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
value |
|---|---|---|---|---|---|---|
mn-bab-SABR_ready | mn-bab-SABR_ready/src/utilities/bilinear_interpolator.py | from typing import Dict, List, Tuple
import dill # type: ignore[import]
import torch
from torch import Tensor
class BilinearInterpol:
def __init__(
self,
inner: Dict[Tuple[float, float], float],
outer: Dict[Tuple[float, float], float],
inner_range: float,
outer_range: flo... | 3,417 | 33.18 | 82 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/utilities/prima_interface.py | from typing import Callable, List, Sequence, Tuple
import numpy as np
import torch
from torch import Tensor
from src.utilities.config import PrimaHyperparameters
from src.utilities.prima_util import ActivationType, KAct, encode_kactivation_cons
def get_prima_constraints(
input_lb: Tensor,
input_ub: Tensor,
... | 9,343 | 37.138776 | 126 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/utilities/general.py | import functools
import itertools
from typing import Callable, List, Optional, Sequence, Tuple, Union
import torch
from torch import Tensor
from src.utilities.config import DomainSplittingConfig
def all_larger_equal(seq: Union[Sequence, Tensor], threshold: float) -> bool:
return all(el >= threshold for el in se... | 10,303 | 31.2 | 161 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/utilities/branching.py | from __future__ import annotations
import random
from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor
from src.abstract_layers.abstract_bn2d import BatchNorm2d
from src.abstract_layers.abstract_conv2d import... | 76,173 | 37.394153 | 126 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/utilities/build_tiny_conv_net.py | import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision # type: ignore[import]
from torchvision import transforms
from src.utilities.loading.network import mnist_conv_tiny
def seed_worker(worker_id: int) -> None:
worker_seed = torch.initial_seed() % 2*... | 2,965 | 28.959596 | 90 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/utilities/attacks.py | from typing import List, Optional, Sequence, Tuple, Union
import numpy as np
import torch
# import torch.nn as nn
import torch.optim as optim
from torch import Tensor
from torch.autograd import Variable
from src.abstract_layers.abstract_network import AbstractNetwork
# adapted from https://github.com/mnmueller/eran... | 11,760 | 32.795977 | 93 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/utilities/loading/network.py | import gzip
import typing
from os.path import exists
from typing import List, Optional, Sequence, Tuple, Type
import onnx # type: ignore[import]
import torch
from bunch import Bunch # type: ignore[import]
import numpy as np
from torch import nn as nn
from src.concrete_layers.basic_block import BasicBlock
from src.... | 28,413 | 32.039535 | 124 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/utilities/loading/data.py | from typing import Sequence, Tuple
import torch
from bunch import Bunch # type: ignore[import]
from torch import Tensor
def transform_image(
pixel_values: Sequence[str],
input_dim: Tuple[int, ...],
) -> Tensor:
if len(pixel_values)==9409:
normalized_pixel_values = torch.tensor([float(p) for p in... | 2,542 | 33.835616 | 88 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/state/subproblem_state.py | from __future__ import annotations
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Dict, Optional, Tuple
import torch
from torch import Tensor
from src.state.constraints import Constraints, ReadonlyConstraints
from src.state.parameters import Parameters, ReadonlyParameters
from src.state.prima_... | 8,046 | 31.447581 | 121 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/state/split_state.py | from __future__ import annotations
from abc import ABC, abstractmethod
from copy import deepcopy
from typing import TYPE_CHECKING, Dict, Mapping, Optional, Sequence, Tuple
if TYPE_CHECKING:
from src.abstract_layers.abstract_container_module import ActivationLayer
import torch
from torch import Tensor
from src.s... | 9,388 | 34.164794 | 122 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/state/parameters.py | from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Callable, Dict, List, Mapping, Optional, Sequence, Tuple, Union
import torch
from torch import Tensor
from src.state.layer_bounds import ReadonlyLayerBounds
from src.state.tags import LayerTag, ParameterTag, QueryTag
from src.u... | 16,431 | 35.678571 | 168 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/state/constraints.py | from __future__ import annotations
from abc import ABC, abstractmethod
from typing import TYPE_CHECKING, Dict, Mapping, Optional, OrderedDict, Sequence, Tuple
import torch
from torch import Tensor
from src.state.layer_bounds import LayerBounds, ReadonlyLayerBounds
from src.state.prima_constraints import PrimaConstra... | 10,998 | 33.806962 | 93 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/state/prima_constraints.py | from __future__ import annotations
from abc import ABC, abstractmethod
from typing import Dict, Mapping, Tuple
import torch
from torch import Tensor
from src.state.tags import LayerTag
from src.utilities.custom_typing import implement_properties_as_fields
from src.utilities.tensor_management import deep_copy_to, dee... | 2,936 | 28.666667 | 91 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/state/layer_bounds.py | from __future__ import annotations
from abc import ABC, abstractmethod
from collections import OrderedDict
from copy import deepcopy
from typing import List, Mapping, Optional, Sequence, Tuple
import torch
from torch import Tensor
from src.state.tags import LayerTag, NodeTag
from src.utilities.custom_typing import i... | 8,005 | 33.808696 | 102 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_flatten.py | from __future__ import annotations
from typing import Any, Optional, Tuple
import numpy as np
import torch.nn as nn
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_module import AbstractModule
from src.mn_bab_shape import AffineForm, MN_BaB_Shape
fr... | 2,866 | 36.233766 | 114 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_mulit_path_block.py | from __future__ import annotations
from collections import OrderedDict
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
from torch import Tensor, nn
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_binary_op import BinaryOp... | 18,401 | 36.026157 | 128 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_normalization.py | from __future__ import annotations
from typing import Any, Optional, Sequence, Tuple
import torch
from torch import Tensor
import src.concrete_layers.normalize as concrete_normalize
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_module import AbstractModule
from src.mn_bab... | 3,476 | 32.757282 | 86 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_binary_op.py | from __future__ import annotations
import typing
from typing import Any, Optional, Sequence, Tuple, Union
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_module import AbstractModule
from src.concrete_layers.binary_op import BinaryOp as concreteBina... | 5,158 | 35.588652 | 136 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_network.py | from __future__ import annotations
from typing import Any, Dict, Iterable, Optional, Tuple
import torch
from torch import Size, Tensor, nn
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_container_module import (
ActivationLayer,
ActivationLayers,
)
from src.abstrac... | 11,985 | 36.691824 | 136 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_concat.py | from __future__ import annotations
from typing import Any, List, Optional, Sequence, Tuple
import torch
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_module import AbstractModule
from src.concrete_layers.concat import Concat as concreteConcat
from... | 4,374 | 34.860656 | 89 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_sigmoid.py | from __future__ import annotations
import os
from typing import Any, Callable, Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_module import AbstractModule
from src.abstract_layers.abstract_sig_base... | 4,927 | 34.453237 | 87 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_module.py | from __future__ import annotations
from typing import Any, List, Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.exceptions.invalid_bounds import InvalidBoundsError
from src.mn_bab_shape import MN_BaB_Shape
from src.state.c... | 4,255 | 31.992248 | 81 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_linear.py | from __future__ import annotations
from typing import Any, Optional, Tuple
# import torch
import torch.nn as nn
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_module import AbstractModule
from src.mn_bab_shape import AffineForm, MN_BaB_Shape
from s... | 3,742 | 34.990385 | 109 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_permute.py | from __future__ import annotations
from typing import Any, Callable, Optional, Tuple
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_module import AbstractModule
from src.concrete_layers.permute import Permute as concretePermute
from src.mn_bab_shap... | 3,722 | 38.189474 | 217 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_avg_pool2d.py | from __future__ import annotations
from math import floor
from typing import Any, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_module import AbstractModule
... | 7,064 | 36.380952 | 89 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_sig_base.py | from __future__ import annotations
from typing import Callable, List, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch import Tensor
from src.abstract_layers.abstract_module import AbstractModule
from src.mn_bab_shape import AffineForm, MN_BaB_Shape
from src.state.tags import (
LayerTag,
... | 14,169 | 35.147959 | 125 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_identity.py | from __future__ import annotations
from typing import Any, Optional, Tuple
import torch.nn as nn
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_module import AbstractModule
from src.mn_bab_shape import MN_BaB_Shape
from src.utilities.config import ... | 1,709 | 31.264151 | 78 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_pad.py | from __future__ import annotations
from typing import Any, Optional, Tuple
import torch.nn.functional as F
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_module import AbstractModule
from src.concrete_layers.pad import Pad as concretePad
from src.m... | 3,956 | 34.648649 | 86 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_unbinary_op.py | from __future__ import annotations
import typing
from typing import Any, Callable, Optional, Tuple, Union
import torch
from torch import Tensor
from src.abstract_layers.abstract_module import AbstractModule
from src.concrete_layers import unbinary_op
from src.mn_bab_shape import AffineForm, MN_BaB_Shape
from src.uti... | 5,758 | 33.90303 | 87 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_residual_block.py | from __future__ import annotations
from collections import OrderedDict
from typing import Any, Callable, Dict, List, Optional, Tuple
import numpy as np
import torch
from torch import Tensor, nn
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_container_module import (
Ab... | 13,871 | 36.390836 | 120 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_basic_block.py | from __future__ import annotations
from typing import Any, Tuple
from torch import Tensor
from src.abstract_layers.abstract_container_module import AbstractContainerModule
from src.abstract_layers.abstract_sequential import Sequential
from src.concrete_layers import basic_block as concrete_basic_block
from src.concr... | 2,288 | 34.215385 | 120 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_reshape.py | from __future__ import annotations
from typing import Any, Optional, Tuple
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_module import AbstractModule
from src.concrete_layers.reshape import Reshape as concreteReshape
from src.mn_bab_shape import A... | 2,999 | 33.090909 | 86 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_convtranspose2d.py | from __future__ import annotations
from typing import Any, Optional, Tuple, Union
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_module import AbstractModule
from src.mn_bab_shape import AffineF... | 9,058 | 35.528226 | 103 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_slice.py | from __future__ import annotations
from typing import Any, List, Optional, Tuple, Union
import torch
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_module import AbstractModule
from src.concrete_layers.slice import Slice as concreteSlice
from src.m... | 5,575 | 36.173333 | 86 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_split_block.py | from __future__ import annotations
from collections import OrderedDict
from typing import Any, Callable, Dict, List, Optional, Tuple
import numpy as np
import torch
from torch import Tensor, nn
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_container_module import (
Ab... | 28,165 | 37.321088 | 138 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_container_module.py | from __future__ import annotations
from collections import OrderedDict
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import Tensor
from src.abstract_layers.abstract_max_pool2d import MaxPool2d
from src.abstract_layers.abstract_module import AbstractModule
from src.abstr... | 3,496 | 32.625 | 85 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_tanh.py | from __future__ import annotations
import os
from typing import Any, Callable, Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from src.abstract_layers.abstract_module import AbstractModule
from src.abstract_layers.abstract_sig_base import SigBase
from src.mn_bab_shape import MN_BaB_Shape... | 4,335 | 32.612403 | 87 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_max_pool2d.py | from __future__ import annotations
from math import floor
from typing import Any, Callable, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_... | 22,231 | 37.530329 | 128 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_bn2d.py | from __future__ import annotations
from typing import Any, Optional, Tuple, Union
import torch
import torch.nn as nn
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_module import AbstractModule
from src.mn_bab_shape import AffineForm, MN_BaB_Shape
f... | 6,136 | 34.680233 | 125 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_conv2d.py | from __future__ import annotations
from math import floor
from typing import Any, Optional, Tuple, Union
import torch.nn as nn
import torch.nn.functional as F
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_module import AbstractModule
from src.mn_b... | 9,757 | 34.613139 | 103 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_relu.py | from __future__ import annotations
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.abstract_conv2d import Conv2d
from src.abstract_layers.abs... | 25,442 | 35.873913 | 255 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_layers/abstract_sequential.py | from __future__ import annotations
import time
from collections import OrderedDict
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
from src.abstract_domains.ai_util import AbstractElement
from src.abstract_layers.a... | 54,902 | 42.817239 | 177 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_domains/zonotope.py | """
Based on HybridZonotope from DiffAI (https://github.com/eth-sri/diffai/blob/master/ai.py)
"""
from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor
from src.abstract_domains.ai_uti... | 56,944 | 37.870307 | 185 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_domains/ai_util.py | from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import torch
from torch import Tensor
if TYPE_CHECKING:
from src.abstract_layers.abstract_bn2d import BatchNorm2d
def clamp_image(
x: Tensor, eps: Union[Tensor, float], clamp_min: float = 0, clamp_max: float =... | 6,159 | 28.615385 | 88 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/abstract_domains/DP_f.py | """
Based on DeepPoly_f from DiffAI (https://github.com/eth-sri/diffai/blob/master/ai.py)
"""
from __future__ import annotations
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import Tensor
from src.abstract_layers.abstract_si... | 48,126 | 35.267521 | 203 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/src/exceptions/invalid_bounds.py | from torch import Tensor
class InvalidBoundsError(ValueError):
"""When intermediate bounds are set with lb > ub"""
def __init__(self, invalid_bounds_mask_in_batch: Tensor):
self.invalid_bounds_mask_in_batch = invalid_bounds_mask_in_batch
| 257 | 27.666667 | 72 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/test_util.py | import functools
import time
from typing import Any, Callable, List, Optional, Sequence, Tuple
import numpy as np
import torch
import torch.nn as nn
import tqdm # type: ignore[import]
from bunch import Bunch # type: ignore[import]
from torch import Tensor
from torch.distributions.beta import Beta
from src.abstract_... | 41,368 | 30.994586 | 125 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/gurobi_util.py | """
This code is mostly adapted from ERAN
# source: https://github.com/mnmueller/eran/blob/all_constraints/tf_verify/ai_milp.py
# commit hash: 4d25107db9db743a008eb63c8fa5a4fe8463b16d
"""
from typing import List, Tuple
import numpy as np
from gurobipy import GRB, LinExpr, Model, Var # type: ignore[import]
from torch... | 11,849 | 34.909091 | 162 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/regression_tests/dependence_sets_regression_test.py | import csv
from typing import Dict, Sequence, Tuple
import numpy as np
import torch
from torch import Tensor
from src.abstract_layers.abstract_network import AbstractNetwork
from src.mn_bab_optimizer import MNBabOptimizer
from src.utilities.config import make_backsubstitution_config, make_optimizer_config
from src.ut... | 6,046 | 33.752874 | 91 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/milp_full_test.py | import random
import time
import torch
from torch import nn
from src.abstract_layers.abstract_network import AbstractNetwork
from src.milp_network import MILPNetwork
from src.state.tags import layer_tag
from src.utilities.argument_parsing import get_config_from_json
from src.utilities.initialization import seed_every... | 6,256 | 39.367742 | 95 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/pooling_test.py | import time
import torch
from torch.distributions.beta import Beta
from src.abstract_domains.DP_f import DeepPoly_f
from src.abstract_domains.zonotope import HybridZonotope
from src.milp_network import MILPNetwork
from src.state.tags import layer_tag
from src.utilities.initialization import seed_everything
from tests... | 6,159 | 34.2 | 89 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/conv2d_test.py | import csv
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor
from src.abstract_domains.DP_f import DeepPoly_f
from src.abstract_domains.zonotope import HybridZonotope
from src.abstract_layers.abstract_conv2d import Conv2d
from src.abstract_layers.abstract_network import AbstractNetwork
fr... | 13,661 | 33.675127 | 85 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/alpha_optimization_test.py | import torch
from tests.test_util import (
get_deep_poly_bounds,
optimize_output_node_bounds_with_prima_crown,
toy_net,
)
class TestAlphaOptimization:
def test_toy_net(self) -> None:
network = toy_net()[0]
input_lb = torch.tensor([-1.0, -1.0]).unsqueeze(0)
input_ub = torch.te... | 917 | 24.5 | 63 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/backprop_test.py | import torch
from tests.test_util import (
get_deep_poly_bounds,
optimize_output_node_bounds_with_prima_crown,
toy_stack_seq_net,
)
class TestBackprop:
"""
Test the more intricate parts of the backsubstitution implementation.
"""
def test_seq_layer_stacking_sound(self) -> None:
n... | 1,813 | 29.745763 | 86 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/onnx_test.py | import gzip
import numpy as np
# import onnx
import onnxruntime as ort # type: ignore[import]
import torch
from src.utilities.initialization import seed_everything
from src.utilities.loading.network import load_onnx_model
def is_float_try(str: str) -> bool:
try:
float(str)
return True
exce... | 5,657 | 32.678571 | 115 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/pad_permute_test.py | import shutil
import time
from pathlib import Path
import torch
from torch import nn
from src.abstract_domains.DP_f import DeepPoly_f
from src.abstract_domains.zonotope import HybridZonotope
from src.abstract_layers.abstract_network import AbstractNetwork
from src.milp_network import MILPNetwork
from src.state.tags i... | 11,374 | 30.685237 | 87 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/mn_bab_shape_test.py | import torch
from src.abstract_layers.abstract_relu import ReLU
from src.mn_bab_shape import AffineForm, MN_BaB_Shape
from src.state.tags import query_tag
class TestMNBaBShape:
def test_init_with_full_argument_list(self) -> None:
lb = AffineForm(torch.rand(1, 2, 2), torch.rand(1, 2, 2))
ub = Affi... | 1,032 | 29.382353 | 65 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/sigmoid_approx_test.py | import numpy as np
import torch
import torch.nn as nn
from src.abstract_layers.abstract_network import AbstractNetwork
from src.abstract_layers.abstract_sigmoid import Sigmoid
from src.mn_bab_shape import AffineForm, MN_BaB_Shape
from src.state.tags import query_tag
from src.utilities.argument_parsing import get_confi... | 12,725 | 35.674352 | 104 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/relu_test.py | import pytest
import torch
from torch import Tensor
from src.abstract_domains.DP_f import DeepPoly_f
from src.abstract_domains.zonotope import HybridZonotope
from src.abstract_layers.abstract_relu import ReLU
from src.mn_bab_shape import AffineForm, MN_BaB_Shape
from src.state.tags import query_tag
from src.utilities.... | 9,655 | 36.866667 | 83 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/prima_optimization_test.py | import torch
from tests.test_util import optimize_output_node_bounds_with_prima_crown, toy_net
class TestPrimaOptimization:
def test_toy_net(self) -> None:
network = toy_net()[0]
input_lb = torch.tensor([-1.0, -1.0]).unsqueeze(0)
input_ub = torch.tensor([1.0, 1.0]).unsqueeze(0)
... | 1,027 | 28.371429 | 84 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/fuzzing_test.py | from typing import Callable, List, Optional, Tuple, Union
import torch
from src.abstract_layers.abstract_network import AbstractNetwork
# from src.utilities.config import AbstractDomain
from src.utilities.initialization import seed_everything
from src.utilities.loading.network import freeze_network
from tests.test_u... | 9,519 | 30.733333 | 149 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/ibp_test.py | from typing import Dict, Tuple
import torch
from torch import Tensor
from src.milp_network import MILPNetwork
from tests.test_util import get_deep_poly_bounds, toy_net
class TestIBP:
"""
We test our IBP implementation (also with respect to existing bound reusage)
"""
def test_milp_toy_example(self)... | 2,406 | 36.030769 | 135 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/unbinaryop_test.py | import shutil
import time
from pathlib import Path
import torch
from torch import nn
from src.abstract_layers.abstract_network import AbstractNetwork
from src.milp_network import MILPNetwork
from src.state.tags import layer_tag
from src.utilities.loading.network import load_onnx_model
from tests.test_util import get_... | 3,317 | 30.301887 | 84 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/reshape_test.py | import shutil
from pathlib import Path
import torch
from torch import nn
from torch.distributions import Beta
from src.abstract_domains.DP_f import DeepPoly_f
from src.abstract_domains.zonotope import HybridZonotope
from src.abstract_layers.abstract_network import AbstractNetwork
from src.utilities.loading.network im... | 2,895 | 29.166667 | 87 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/milp_test.py | import torch
from gurobipy import GRB # type: ignore[import]
from tests.gurobi_util import create_milp_model
from tests.test_util import get_deep_poly_bounds, toy_net
class TestAgainstMILP:
"""
We compare our bounds with the bounds obtained by solving the verification with a MILP solver. The MILP bounds are... | 1,892 | 30.032787 | 125 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/batch_test.py | import csv
from copy import deepcopy
import torch
from src.abstract_layers.abstract_network import AbstractNetwork
from src.branch_and_bound import BranchAndBound
from src.mn_bab_optimizer import MNBabOptimizer
from src.utilities.config import make_verifier_config
from src.utilities.loading.data import transform_and_... | 4,032 | 33.470085 | 84 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/sequential_test.py | import torch
from tests.test_util import toy_net
class TestSequential:
def test_set_layer_bounds_via_interval_propagation(self) -> None:
"""
Correct bounds found by hand on toy_net.
"""
model = toy_net()[0]
input_lb = torch.tensor([-1.0, -1.0]).unsqueeze(0)
input_... | 1,495 | 35.487805 | 75 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/determinism_test.py | import torch
from src.utilities.initialization import seed_everything
from src.utilities.loading.network import load_onnx_model
class TestDeterminism:
"""
We test our deterministic behaviour
"""
def test_determ(self) -> None:
d_type = torch.float64
seed_everything(1)
assert_... | 3,355 | 33.958333 | 142 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/reduced_parameter_sharing_test.py | import torch
from src.utilities.config import make_optimizer_config
from tests.test_util import optimize_output_node_bounds_with_prima_crown, toy_net
class TestReducedParameterSharing:
def test_toy_net(self) -> None:
network = toy_net()[0]
input_lb = torch.tensor([-1.0, -1.0]).unsqueeze(0)
... | 1,369 | 29.444444 | 86 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/leaky_gradient_min_max_test.py | import torch
from src.utilities.leaky_gradient_maximum_function import LeakyGradientMaximumFunction
from src.utilities.leaky_gradient_minimum_function import LeakyGradientMinimumFunction
leaky_gradient_minimum = LeakyGradientMinimumFunction.apply
leaky_gradient_maximum = LeakyGradientMaximumFunction.apply
class Tes... | 1,345 | 33.512821 | 88 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/einsum_test.py | from typing import Callable, Tuple, Union
import torch
from torch import Tensor
from torch.distributions.beta import Beta
from src.abstract_domains.DP_f import DeepPoly_f
from src.abstract_domains.zonotope import HybridZonotope
from src.utilities.initialization import seed_everything
from tests.test_util import _pgd_... | 3,535 | 26.2 | 92 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/disjunctive_encoding_test.py | import torch
from src.mn_bab_verifier import MNBaBVerifier
from src.state.subproblem_state import SubproblemState
from src.utilities.argument_parsing import get_config_from_json
from src.utilities.config import make_config
# from src.utilities.config import AbstractDomain
from src.utilities.output_property_form impor... | 4,014 | 34.530973 | 88 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/linear_test.py | import torch
from torch import Tensor
from src.abstract_domains.DP_f import DeepPoly_f
from src.abstract_domains.zonotope import HybridZonotope
from src.abstract_layers.abstract_linear import Linear
from src.mn_bab_shape import AffineForm, MN_BaB_Shape
from src.state.tags import query_tag
from src.utilities.config imp... | 4,292 | 35.692308 | 80 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/deeppoly_test.py | import torch
from tests.test_util import get_deep_poly_bounds, get_deep_poly_lower_bounds, toy_net
# DeepPoly paper source: https://files.sri.inf.ethz.ch/website/papers/DeepPoly.pdf
class TestDeepPoly:
"""
PRIMA_CROWN is an extension of DeepPoly. Without any optimization,
it should yield the same approx... | 1,060 | 26.921053 | 85 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/unit_tests/dependence_sets_test.py | import torch
import torch.nn as nn
from torch import Tensor
from src.abstract_layers.abstract_conv2d import Conv2d
from src.abstract_layers.abstract_max_pool2d import MaxPool2d
from src.abstract_layers.abstract_network import AbstractNetwork
from src.abstract_layers.abstract_relu import ReLU
from src.mn_bab_shape impo... | 17,973 | 37.989154 | 88 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/integration_tests/vnn22_to_as_test.py | import gzip
import numpy as np
import onnxruntime as ort # type: ignore[import]
import torch
from torch import nn
from src.abstract_layers.abstract_network import AbstractNetwork
from src.utilities.loading.network import load_onnx_model
from tests.test_util import get_deep_poly_bounds
def is_float_try(str: str) ->... | 5,518 | 31.087209 | 101 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/integration_tests/intermediate_layer_bound_opt_test.py | import csv
import torch
from src.abstract_layers.abstract_network import AbstractNetwork
from src.utilities.initialization import seed_everything
from src.utilities.loading.data import transform_and_bound
from src.utilities.loading.network import freeze_network, load_onnx_model, mnist_a_b
from tests.test_util import ... | 3,899 | 34.779817 | 122 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/integration_tests/prima_optimization_integration_test.py | import csv
import torch
from src.abstract_layers.abstract_network import AbstractNetwork
from src.utilities.initialization import seed_everything
from src.utilities.loading.data import transform_and_bound
from src.utilities.loading.network import freeze_network, mnist_a_b, mnist_conv_small
from tests.test_util import... | 6,143 | 35.571429 | 85 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/integration_tests/alpha_optimization_integration_test.py | import csv
import torch
from src.abstract_layers.abstract_network import AbstractNetwork
from src.utilities.initialization import seed_everything
from src.utilities.loading.data import transform_and_bound
from src.utilities.loading.network import freeze_network, mnist_a_b, mnist_conv_small
from tests.test_util import... | 4,879 | 34.362319 | 87 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/integration_tests/completeness_test.py | import csv
import torch
from gurobipy import GRB # type: ignore[import]
from src.abstract_layers.abstract_network import AbstractNetwork
from src.utilities.initialization import seed_everything
from src.utilities.loading.data import transform_and_bound
from src.utilities.loading.network import freeze_network, mnist_... | 5,741 | 37.536913 | 88 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/integration_tests/soundness_test.py | import csv
from copy import deepcopy
import torch
from gurobipy import GRB # type: ignore[import]
from src.abstract_layers.abstract_network import AbstractNetwork
from src.utilities.initialization import seed_everything
from src.utilities.loading.data import transform_and_bound
from src.utilities.loading.network imp... | 17,642 | 38.033186 | 88 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/integration_tests/vnn21_to_as_test.py | import os
import numpy as np
import onnxruntime as ort # type: ignore[import]
import pytest
import torch
from torch import nn
from torch.distributions.beta import Beta
from src.abstract_layers.abstract_network import AbstractNetwork
from src.utilities.initialization import seed_everything
from src.utilities.loading.... | 5,121 | 36.940741 | 88 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/integration_tests/fuzzing_tests.py | import os.path
import shutil
import time
from pathlib import Path
from typing import Callable, Tuple
import onnx # type: ignore [import]
import torch
from torch import Tensor
from src.abstract_layers.abstract_network import AbstractNetwork
from src.utilities.config import make_backsubstitution_config
from src.utilit... | 13,984 | 31.075688 | 137 | py |
mn-bab-SABR_ready | mn-bab-SABR_ready/tests/integration_tests/onnx_integration_tests.py | import gzip
import os
import re
import shutil
from pathlib import Path
from typing import Tuple
import numpy as np
# import onnx
import onnxruntime as ort # type: ignore[import]
import torch
from torch import nn
from src.abstract_layers.abstract_network import AbstractNetwork
from src.utilities.loading.network impo... | 6,655 | 35.173913 | 81 | py |
DualRE | DualRE-master/selection.py | """Select new instances given prediction and retrieval modules"""
import math
import collections
import torch
from torchtext import data
from utils import scorer
from utils.torch_utils import example_to_dict
TOKEN = data.Field(sequential=True, batch_first=True, lower=True, include_lengths=True)
RELATION = data.Field(... | 6,145 | 44.525926 | 116 | py |
DualRE | DualRE-master/train.py | """Main file for training DualRE"""
import argparse
# pylint: disable=invalid-name, missing-docstring
import math
import random
import torch
from torchtext import data
from model.predictor import Predictor
from model.selector import Selector
from model.trainer import Trainer, evaluate
from selection import get_relat... | 10,578 | 41.657258 | 119 | py |
DualRE | DualRE-master/utils/torch_utils.py | """
Utility functions for torch.
"""
import torch
from torch.optim import Optimizer
import numpy as np
# class
class MyAdagrad(Optimizer):
"""My modification of the Adagrad optimizer that allows to specify an initial
accumulater value. This mimics the behavior of the default Adagrad implementation
in Ten... | 6,900 | 31.247664 | 109 | py |
DualRE | DualRE-master/model/predictor.py | from torch import nn
import torch.nn.functional as F
from .layers import Classifier
from .encoder import RNNEncoder
class Predictor(nn.Module):
""" A sequence model for relation extraction. """
def __init__(self, opt, emb_matrix=None):
super(Predictor, self).__init__()
self.encoder = RNNEnco... | 708 | 26.269231 | 53 | py |
DualRE | DualRE-master/model/encoder.py | """
A rnn model for relation extraction, written in pytorch.
"""
import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
class PositionAwareAttention(nn.Module):
"""
A position-augmented attention layer where the attention weight is
a = T' . tanh(Ux + Vq + Wf)... | 8,907 | 34.349206 | 95 | py |
DualRE | DualRE-master/model/layers.py | from torch import nn
from torch.nn import init
class Classifier(nn.Module):
def __init__(self, opt):
super(Classifier, self).__init__()
self.hidden_dim = opt["hidden_dim"]
self.num_class = opt["num_class"]
self.linear = nn.Linear(self.hidden_dim, self.num_class)
self.linea... | 1,169 | 27.536585 | 75 | py |
DualRE | DualRE-master/model/selector.py | from torch import nn
from .layers import Classifier
from .encoder import RNNEncoder
class Selector(nn.Module):
""" A sequence model for relation extraction. """
def __init__(self, opt, emb_matrix=None):
super(Selector, self).__init__()
self.encoder = RNNEncoder(opt, emb_matrix)
self.... | 634 | 24.4 | 53 | py |
DualRE | DualRE-master/model/trainer.py | """
A rnn model for relation extraction, written in pytorch.
"""
import math
import time
import os
from datetime import datetime
from shutil import copyfile
import numpy as np
import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
from torchtext import data
from utils imp... | 14,526 | 38.368564 | 102 | py |
PSGLoss | PSGLoss-main/data.py | import os
from PIL import Image
import torch.utils.data as data
import torchvision.transforms as transforms
import random
def cv_random_flip(img, label):
flip_flag = random.randint(0, 1)
if flip_flag == 1:
img = img.transpose(Image.FLIP_LEFT_RIGHT)
label = label.transpose(Image.FLIP_LEFT_RIGHT)... | 7,946 | 36.485849 | 124 | py |
PSGLoss | PSGLoss-main/ystrain.py | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import pdb, os, argparse
from datetime import datetime
from model.ysmodel import *
from data import get_loader
from utils import clip_gradient, adjust_lr
# seed1 = 1026
# np.random.seed(seed1)
# torch.manual_seed(see... | 5,861 | 36.819355 | 273 | py |
PSGLoss | PSGLoss-main/test_ys.py | import torch
import torch.nn.functional as F
import numpy as np
import pdb, os, argparse
from scipy import misc
from model.ysmodel import *
from data import test_dataset2
import time
import cv2
def get_test_info(sal_mode='ECSSD'):
if sal_mode == 'ECSSD':
image_root = '/home/syang/ysdata/salientobj_datas... | 4,976 | 41.177966 | 114 | py |
PSGLoss | PSGLoss-main/model/ResNet.py | import torch.nn as nn
import math
def conv3x3(in_planes, out_planes, stride=1, dilation = 1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, dilation =dilation,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = ... | 9,018 | 32.403704 | 97 | py |
PSGLoss | PSGLoss-main/model/ysmodel.py | import torch
import torch.nn as nn
import torchvision.models as models
from model.ResNet import B2_ResNet,B1_ResNet,B1_DRN
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
class DiceLoss(nn.Module):
def __init__(self):
super(DiceLoss, self).__init__()
... | 11,529 | 35.37224 | 103 | py |
AFC | AFC-master/inclearn/train.py | import copy
import json
import logging
import os
import pickle
import random
import statistics
import sys
import time
import numpy as np
import torch
import yaml
from inclearn.lib import factory
from inclearn.lib import logger as logger_lib
from inclearn.lib import metrics, results_utils, utils
logger = logging.getL... | 11,516 | 33.689759 | 100 | py |
AFC | AFC-master/inclearn/convnet/ucir_resnet.py | import torch.nn as nn
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, st... | 3,464 | 29.394737 | 87 | py |
AFC | AFC-master/inclearn/convnet/my_resnet2.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from inclearn.lib import pooling
class DownsampleStride(nn.Module):
def __init__(self, n=2):
super(DownsampleStride, self).__init__()
self._n = n
def forward(self, x):
x = x[..., ::2, ::2]
return torch.cat((x... | 6,532 | 28.035556 | 92 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.