
import logging
import numpy as np
from remix.logic_manipulator.substitute_rules import substitute
from remix.rules.C5 import C5
from remix.rules.cart import cart_rules, random_forest_rules
from remix.rules.rule import Rule... | 15,567 | 42.853521 | 80 | py |
remix | remix-main/remix/extract_rules/utils.py | """
Helper utilities common to several rule extractors.
"""
import pandas as pd
import scipy.special as activation_fns
import tensorflow.keras.models as keras
################################################################################
## Helper Classes
###########################################################... | 6,691 | 40.308642 | 80 | py |
remix | remix-main/remix/extract_rules/eclaire.py | """
Implementation of ECLAIRE algorithm. This algorithm extracts intermediate rules
for each hidden layer and then performs a change of variables in all of these
rule sets by using a clause-wise level rather than at a term-wise level.
This helps the model avoiding the exponential explosion of terms that arises
from dis... | 27,886 | 42.77865 | 80 | py |
remix | remix-main/remix/extract_rules/pedagogical.py | """
Baseline implementation of an algorithm to extract rules from a DNN using a
simple pedagogical algorithm: we extract a decision tree that maps input
features with the model's outputs.
"""
import numpy as np
import pandas as pd
from remix.logic_manipulator.merge import merge
from remix.rules.C5 import C5
from rem... | 7,315 | 37.303665 | 80 | py |
remix | remix-main/remix/extract_rules/rem_t.py | """
Baseline implementation of an algorithm to extract rules while ignoring the
given DNN's predictions. It simply uses a vanilla decision tree learner
and extracts rules from it.
"""
import numpy as np
import pandas as pd
from remix.logic_manipulator.merge import merge
from remix.rules.C5 import C5
from remix.rules.... | 5,794 | 37.633333 | 80 | py |
ReInfoSelect | ReInfoSelect-master/ReInfoSelect/main.py | import argparse
import json
import torch
from torch import nn, optim
from torch.autograd import Variable
from torch.distributions import Categorical
from cknrm_tokenizer import *
from policies import *
from dataloaders import *
from models import *
from metrics import *
from transformers import *
def dev(args, mode... | 11,421 | 41.460967 | 148 | py |
ReInfoSelect | ReInfoSelect-master/ReInfoSelect/policies/policy.py | import torch
from torch import nn
import torch.nn.functional as F
from models import cknrm
class Policy(nn.Module):
def __init__(self, args, embedding_init=None):
super(Policy, self).__init__()
self.embedding = nn.Embedding(args.vocab_size, args.embed_dim)
if embedding_init is not None:
... | 2,008 | 40 | 94 | py |
ReInfoSelect | ReInfoSelect-master/ReInfoSelect/models/bert.py | import torch
import torch.nn as nn
from transformers import AutoModel
class Bert(nn.Module):
def __init__(self, pretrained, enc_dim):
super(Bert, self).__init__()
self._model = AutoModel.from_pretrained(pretrained)
self._dense = nn.Linear(enc_dim, 1)
def forward(self, input_ids, atten... | 668 | 36.166667 | 106 | py |
ReInfoSelect | ReInfoSelect-master/ReInfoSelect/models/cknrm.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
def kernal_mus(n_kernels):
l_mu = [1]
if n_kernels == 1:
return l_mu
bin_size = 2.0 / (n_kernels - 1) # score range from [-1, 1]
l_mu.append(1 - bin_size / 2) # mu: middle of the bin
fo... | 6,341 | 52.745763 | 147 | py |
ReInfoSelect | ReInfoSelect-master/ReInfoSelect/dataloaders/bert_dataloader.py | import numpy as np
import torch
from torch import nn
def pack_bert_seq(q_tokens, p_tokens, tokenizer, max_seq_length):
tokens = []
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in q_tokens:
tokens.append(token)
segment_ids.append(0)
tokens.append("[SEP]... | 7,445 | 42.54386 | 140 | py |
ReInfoSelect | ReInfoSelect-master/ReInfoSelect/dataloaders/dataloader.py | import numpy as np
import torch
from torch import nn
def read_train_to_features(args, tokenizer):
with open(args.train, 'r') as reader:
features = []
for line in reader:
if len(features) >= args.max_input:
break
s = line.strip('\n').split('\t')
q... | 5,297 | 41.047619 | 110 | py |
ReInfoSelect | ReInfoSelect-master/ReInfoSelect/inference/cknrm_inference.py | import os
import re
import json
import argparse
import numpy as np
import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
from CKNRM import CKNRM
from nltk.corpus import stopwords
sws = {}
for w in stopwords.words('english'):
sws[w] = 1
from krovetzstemmer import Stem... | 9,943 | 37.099617 | 120 | py |
ReInfoSelect | ReInfoSelect-master/ReInfoSelect/inference/CKNRM.py | import torch.nn.functional as F
from torch.autograd import Variable
import torch
import torch.nn as nn
def kernal_mus(n_kernels):
"""
get the mu for each gaussian kernel. Mu is the middle of each bin
:param n_kernels: number of kernels (including exact match). first one is exact match
:return: l_mu, a ... | 6,437 | 52.65 | 148 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/preprocessing/ccccii/show_img.py | import cv2
import numpy as np
from PIL import Image
from scipy import misc
import os
import sys
import cv2
from skimage import measure, morphology, segmentation
MIN_BOUND = -1000.0
MAX_BOUND = 400.0
def normalize(image):
image = (image - MIN_BOUND) / (MAX_BOUND - MIN_BOUND)
image[image>1] = 1.
image[... | 1,487 | 19.666667 | 103 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/main.py | """
Runs a model on a single node across N-gpus.
"""
import argparse
import os
import sys
from argparse import ArgumentParser
import numpy as np
import torch
from torchline.config import get_cfg
from torchline.engine import build_module
from torchline.models import META_ARCH_REGISTRY
from torchline.trainer import buil... | 3,759 | 36.6 | 139 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/config/config.py | from torchline.config import CfgNode as CN
__all__ = [
'add_config'
]
def add_config(cfg):
'''
cfg.new_item = CN()
'''
cfg.mixup = CN()
cfg.mixup.enable = 0
cfg.mixup.alpha = 0.4
cfg.dataset.slice_num = 64
cfg.dataset.is_color = False
cfg.dataset.is_3d = True
cfg.dataset.s... | 2,972 | 34.392857 | 84 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/models/pre_act_resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .resnet import conv3x3x3, conv1x1x1, get_inplanes, ResNet
from torchline.models import META_ARCH_REGISTRY
__all__ = ['preact_resnet3d']
class PreActivationBasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, str... | 3,307 | 27.765217 | 120 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/models/resnet.py | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchline.models import META_ARCH_REGISTRY
__all__ = ['resnet3d']
def get_inplanes():
return [64, 128, 256, 512]
def conv3x3x3(in_planes, out_planes, stride=1):
return nn.Conv3d(in_planes,
... | 8,020 | 30.829365 | 80 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/models/utils.py | import csv
import random
from functools import partialmethod
import torch
import numpy as np
from sklearn.metrics import precision_recall_fscore_support
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
... | 2,325 | 22.979381 | 66 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/models/model.py |
import torch
import torch.nn as nn
from torchvision import models
from torchline.models import META_ARCH_REGISTRY
__all__ = [
'mc3_18',
'r3d_18',
'r2plus1d_18'
]
model_urls = {
'r3d_18': 'https://download.pytorch.org/models/r3d_18-b3b3357e.pth',
'mc3_18': 'https://download.pytorch.org/models/mc3... | 11,623 | 30.846575 | 106 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/models/densenet.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
from torchline.models import META_ARCH_REGISTRY
__all__ = ['densenet3d']
class _DenseLayer(nn.Sequential):
def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
super()... | 7,915 | 36.875598 | 95 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/models/resnet2d.py | import torch
import torch.nn as nn
import torchvision as tv
from torchline.models import META_ARCH_REGISTRY
def modify_in_channels(model, n_input_channels):
conv1 = model.conv1
in_channels = conv1.in_channels
out_channels = conv1.out_channels
kernel_size = conv1.kernel_size
model.conv1.in_channels... | 2,768 | 41.6 | 102 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/models/resnext.py | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from .resnet import conv1x1x1, Bottleneck, ResNet
from .utils import partialclass
from torchline.models import META_ARCH_REGISTRY
__all__ = ['resnext3d']
def get_inplanes():
return [128, 256, 512, 102... | 2,770 | 30.488636 | 74 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/models/wide_resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from . import resnet
from torchline.models import META_ARCH_REGISTRY
__all__ = ['wide_resnet3d']
class WideBottleneck(resnet.Bottleneck):
expansion = 2
def generate_model(model_depth, k=2, **kwargs):
assert model_depth in [50, 101, 152, 2... | 996 | 27.485714 | 80 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/models/fake_model.py | import torch
import torch.nn as nn
from torchline.models import META_ARCH_REGISTRY
__all__ = [
'FakeNet3D',
'_FakeNet3D',
]
@META_ARCH_REGISTRY.register()
def FakeNet3D(cfg):
if cfg.dataset.is_3d:
c_in = cfg.model.n_input_channels
else:
c_in = cfg.dataset.slice_num
return _FakeNet... | 750 | 22.46875 | 51 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/models/densenet2d.py | import torch
import torch.nn as nn
import torchvision as tv
from torchline.models import META_ARCH_REGISTRY
def modify_in_channels(model, n_input_channels):
conv0 = model.features.conv0
in_channels = conv0.in_channels
out_channels = conv0.out_channels
kernel_size = conv0.kernel_size
model.features... | 1,147 | 38.586207 | 104 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/tests/test_nocolor_model.py | import sys
sys.path.append('..')
import torchline as tl
from config.config import add_config
import models
import torch
cfg = tl.config.get_cfg()
cfg = add_config(cfg)
cfg.merge_from_file('../config/config.yml')
cfg.model.n_input_channels = 1
x = torch.rand(1,1,32,64,64)
model_names = {
'mc3_18': [],
'r3d_18... | 1,151 | 24.6 | 52 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/tests/test_2dmodels.py | import sys
sys.path.append('..')
import torch
import models
import torchline as tl
from config.config import add_config
cfg = tl.config.get_cfg()
cfg = add_config(cfg)
x = torch.rand(1,16,64,64)
cfg.model.n_input_channels = 16
names = [
# 'resnet2d', 'resnext2d', 'wide_resnet2d',
'densenet2d']
depths =... | 882 | 20.536585 | 54 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/tests/test_model.py | import sys
sys.path.append('..')
import torchline as tl
from config.config import add_config
import models
cfg = tl.config.get_cfg()
cfg = add_config(cfg)
cfg.merge_from_file('../config/config.yml')
model_names = {
'mc3_18': [],
'r3d_18': [],
'r2plus1d_18': [],
'densenet3d': [121,169,201,264],
're... | 1,421 | 29.255319 | 69 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/engine/utils.py | import numpy as np
import torch
def mixup_data(x, y, alpha=1.0, use_cuda=True):
'''Returns mixed inputs, pairs of targets, and lambda
the larger the alpha, the more it can suppress overfitting.
recommend alpha=0.4
'''
if alpha > 0:
lam = np.random.beta(alpha, alpha)
else:
lam =... | 701 | 26 | 68 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/engine/module.py | import os
from collections import OrderedDict
import numpy as np
import torch
import imblearn
from sklearn import metrics
from torchline.engine import MODULE_REGISTRY, DefaultModule, build_module
from torchline.utils import AverageMeterGroup, topk_acc
from .utils import mixup_data, mixup_loss_fn
from medcam import m... | 7,303 | 37.442105 | 131 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/utils/visual_cam.py | import os
import sys
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
from PIL import Image
from skimage.transform import resize, rotate
from torchvision import transforms as TF
def pil_loader(path):
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/i... | 7,388 | 33.528037 | 101 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/data/sampler.py | import torch
import torchvision
from torchline.data.sampler import SAMPLER_REGISTRY
from torchline.data import build_data
__all__ = [
'WeightedRandomSampler',
]
@SAMPLER_REGISTRY.register()
def WeightedRandomSampler(cfg):
dataset = build_data(cfg)
sampler_cfg = cfg.dataloader.sampler
weights = []
... | 657 | 25.32 | 84 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/data/fake_data.py | import torch
from torchline.data import DATASET_REGISTRY
__all__ = [
'FakeData',
'_FakeData'
]
class _FakeData(torch.utils.data.Dataset):
def __init__(self, channels=1, size=[64,64], num=4):
if isinstance(size, int):
self.size = [size, size]
elif isinstance(size, list):
... | 765 | 24.533333 | 56 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/data/ct_data.py | import json
import os
import random
import nibabel as nib
import imageio
import PIL
import cv2
import torch
import torchvision.transforms as TF
from torchline.data import (DATASET_REGISTRY, build_label_transforms,
build_transforms)
from .utils import SymmetricalResampler, RandomResampler, ... | 7,805 | 37.453202 | 132 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/data/transforms.py | from torchline.data.transforms import TRANSFORMS_REGISTRY
import torchio
import torchio.transforms as iotf
import torchvision
import numpy as np
__all__ = [
'CTTransforms',
'_CTTransforms'
]
@TRANSFORMS_REGISTRY.register()
def CTTransforms(cfg):
is_train = cfg.dataset.is_train
slice_num = cfg.dataset.... | 4,589 | 41.5 | 113 | py |
HKBU_HPML_COVID-19 | HKBU_HPML_COVID-19-master/covid19_pipeline/losses/loss.py | from torchline.losses import LOSS_FN_REGISTRY
| 47 | 15 | 45 | py |
RVT | RVT-master/validation.py | import os
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
from pathlib import Path
import torch
from torch.backends import ... | 2,625 | 27.857143 | 92 | py |
RVT | RVT-master/train.py | import os
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ["OMP_NUM_THREADS"] = "1"
os.environ["OPENBLAS_NUM_THREADS"] = "1"
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["VECLIB_MAXIMUM_THREADS"] = "1"
os.environ["NUMEXPR_NUM_THREADS"] = "1"
import torch
torch.multiprocessing.set_sharing_strategy('file_sy... | 5,556 | 37.86014 | 118 | py |
RVT | RVT-master/modules/detection.py | from typing import Any, Optional, Tuple, Union, Dict
from warnings import warn
import numpy as np
import pytorch_lightning as pl
import torch
import torch as th
import torch.distributed as dist
from omegaconf import DictConfig
from pytorch_lightning.utilities.types import STEP_OUTPUT
from data.genx_utils.labels impor... | 19,420 | 48.417303 | 128 | py |
RVT | RVT-master/modules/utils/fetch.py | import pytorch_lightning as pl
from omegaconf import DictConfig
from modules.data.genx import DataModule as genx_data_module
from modules.detection import Module as rnn_det_module
def fetch_model_module(config: DictConfig) -> pl.LightningModule:
model_str = config.model.name
if model_str == 'rnndet':
... | 1,205 | 40.586207 | 85 | py |
RVT | RVT-master/modules/utils/detection.py | from enum import Enum, auto
from typing import List, Optional, Union, Tuple, Dict, Any
import torch
import torch as th
from data.genx_utils.labels import SparselyBatchedObjectLabels
from data.utils.types import BackboneFeatures, LstmStates, DatasetSamplingMode
class Mode(Enum):
TRAIN = auto()
VAL = auto()
... | 5,918 | 35.537037 | 117 | py |
RVT | RVT-master/modules/data/genx.py | from functools import partial
from typing import Any, Dict, Optional, Union
import math
import pytorch_lightning as pl
from omegaconf import DictConfig
from torch.utils.data import DataLoader, Dataset
from data.genx_utils.collate import custom_collate_rnd, custom_collate_streaming
from data.genx_utils.dataset_rnd imp... | 10,408 | 51.306533 | 120 | py |
RVT | RVT-master/callbacks/custom.py | from omegaconf import DictConfig
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.callbacks import ModelCheckpoint
from callbacks.detection import DetectionVizCallback
def get_ckpt_callback(config: DictConfig) -> ModelCheckpoint:
model_name = config.model.name
prefix = 'val'
if mo... | 1,292 | 31.325 | 116 | py |
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