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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MILLI | MILLI-master/src/interpretability/mnist_interpretability.py | import numpy as np
import torch
from data.mnist_bags import create_andmil_datasets, MNIST_N_CLASSES
from interpretability import metrics as met
from interpretability.base_interpretability import Model, InterpretabilityStudy, Method, Metric
from interpretability.instance_attribution import independent_instance_attribut... | 4,757 | 49.617021 | 117 | py |
MILLI | MILLI-master/src/interpretability/crc_interpretability.py | import numpy as np
import torch
from data.crc.crc_dataset import CRC_N_CLASSES, load_crc
from interpretability.base_interpretability import Model, InterpretabilityStudy, Method, Metric
from interpretability.instance_attribution import lime_instance_attribution as lime
from interpretability.instance_attribution import ... | 4,531 | 52.317647 | 108 | py |
MILLI | MILLI-master/src/interpretability/interpretability_util.py | import torch.nn.functional as F
class InherentInterpretabilityError(Exception):
pass
def get_pred(bag, model):
return F.softmax(model(bag), dim=0)
def get_clz_proba(bag, model, clz):
pred = get_pred(bag, model)
proba = pred[clz].detach().cpu().item()
return proba
def get_clz_probas(bags, mod... | 461 | 18.25 | 47 | py |
MILLI | MILLI-master/src/interpretability/musk_interpretability.py | from functools import partial
import torch
from data.musk_dataset import create_datasets, MUSK_N_CLASSES
from interpretability import metrics as met
from interpretability.base_interpretability import Model, InterpretabilityStudy, Method, Metric
from interpretability.instance_attribution import independent_instance_at... | 3,555 | 47.712329 | 117 | py |
MILLI | MILLI-master/src/interpretability/instance_attribution/independent_instance_attribution.py | import numpy as np
import torch
from interpretability.interpretability_util import get_pred, get_clz_proba
from interpretability.instance_attribution.base_instance_attribution import InstanceAttributionMethod
def get_independent_instance_method(method_name):
if method_name == 'single':
return SingleInsta... | 2,952 | 41.797101 | 108 | py |
MILLI | MILLI-master/src/interpretability/instance_attribution/base_instance_attribution.py | from abc import ABC, abstractmethod
import torch.nn.functional as F
from interpretability.interpretability_util import InherentInterpretabilityError
class InstanceAttributionMethod(ABC):
@abstractmethod
def get_instance_clz_attributions(self, bag, model, original_pred, clz):
pass
class InherentAt... | 983 | 31.8 | 111 | py |
MILLI | MILLI-master/src/train/crc_training.py | from abc import ABC
import torch
from torch import nn
from torch.utils.data import DataLoader
from data.crc.crc_dataset import load_crc, CRC_N_CLASSES
from train.train_base import Trainer
from train.train_util import GraphDataloader
class CrcTrainer(Trainer, ABC):
def __init__(self, device, train_params, model... | 2,269 | 33.923077 | 104 | py |
MILLI | MILLI-master/src/train/train_base.py | import copy
import os
from abc import ABC, abstractmethod
import latextable
import numpy as np
import optuna
import pandas as pd
import torch
import torch.nn.functional as F
from matplotlib import pyplot as plt
from sklearn.metrics import accuracy_score, confusion_matrix
from texttable import Texttable
from torch impo... | 10,204 | 38.863281 | 118 | py |
MILLI | MILLI-master/src/train/sival_training.py | from abc import ABC
import torch
from torch import nn
from torch.utils.data import DataLoader
from data.sival.sival_dataset import create_datasets, SIVAL_N_CLASSES
from model.sival_models import SivalGNN
from train.train_base import Trainer
from train.train_util import GraphDataloader
class SivalTrainer(Trainer, AB... | 2,715 | 35.702703 | 104 | py |
MILLI | MILLI-master/src/train/musk_training.py | from abc import ABC
import torch
from torch import nn
from torch.utils.data import DataLoader
from data.musk_dataset import create_datasets, MUSK_N_CLASSES
from model.musk_models import MuskGNN
from train.train_base import Trainer
from train.train_util import GraphDataloader
class MuskTrainer(Trainer, ABC):
de... | 2,924 | 37.486842 | 109 | py |
MILLI | MILLI-master/src/train/tef_training.py | from abc import ABC
import torch
from torch import nn
from torch.utils.data import DataLoader
from data.tef_dataset import create_datasets, TEF_N_CLASSES
from model.tef_models import TefGNN
from train.train_base import Trainer
from train.train_util import GraphDataloader
class TefTrainer(Trainer, ABC):
def __i... | 2,854 | 37.066667 | 104 | py |
MILLI | MILLI-master/src/train/mnist_training.py | from abc import ABC
import torch
from torch import nn
from torch.utils.data import DataLoader
from data.mnist_bags import create_andmil_datasets, MNIST_N_CLASSES
from model.mnist_models import MnistGNN
from train.train_base import Trainer
from train.train_util import GraphDataloader
class MnistTrainer(Trainer, ABC)... | 2,727 | 35.864865 | 104 | py |
MILLI | MILLI-master/src/train/train_util.py | import random
from torch_geometric.data.data import Data
class GraphDataloader:
def __init__(self, graph_dataset):
self.graph_dataset = graph_dataset
self.n_graphs = len(self.graph_dataset.bags)
def __iter__(self):
self.idx = 0
self.order = list(range(self.n_graphs))
... | 787 | 25.266667 | 58 | py |
MILLI | MILLI-master/src/data/mil_dataset.py | from collections import Counter
import numpy as np
import torch
from torch.utils.data import Dataset
from torch_geometric.data import Data
from torch_geometric.utils import dense_to_sparse
class MilDataset(Dataset):
def __init__(self, bags, targets, instance_targets):
super(Dataset, self).__init__()
... | 2,106 | 34.116667 | 104 | py |
MILLI | MILLI-master/src/data/musk_dataset.py | import csv
import torch
from sklearn.model_selection import train_test_split
from data.mil_dataset import MilDataset
MUSK1_FILE_PATH = "./data/MUSK/clean1.data"
MUSK2_FILE_PATH = "./data/MUSK/clean2.data"
MUSK_N_CLASSES = 2
MUSK_N_EXPECTED_DIMS = 2 # i * f
MUSK_D_IN = 166
class MuskDataset(MilDataset):
def ... | 3,028 | 29.908163 | 116 | py |
MILLI | MILLI-master/src/data/mnist_bags.py | import numpy as np
import torch
from matplotlib import pyplot as plt
from torch.utils.data import random_split
from torchvision import transforms
from torchvision.datasets import MNIST
from data.mil_dataset import MilDataset
MNIST_N_CLASSES = 4
MNIST_N_EXPECTED_DIMS = 4 # i * c * h * w
MNIST_FV_SIZE = 800
def loa... | 7,460 | 37.066327 | 117 | py |
MILLI | MILLI-master/src/data/tef_dataset.py | import csv
import torch
from sklearn.model_selection import train_test_split
from data.mil_dataset import MilDataset
TIGER_FILE_PATH = "./data/TEF/tiger.svm"
ELEPHANT_FILE_PATH = "./data/TEF/elephant.svm"
FOX_FILE_PATH = "./data/TEF/fox.svm"
TEF_N_CLASSES = 2
TEF_N_EXPECTED_DIMS = 2 # i * f
TEF_D_IN = 230
class ... | 3,445 | 27.957983 | 105 | py |
MILLI | MILLI-master/src/data/sival/compute_norm.py | import torch
from PIL import Image
from torchvision import transforms
from tqdm import tqdm
from data.sival.sival_dataset import parse_data_from_file
from data.bach_dataset import load_bach_bags
_, bags, _, _ = parse_data_from_file()
print(bags[0].shape)
bags = torch.cat(bags)
print(bags.shape)
arrs_mean = torch... | 407 | 16.73913 | 57 | py |
MILLI | MILLI-master/src/data/sival/sival_dataset.py | import csv
import torch
from PIL import Image
from sklearn.model_selection import train_test_split
from data.mil_dataset import MilDataset
raw_dir = "data/SIVAL/raw"
input_file = "data/SIVAL/processed.data"
all_clzs = ['ajaxorange', 'apple', 'banana', 'bluescrunge', 'candlewithholder', 'cardboardbox', 'checkeredsca... | 7,689 | 39.052083 | 117 | py |
MILLI | MILLI-master/src/data/crc/crc_dataset.py | import csv
import os
import random
import torch
import torchvision.transforms.functional as TF
from PIL import Image
from sklearn.model_selection import train_test_split
from torchvision import transforms
from data.mil_dataset import MilDataset
cell_types = ['others', 'inflammatory', 'fibroblast', 'epithelial']
bina... | 7,113 | 34.217822 | 119 | py |
MILLI | MILLI-master/src/data/crc/crc_compute_norm.py | import torch
from PIL import Image
from torchvision import transforms
from tqdm import tqdm
from data.crc.crc_dataset import load_crc_bags
bags, _, _ = load_crc_bags()
avgs = []
transformation = transforms.ToTensor()
for bag in tqdm(bags):
for file_name in bag:
with open(file_name, 'rb') as f:
... | 631 | 20.066667 | 66 | py |
MILLI | MILLI-master/src/model/crc_models.py | import model.base_models as bm
from data.crc.crc_dataset import CRC_N_EXPECTED_DIMS, CRC_FV_SIZE
from model import modules as mod
from model import aggregator as agg
from torch import nn
from overrides import overrides
class CrcEncoder(nn.Module):
def __init__(self, ds_enc_hid, d_enc, dropout):
super()... | 2,994 | 33.825581 | 119 | py |
MILLI | MILLI-master/src/model/modules.py | import torch
from torch import nn
class ConvBlock(nn.Module):
def __init__(self, c_in, c_out, kernel_size, stride, padding, dropout):
super().__init__()
conv = nn.Conv2d(c_in, c_out, kernel_size=kernel_size, stride=stride, padding=padding)
relu = nn.ReLU()
pool = nn.MaxPool2d(kern... | 3,450 | 30.09009 | 115 | py |
MILLI | MILLI-master/src/model/aggregator.py | import torch
from torch import nn
from model import modules as mod
from abc import ABC
class Aggregator(nn.Module, ABC):
def __init__(self):
super().__init__()
@staticmethod
def _parse_agg_method(agg_func_name):
if agg_func_name == 'mean':
return lambda x: torch.mean(x, dim=... | 2,167 | 35.745763 | 113 | py |
MILLI | MILLI-master/src/model/mnist_models.py | import model.base_models as bm
from data.mnist_bags import MNIST_N_CLASSES, MNIST_N_EXPECTED_DIMS, MNIST_FV_SIZE
from model import modules as mod
from model import aggregator as agg
from torch import nn
from overrides import overrides
class MnistEncoder(nn.Module):
def __init__(self, ds_enc_hid, d_enc, dropout... | 3,014 | 34.470588 | 123 | py |
MILLI | MILLI-master/src/model/base_models.py | from abc import ABC, abstractmethod
import torch
import torch.nn.functional as F
from torch import nn
from torch_geometric.data.data import Data
from torch_geometric.nn import SAGEConv, dense_diff_pool
from torch_geometric.utils import to_dense_adj, dense_to_sparse
from model import modules as mod
class MultipleIns... | 7,065 | 36.189474 | 111 | py |
MILLI | MILLI-master/scripts/tuning/tune_crc.py | import torch
from model import crc_models
from tuning import crc_tuning
from tuning.tune_util import setup_study
if __name__ == "__main__":
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_clz = crc_models.CrcEmbeddingSpaceNN
tuner_clz = crc_tuning.get_tuner(model_clz)
pr... | 554 | 31.647059 | 75 | py |
MILLI | MILLI-master/scripts/tuning/tune_mnist.py | import torch
from model import mnist_models
from tuning import mnist_tuning
from tuning.tune_util import setup_study
if __name__ == "__main__":
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_clz = mnist_models.MnistGNN
tuner_clz = mnist_tuning.get_tuner(model_clz)
print... | 551 | 31.470588 | 75 | py |
MILLI | MILLI-master/scripts/tuning/tune_milli.py | import pickle as pkl
import numpy as np
import torch
from matplotlib import pyplot as plt
from texttable import Texttable
from interpretability import metrics as met
from interpretability.base_interpretability import Method, Model, Metric
from interpretability.crc_interpretability import CrcInterpretabilityStudy
from... | 9,476 | 41.497758 | 120 | py |
MILLI | MILLI-master/scripts/tuning/tune_sival.py | import torch
from model import sival_models
from tuning import sival_tuning
from tuning.tune_util import setup_study
if __name__ == "__main__":
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_clz = sival_models.SivalInstanceSpaceNN
tuner_clz = sival_tuning.get_tuner(model_clz... | 563 | 32.176471 | 75 | py |
MILLI | MILLI-master/scripts/training/train_tef.py | import torch
from model import tef_models
from train.tef_training import TefNetTrainer, TefGNNTrainer
if __name__ == "__main__":
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_clz = tef_models.TefInstanceSpaceNN
dataset_names = ["tiger", "elephant", "fox"]
for dataset... | 714 | 33.047619 | 88 | py |
MILLI | MILLI-master/scripts/training/train_musk.py | import torch
from model import musk_models
from train.musk_training import MuskNetTrainer, MuskGNNTrainer
if __name__ == "__main__":
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_clz = musk_models.MuskInstanceSpaceNN
trainer_clz = MuskGNNTrainer if model_clz == musk_model... | 528 | 30.117647 | 88 | py |
MILLI | MILLI-master/scripts/training/train_crc.py | import torch
from model import crc_models
from train.crc_training import CrcNetTrainer, CrcGNNTrainer
if __name__ == "__main__":
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_clz = crc_models.CrcAttentionNN
trainer_clz = CrcGNNTrainer if model_clz == crc_models.CrcGNN else... | 515 | 31.25 | 84 | py |
MILLI | MILLI-master/scripts/training/train_sival.py | import torch
from model import sival_models
from train.sival_training import SivalNetTrainer, SivalGNNTrainer
if __name__ == "__main__":
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_clz = sival_models.SivalAttentionNN
trainer_clz = SivalGNNTrainer if model_clz == sival_m... | 534 | 30.470588 | 92 | py |
MILLI | MILLI-master/scripts/training/eval_models.py | import numpy as np
import torch
from texttable import Texttable
from torch.utils.data import DataLoader
from data import musk_dataset
from data import tef_dataset
from model import musk_models, tef_models
from train.musk_training import MuskNetTrainer, MuskGNNTrainer
from train.tef_training import TefNetTrainer, TefGN... | 5,003 | 41.05042 | 118 | py |
MILLI | MILLI-master/scripts/training/train_mnist.py | import torch
from model import mnist_models
from train.mnist_training import MnistNetTrainer, MnistGNNTrainer
if __name__ == "__main__":
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_clz = mnist_models.MnistGNN
trainer_clz = MnistGNNTrainer if model_clz == mnist_models.Mni... | 527 | 32 | 92 | py |
MILLI | MILLI-master/scripts/interpretability/interpret_tef.py | import torch
from interpretability.tef_interpretability import TefInterpretabilityStudy
if __name__ == "__main__":
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
n_repeats = 10
dataset_name = "tiger"
study = TefInterpretabilityStudy(device, dataset_name, n_repeats=n_repeats)
... | 423 | 27.266667 | 79 | py |
MILLI | MILLI-master/scripts/interpretability/interpret_mnist.py | import torch
from interpretability.mnist_interpretability import MnistInterpretabilityStudy
if __name__ == "__main__":
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
n_repeats = 10
study = MnistInterpretabilityStudy(device, n_repeats=n_repeats)
gather_data = True
if gathe... | 388 | 26.785714 | 78 | py |
MILLI | MILLI-master/scripts/interpretability/interpret_crc.py | import torch
from interpretability.crc_interpretability import CrcInterpretabilityStudy
if __name__ == "__main__":
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
n_repeats = 10
study = CrcInterpretabilityStudy(device, n_repeats=n_repeats)
gather_data = True
if gather_data... | 382 | 26.357143 | 75 | py |
MILLI | MILLI-master/scripts/interpretability/interpret_musk.py | import torch
from interpretability.musk_interpretability import MuskInterpretabilityStudy
if __name__ == "__main__":
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
n_repeats = 10
study = MuskInterpretabilityStudy(device, n_repeats=n_repeats)
gather_data = True
if gather_d... | 385 | 26.571429 | 76 | py |
MILLI | MILLI-master/scripts/interpretability/interpret_sival.py | import torch
from interpretability.sival_interpretability import SivalInterpretabilityStudy
if __name__ == "__main__":
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
n_repeats = 10
study = SivalInterpretabilityStudy(device, n_repeats=n_repeats)
gather_data = True
if gathe... | 388 | 26.785714 | 78 | py |
MILLI | MILLI-master/scripts/experiments/sample_size_experiment.py | import pickle as pkl
import numpy as np
import torch
from matplotlib import pyplot as plt
from interpretability.metrics import normalized_discounted_cumulative_gain, perturbation_metric
from interpretability.instance_attribution import lime_instance_attribution as lime
from interpretability.instance_attribution impor... | 10,815 | 41.920635 | 119 | py |
MILLI | MILLI-master/scripts/experiments/kernel_width_experiment.py | import pickle as pkl
import numpy as np
import torch
from matplotlib import pyplot as plt
from interpretability.metrics import normalized_discounted_cumulative_gain
from interpretability.instance_attribution import lime_instance_attribution as lime
from interpretability.base_interpretability import Method, Metric, Mo... | 7,411 | 39.282609 | 120 | py |
MILLI | MILLI-master/scripts/data/witness_rate.py | from collections import Counter
import numpy as np
import torch
from data.crc.crc_dataset import load_crc
from data.mnist_bags import create_andmil_datasets
from data.sival.sival_dataset import create_datasets
def run(dataset_name):
train_dataset, val_dataset, test_dataset = get_datasets_and_n_clzs(dataset_name... | 1,668 | 27.775862 | 100 | py |
MILLI | MILLI-master/scripts/out/sival_interpretability_out.py | import csv
import math
import random
import numpy as np
import torch
from PIL import Image
from matplotlib import pyplot as plt
from data.sival import sival_dataset
from interpretability.instance_attribution.milli_instance_attribution import Milli
from interpretability.base_interpretability import Model
from interpre... | 6,497 | 35.1 | 109 | py |
MILLI | MILLI-master/scripts/out/crc_interpretability_out.py | import matplotlib.image as mpimg
import numpy as np
import torch
from matplotlib import pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from interpretability.instance_attribution.milli_instance_attribution import Milli
from data.crc.crc_dataset import load_crc
from PIL import Image
from interpretab... | 5,674 | 33.186747 | 116 | py |
MILLI | MILLI-master/scripts/out/mnist_bags_interpretability_out.py | import numpy as np
import torch
from matplotlib import pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.gridspec import GridSpec
from data.mnist_bags import create_andmil_datasets
from interpretability.instance_attribution import milli_instance_attribution as milli
from interpretabil... | 3,936 | 34.151786 | 95 | py |
learn2comparenodes | learn2comparenodes-master/main.py | #!/usr/bin/env python
# coding: utf-8
# In[90]:
import sys
import os
import re
import numpy as np
import torch
from torch.multiprocessing import Process, set_start_method
from functools import partial
from utils import record_stats, display_stats, distribute
from pathlib import Path
if __name__ == "__main__":
... | 3,757 | 30.579832 | 126 | py |
learn2comparenodes | learn2comparenodes-master/learning/data_type.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 4 10:08:53 2022
@author: aglabassi
"""
import torch
import torch_geometric
class BipartiteGraphPairData(torch_geometric.data.Data):
"""
This class encode a pair of node bipartite graphs observation, s is graph0, t is graph1
"""
d... | 2,398 | 38.983333 | 149 | py |
learn2comparenodes | learn2comparenodes-master/learning/utils.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 4 10:04:12 2022
@author: aglabassi
"""
import torch
import torch_geometric
def normalize_graph(constraint_features,
edge_index,
edge_attr,
variable_features,
bounds,... | 5,993 | 36.229814 | 120 | py |
learn2comparenodes | learn2comparenodes-master/learning/model.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 19 20:44:58 2021
@author: abdel
from https://github.com/ds4dm/ecole/blob/master/examples/branching-imitation.ipynb with some modifications
"""
import torch
import torch.nn.functional as F
import torch_geometric
from torch_geometric.nn import GraphC... | 7,136 | 31.589041 | 132 | py |
learn2comparenodes | learn2comparenodes-master/learning/train_ranknet.py | # -*- coding: utf-8 -*-
import os
import sys
import torch
import torch_geometric
from pathlib import Path
from model import RankNet
from data_type import GraphDataset
from utils import process, process_ranknet
import numpy as np
def get_data(files):
X = []
y = []
depths = []
for file in files... | 4,910 | 31.098039 | 169 | py |
learn2comparenodes | learn2comparenodes-master/learning/train.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Nov 20 10:38:45 2021
@author: abdel
"""
import os
import sys
import torch
import torch_geometric
from pathlib import Path
from model import GNNPolicy
from data_type import GraphDataset
from utils import process
if __name__ == "__main__":
pro... | 5,508 | 36.222973 | 164 | py |
learn2comparenodes | learn2comparenodes-master/node_selection/recorders.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Sep 30 17:16:39 2021
@author: abdel
Contains utilities to save and load comparaison behavioural data
"""
import os
import imp
import torch
import numpy as np
import re
import time
def load_src(name, fpath):
return imp.load_source(name, os.path... | 17,698 | 32.39434 | 161 | py |
learn2comparenodes | learn2comparenodes-master/node_selection/node_selectors.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 14 14:43:54 2021
@author: abdel
"""
def load_src(name, fpath):
import os, imp
return imp.load_source(name, os.path.join(os.path.dirname(__file__), fpath))
load_src("data_type", "../learning/data_type.py" )
load_src("model", "../learning/... | 13,370 | 30.167832 | 145 | py |
learn2comparenodes | learn2comparenodes-master/node_selection/behaviour_gen.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 19 19:26:18 2021
@author: abdel
"""
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 12 12:54:57 2021
@author: abdel
"""
import os
import sys
import random
import numpy as np
import pyscipopt.scip as sp
from pathlib import P... | 7,466 | 30.774468 | 118 | py |
gepc | gepc-master/stc_train_eval.py | import os
import random
import collections
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from models.gcae.gcae import Encoder
from models.fe.fe_model import init_fenet
from models.dc_gcae.dc_gcae import DC_GCAE, load_ae_dcec
from models.dc_gcae.dc_gcae_training import ... | 6,012 | 41.048951 | 118 | py |
gepc | gepc-master/models/dc_gcae/dc_gcae_training.py | import os
import math
import time
import numpy as np
import torch
from torch import nn as nn
from tqdm import tqdm
from models.dc_gcae.dc_gcae import save_checkpoint
from utils.train_utils import calc_reg_loss
def dc_gcae_train(dc_gcae, dataset, args, optimizer=None, scheduler=None, stop_cret=1e-3):
"""
By n... | 7,163 | 36.904762 | 115 | py |
gepc | gepc-master/models/dc_gcae/clustering_layer.py | import torch
from torch import nn as nn
class ClusteringLayer(nn.Module):
"""
Clustering layer converts input sample (feature) to soft label, i.e. a vector that represents the probability of the
sample belonging to each cluster. The probability is calculated with student's t-distribution.
Partially po... | 2,468 | 40.15 | 120 | py |
gepc | gepc-master/models/dc_gcae/dc_gcae.py | """
A Deep clustering models using a Graph-Convolutional Auto Encoder
Takes a trained GCAE, adds a classification layer and loss and optimizes for clustering performance
while fine-tuning the Autoencoder.
"""
import os
import torch
import torch.nn as nn
from models.fe.fe_model import init_fenet
from models.fe.patchmo... | 3,428 | 31.657143 | 103 | py |
gepc | gepc-master/models/gcae/gcae.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.graph.graph import Graph
from models.graph.st_graph_conv_block import ConvBlock
class GCAE(nn.Module):
"""
Graph Conv AutoEncoder
"""
def __init__(self, in_channels, h_dim=8, graph_args=None, split_se... | 8,341 | 38.535545 | 121 | py |
gepc | gepc-master/models/gcae/gcae_training.py | import os
import time
import shutil
import numpy as np
import torch
import torch.optim as optim
from tqdm import tqdm
from utils.train_utils import calc_reg_loss
from models.dc_gcae.dc_gcae_training import adjust_lr
class Trainer:
def __init__(self, args, model, loss, train_loader, test_loader,
... | 5,853 | 38.288591 | 112 | py |
gepc | gepc-master/models/fe/patch_resnet.py | """
Resnet implementation courtesy of Yerlan Idelbayev.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torch import nn as nn
__all__ = ['ResNet', 'resnet20']
def _weights_init(m):
if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d):
init.kaimi... | 4,702 | 33.580882 | 120 | py |
gepc | gepc-master/models/fe/patchmodel.py | import os
import torch
import torch.nn as nn
from models.gcae.gcae import GCAE
from models.fe.patch_resnet import pt_resnet
class PatchModel(nn.Module):
"""
A Wrapper class for hadling per-patch feature extraction
"""
def __init__(self, patch_fe, gcae, backbone='resnet'):
super().__init__()
... | 3,880 | 35.613208 | 79 | py |
gepc | gepc-master/models/graph/sagc.py | import torch
import torch.nn as nn
import numpy as np
from models.graph.graph import Graph
class SAGC(nn.Module):
"""
Spatial Attention Graph Convolution
Applied to K_n adjacency subsets, each with K_a matrices
Base class provides the data-based C matrix and returns
K_n * K_a results.
"""
... | 6,214 | 37.84375 | 98 | py |
gepc | gepc-master/models/graph/st_graph_conv_block.py | import torch.nn as nn
from models.graph.pygeoconv import PyGeoConv
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size,
stride=1,
dropout=0,
conv_oper='sagc',
act=None,
out_bn=True,
... | 2,269 | 34.46875 | 100 | py |
gepc | gepc-master/models/graph/pygeoconv.py | import torch
import torch.nn as nn
from models.graph.sagc import SAGC
class PyGeoConv(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_size=1,
conv_oper='sagc',
headless=False,
dropout=0.0,
... | 2,397 | 30.973333 | 101 | py |
gepc | gepc-master/utils/pose_seg_dataset.py | import os
import json
import lmdb
import numpy as np
from torch.utils.data import Dataset, DataLoader
from utils.data_utils import normalize_pose
from utils.patch_utils import get_seg_patches, gen_clip_seg_data_np, seg_patches_to_tensor, patches_from_db
class PoseSegDataset(Dataset):
"""
Generates a dataset ... | 8,261 | 44.9 | 112 | py |
gepc | gepc-master/utils/data_utils.py | import math
import torch
import numpy as np
def get_aff_trans_mat(sx=1, sy=1, tx=0, ty=0, rot=0, flip=False):
"""
Generate affine transfomation matrix (torch.tensor type) for transforming pose sequences
:rot is given in degrees
"""
cos_r = math.cos(math.radians(rot))
sin_r = math.sin(math.radi... | 4,263 | 38.481481 | 103 | py |
gepc | gepc-master/utils/patch_utils.py | import os
import six
import numpy as np
import torch
import lmdb
import pyarrow as pa
from torchvision.transforms import ToTensor
from PIL import Image
def seg_patches_to_tensor(patches):
"""
Converts an [T, V, W, H, C] temporal patch collection to tensor
:param patches:
:return:
"""
t, v, w, ... | 8,210 | 35.986486 | 117 | py |
gepc | gepc-master/utils/train_utils.py | import os
import torch
import numpy as np
from utils.clustering import compute_features, Kmeans
def init_clusters(dataset, dcec_args, encoder, num_reevals=0):
downsample_factor = vars(dcec_args).get('k_init_downsample', 1)
downsample_data = downsample_factor > 1
initial_clusters, clustering_loss = calc_i... | 4,656 | 45.108911 | 111 | py |
gepc | gepc-master/utils/clustering.py | import time
import faiss
import torch
import numpy as np
def compute_features(dataloader, model, args, use_predict_fn=False, concat_vid=False, keep_dim=False):
cargs = args
if cargs.verbose:
print('Compute features')
start = time.time()
model.eval()
features = []
# discard the label in... | 3,550 | 31.577982 | 102 | py |
gepc | gepc-master/utils/scoring_utils.py | import os
import numpy as np
import torch
from scipy.ndimage import gaussian_filter1d
from sklearn.metrics import roc_auc_score
from torch.utils.data import DataLoader
from sklearn import mixture
from joblib import dump, load
def dpmm_calc_scores(model, train_dataset, eval_normal_dataset, eval_abn_dataset=None, args=... | 8,234 | 41.015306 | 118 | py |
gepc | gepc-master/utils/optim_utils/optim_init.py | """
File for initializing optimizers and schedulers
"""
import torch.optim as optim
from functools import partial
from utils.optim_utils.schedulers.delayed_sched import *
from utils.optim_utils.schedulers.cosine_annealing_with_warmup import *
def init_optimizer(type_str, **kwargs):
if type_str.lower() == 'adam':... | 1,388 | 32.071429 | 81 | py |
gepc | gepc-master/utils/optim_utils/schedulers/cosine_annealing_with_warmup.py | import math
from torch.optim.lr_scheduler import _LRScheduler
# From https://github.com/katsura-jp/pytorch-cosine-annealing-with-warmup
class CosineAnnealingWarmUpRestarts(_LRScheduler):
def __init__(self, optimizer, T_0, T_mult=1, eta_max=0.1, T_up=0, gamma=1., last_epoch=-1):
if T_0 <= 0 or not isinstan... | 2,640 | 42.295082 | 109 | py |
gepc | gepc-master/utils/optim_utils/schedulers/delayed_sched.py | from torch.optim.lr_scheduler import _LRScheduler, CosineAnnealingLR
class DelayerScheduler(_LRScheduler):
""" Starts with a flat lr schedule until it reaches N epochs the applies a scheduler
Args:
optimizer (Optimizer): Wrapped optimizer.
delay_epochs: number of epochs to keep the initial lr until starting apl... | 1,299 | 31.5 | 92 | py |
model-sanitization | model-sanitization-master/evasion_attack/eval_curve.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='VGG16Para-2robust', metavar='DIR',
... | 6,258 | 32.292553 | 102 | py |
model-sanitization | model-sanitization-master/evasion_attack/test_curve.py | import argparse
import torch
import curves
import data
import models
parser = argparse.ArgumentParser(description='Test DNN curve')
parser.add_argument('--dataset', type=str, default=None, metavar='DATASET',
help='dataset name (default: CIFAR10)')
parser.add_argument('--use_test', action='store_... | 4,001 | 35.715596 | 96 | py |
model-sanitization | model-sanitization-master/evasion_attack/AttackPGD.py | import torch
import torch.nn.functional as F
import torch.nn as nn
class AttackPGD(nn.Module):
def __init__(self, basic_net, config):
super(AttackPGD, self).__init__()
self.basic_net = basic_net
self.rand = config['random_start']
self.step_size = config['step_size']
self.e... | 1,348 | 38.676471 | 85 | py |
model-sanitization | model-sanitization-master/evasion_attack/utils.py | import numpy as np
import os
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import curves
def l2_regularizer(weight_decay):
def regularizer(model):
l2 = 0.0
for p in model.parameters():
l2 += torch.sqrt(torch.sum(p ** 2))
return 0.5 * weight_de... | 12,434 | 29.779703 | 113 | py |
model-sanitization | model-sanitization-master/evasion_attack/data.py | import os
import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
class Transforms:
class MNIST:
class VGG:
train = transforms.Compose([
transforms.ToTensor(),
])
test = transforms.Compose([
transfo... | 23,757 | 38.465116 | 131 | py |
model-sanitization | model-sanitization-master/evasion_attack/train_robust_connection.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
from AttackPGD import AttackPGD
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='/tmp/cur... | 7,447 | 35.331707 | 109 | py |
model-sanitization | model-sanitization-master/evasion_attack/connect.py | import argparse
import numpy as np
import os
import sys
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import utils
parser = argparse.ArgumentParser(description='Connect models with polychain')
parser.add_argument('--dir', type=str, default='Para', metavar='DIR',
... | 4,788 | 31.80137 | 121 | py |
model-sanitization | model-sanitization-master/evasion_attack/curves.py | import numpy as np
import math
import torch
import torch.nn.functional as F
from torch.nn import Module, Parameter
from torch.nn.modules.utils import _pair
from scipy.special import binom
class Bezier(Module):
def __init__(self, num_bends):
super(Bezier, self).__init__()
self.register_buffer(
... | 12,363 | 37.517134 | 100 | py |
model-sanitization | model-sanitization-master/evasion_attack/save_model.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='Para128-256_split', metavar='DIR',
... | 3,160 | 33.358696 | 127 | py |
model-sanitization | model-sanitization-master/evasion_attack/train.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='VGG16Para_128_poison_single_target', me... | 7,473 | 36 | 99 | py |
model-sanitization | model-sanitization-master/evasion_attack/eval_curve_robustness.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import torch.nn as nn
from AttackPGD import AttackPGD
import data
import models
import curves
import utils
from tqdm import tqdm
import torchvision
import torchvision.transforms as transforms
parser = argparse.Ar... | 8,802 | 33.252918 | 148 | py |
model-sanitization | model-sanitization-master/evasion_attack/models/preresnet.py |
import math
import torch.nn as nn
import curves
__all__ = ['PreResNet110', 'PreResNet164']
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
def conv3x3curve(in_planes, out_planes, fix_points, strid... | 10,079 | 31.833876 | 100 | py |
model-sanitization | model-sanitization-master/evasion_attack/models/vggW.py |
import math
import torch.nn as nn
import curves
__all__ = ['VGG16W', 'VGG16BNW', 'VGG19W', 'VGG19BNW']
config = {
16: [[160, 160], [320, 320], [640, 640, 640], [640, 640, 640], [640, 640, 640]],
19: [[160, 160], [320, 320], [640, 640, 640, 640], [640, 640, 640, 640], [640, 640, 640, 640]],
}
def make_laye... | 4,956 | 29.78882 | 100 | py |
model-sanitization | model-sanitization-master/evasion_attack/models/vgg.py |
import math
import torch.nn as nn
import curves
__all__ = ['VGG16', 'VGG16BN', 'VGG19', 'VGG19BN']
config = {
16: [[64, 64], [128, 128], [256, 256, 256], [512, 512, 512], [512, 512, 512]],
19: [[64, 64], [128, 128], [256, 256, 256, 256], [512, 512, 512, 512], [512, 512, 512, 512]],
}
def make_layers(conf... | 4,947 | 29.54321 | 100 | py |
model-sanitization | model-sanitization-master/evasion_attack/models/wide_resnet.py |
import torch.nn as nn
import torch.nn.functional as F
import curves
__all__ = ['WideResNet28x10']
def conv3x3(in_planes, out_planes, stride=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=True)
def conv3x3curve(in_planes, out_planes, fix_points, stride=1):
retur... | 6,132 | 36.396341 | 100 | py |
model-sanitization | model-sanitization-master/evasion_attack/models/convfc.py | import math
import torch.nn as nn
import curves
__all__ = [
'ConvFC',
]
class ConvFCBase(nn.Module):
def __init__(self, num_classes):
super(ConvFCBase, self).__init__()
self.conv_part = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=5, padding=2),
nn.ReLU(True),
... | 3,153 | 27.93578 | 92 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/evalacc.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import numpy as np
import torchvision
import torchvision.transforms as transforms
import data
import os
import argparse
import utils
from tqdm import tqdm
from models import *
import mode... | 4,532 | 38.077586 | 114 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/eval_curve.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import data
import models
import curves
import utils
parser = argparse.ArgumentParser(description='DNN curve evaluation')
parser.add_argument('--dir', type=str, default='VGG16Para-2robust', metavar='DIR',
... | 6,258 | 32.292553 | 102 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/train_injection.py | import argparse
import os
import sys
import tabulate
import time
import torch
import torch.nn.functional as F
import curves
import data
import models
import utils
import numpy as np
parser = argparse.ArgumentParser(description='DNN curve training')
parser.add_argument('--dir', type=str, default='Para11/', metavar='DI... | 5,111 | 33.540541 | 132 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/evalacc2.py | import argparse
import numpy as np
import os
import tabulate
import torch
import torch.nn.functional as F
import torch.nn as nn
from AttackPGD import AttackPGD
import data
import models
import curves
import utils
from tqdm import tqdm
import torchvision
import torchvision.transforms as transforms
parser = argparse.Ar... | 8,801 | 33.517647 | 148 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/data1.py | import os
import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
import random
class Transforms:
class MNIST:
class VGG:
train = transforms.Compose([
transforms.ToTensor(),
])
test = transforms.Compose([
... | 12,399 | 35.904762 | 117 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/test_curve.py | import argparse
import torch
import curves
import data
import models
parser = argparse.ArgumentParser(description='Test DNN curve')
parser.add_argument('--dataset', type=str, default=None, metavar='DATASET',
help='dataset name (default: CIFAR10)')
parser.add_argument('--use_test', action='store_... | 4,001 | 35.715596 | 96 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/AttackPGD.py | import torch
import torch.nn.functional as F
import torch.nn as nn
class AttackPGD(nn.Module):
def __init__(self, basic_net, config):
super(AttackPGD, self).__init__()
self.basic_net = basic_net
self.rand = config['random_start']
self.step_size = config['step_size']
self.e... | 1,348 | 38.676471 | 85 | py |
model-sanitization | model-sanitization-master/error-injection/injection_svhn/utils.py | import numpy as np
import os
import torch
import torch.nn.functional as F
import torchvision.transforms as transforms
import curves
import torchvision
import data
import random
def l2_regularizer(weight_decay):
def regularizer(model):
l2 = 0.0
for p in model.parameters():
l2 += torch.s... | 11,718 | 26.50939 | 89 | py |
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