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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AgML | AgML-main/experiments/benchmarking/classification_distributed.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 6,726 | 31.814634 | 83 | py |
AgML | AgML-main/experiments/benchmarking/detection_lightning_local.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 26,638 | 37.607246 | 107 | py |
AgML | AgML-main/experiments/benchmarking/miou_evaluation.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 3,988 | 31.169355 | 91 | py |
AgML | AgML-main/experiments/benchmarking/detection_modeling.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 8,868 | 41.845411 | 96 | py |
AgML | AgML-main/experiments/benchmarking/classification_lightning_resnet50.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 9,011 | 33.136364 | 90 | py |
AgML | AgML-main/agml/models/base.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 6,551 | 33.851064 | 96 | py |
AgML | AgML-main/agml/models/classification.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 8,921 | 40.887324 | 81 | py |
AgML | AgML-main/agml/models/losses.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 2,104 | 34.677966 | 74 | py |
AgML | AgML-main/agml/models/preprocessing.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 8,321 | 41.676923 | 85 | py |
AgML | AgML-main/agml/models/segmentation.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 14,006 | 42.231481 | 86 | py |
AgML | AgML-main/agml/models/tools.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 4,452 | 32.734848 | 103 | py |
AgML | AgML-main/agml/models/__init__.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 1,690 | 37.431818 | 90 | py |
AgML | AgML-main/agml/models/detection.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 22,088 | 41.397313 | 87 | py |
AgML | AgML-main/agml/models/metrics/accuracy.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 2,632 | 38.298507 | 84 | py |
AgML | AgML-main/agml/models/metrics/map.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 9,795 | 39.147541 | 85 | py |
AgML | AgML-main/agml/backend/random.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 983 | 32.931034 | 74 | py |
AgML | AgML-main/agml/backend/tftorch.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 10,087 | 33.081081 | 104 | py |
AgML | AgML-main/agml/backend/__init__.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 1,371 | 33.3 | 76 | py |
AgML | AgML-main/agml/viz/labels.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 5,095 | 38.8125 | 87 | py |
AgML | AgML-main/agml/viz/tools.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 6,828 | 31.061033 | 82 | py |
AgML | AgML-main/agml/viz/general.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 4,786 | 35.823077 | 87 | py |
AgML | AgML-main/agml/utils/general.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 4,734 | 29.352564 | 94 | py |
AgML | AgML-main/agml/data/object.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 9,127 | 36.875519 | 86 | py |
AgML | AgML-main/agml/data/tools.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 7,351 | 39.395604 | 84 | py |
AgML | AgML-main/agml/data/experimental.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 3,748 | 34.704762 | 77 | py |
AgML | AgML-main/agml/data/manager.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 16,197 | 41.626316 | 94 | py |
AgML | AgML-main/agml/data/loader.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 75,355 | 43.828079 | 90 | py |
AgML | AgML-main/agml/data/multi_loader.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 66,025 | 42.639128 | 94 | py |
AgML | AgML-main/agml/data/managers/transform_helpers.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 6,074 | 36.5 | 76 | py |
AgML | AgML-main/agml/data/managers/training.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 15,928 | 40.481771 | 82 | py |
AgML | AgML-main/agml/data/managers/transforms.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 26,516 | 48.288104 | 89 | py |
AgML | AgML-main/agml/data/exporters/tensorflow.py | # Copyright 2021 UC Davis Plant AI and Biophysics Lab
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law ... | 9,639 | 38.02834 | 79 | py |
NeLLoC | NeLLoC-main/utils.py | '''
Code by Hrituraj Singh
Indian Institute of Technology Roorkee
'''
from torchvision import datasets, transforms
import configparser
import os
import torch.nn.functional as F
import torch
import numpy as np
import matplotlib.pyplot as plt
from torch.autograd import Variable
from torch.nn.utils import weight_norm as ... | 6,542 | 29.863208 | 96 | py |
NeLLoC | NeLLoC-main/model.py | import torch.nn as nn
import torch
import torch.nn.functional as F
class MaskedCNN(nn.Conv2d):
def __init__(self, mask_type, *args, **kwargs):
self.mask_type = mask_type
assert mask_type in ['A', 'B'], "Unknown Mask Type"
super(MaskedCNN, self).__init__(*args, **kwargs)
self.regi... | 2,366 | 31.875 | 116 | py |
NeLLoC | NeLLoC-main/train.py | import os
import torch
from torch import optim
from torch.utils import data
import torch.nn as nn
from model import *
import numpy as np
import torchvision
import torch.nn.functional as F
from torch.optim import lr_scheduler
from torchvision import datasets, transforms
from torch.autograd import Variable
from utils imp... | 2,146 | 26.883117 | 118 | py |
NeLLoC | NeLLoC-main/coder/distributions.py | import torch
import numpy as np
rescaling = lambda x : (x - .5) * 2.
rescaling_inv = lambda x : .5 * x + .5
def discretized_mix_logistic_cdftable(means, log_scales,pi, alpha=0.0001):
x=rescaling(torch.arange(0,256)/255.).view(256,1).repeat(1,10).to(means.device)
centered_x = x - means
inv_stdv = torc... | 1,553 | 33.533333 | 83 | py |
NeLLoC | NeLLoC-main/coder/ans_coder.py | from coder.distributions import *
import numpy as np
class ANSStack(object):
def __init__(self, s_prec , t_prec, p_prec):
self.s_prec=s_prec
self.t_prec=t_prec
self.p_prec=p_prec
self.t_mask = (1 << t_prec) - 1
self.s_min=1 << s_prec - t_prec
self.s_max=1 << s_prec... | 4,241 | 36.875 | 137 | py |
NeLLoC | NeLLoC-main/coder/ac_coder.py | from model import *
from decimal import *
from coder.distributions import *
tensor2decimal= lambda x : Decimal(str(x.cpu().item()))
def bin_2_float(binary):
prob = Decimal('0.0')
cur_prob=Decimal('0.5')
for i in binary:
prob=prob+cur_prob* int(i)
cur_prob*=Decimal('0.5')
ret... | 4,462 | 33.596899 | 103 | py |
NeLLoC | NeLLoC-main/batch_coder/distributions.py | import torch
import numpy as np
rescaling = lambda x : (x - .5) * 2.
rescaling_inv = lambda x : .5 * x + .5
def discretized_mix_logistic_cdftable(means, log_scales,pi, alpha=0.0001):
bs=means.size(0)
pi=pi.unsqueeze(1)
x=rescaling(torch.arange(0,256)/255.).view(1,256,1).repeat(bs,1,10)
centered_... | 2,159 | 35 | 115 | py |
NeLLoC | NeLLoC-main/batch_coder/ians_coder.py | import torch
import numpy as np
from batch_coder.distributions import *
s_prec = 64
t_prec = 32
t_mask = (1 << t_prec) - 1
s_min = 1 << s_prec - t_prec
s_max = 1 << s_prec
s_prec_u,t_prec_u=np.uint8(s_prec),np.uint8(t_prec)
def get_length(s,t_stack):
return ((len(t_stack))*t_prec+sum(len(bin(i)) for i in s... | 4,562 | 37.025 | 150 | py |
onnx | onnx-main/docs/docsgen/source/conf.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0622
# type: ignore
import os
import sys
import warnings
import onnx
sys.path.append(os.path.abspath(os.path.dirname(__file__)))
# -- Project information -----------------------------------------------------
autho... | 3,121 | 24.590164 | 125 | py |
onnx | onnx-main/onnx/tools/net_drawer.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# A library and utility for drawing ONNX nets. Most of this implementation has
# been borrowed from the caffe2 implementation
# https://github.com/pytorch/pytorch/blob/master/caffe2/python/net_drawer.py
#
# The script takes two required ar... | 4,901 | 30.625806 | 86 | py |
onnx | onnx-main/onnx/reference/ops/op_lp_pool.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221,R0913,R0914
import numpy as np
from onnx.reference.ops._op_common_pool import CommonPool
class LpPool(CommonPool):
def _run( # type: ignore
self,
x,
auto_pad=None,
ceil_mode=... | 1,216 | 27.97619 | 119 | py |
onnx | onnx-main/onnx/reference/ops/op_hamming_window.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op_common_window import _CommonWindow
class HammingWindow(_CommonWindow):
"""
Returns
:math:`\\omega_n = \\alpha - \\beta \\cos \\left( \\frac{\\pi n}{N-1}... | 811 | 29.074074 | 83 | py |
onnx | onnx-main/onnx/reference/ops/op_hann_window.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op_common_window import _CommonWindow
class HannWindow(_CommonWindow):
"""
Returns
:math:`\\omega_n = \\sin^2\\left( \\frac{\\pi n}{N-1} \\right)`
wher... | 690 | 27.791667 | 78 | py |
onnx | onnx-main/onnx/reference/ops/op_col2im.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,W0221
import numpy as np
from onnx.reference.op_run import OpRun
from onnx.reference.ops._op_common_indices import _get_indices, _is_out
def _col2im_shape_check_2d(X, output_shape, kernel_shape, dilations,... | 8,046 | 37.319048 | 123 | py |
onnx | onnx-main/onnx/reference/ops/op_resize.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=C0123,C3001,R0912,R0913,R0914,R1730,W0221,W0613
from typing import Any, Callable, List, Optional, Tuple
import numpy as np
from onnx.reference.op_run import OpRun
def _cartesian(
arrays: List[np.ndarray], out: Op... | 15,638 | 32.274468 | 102 | py |
onnx | onnx-main/onnx/reference/ops/op_stft.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0913,R0914,R0915,W0613,W0221
import numpy as np
from onnx.reference.op_run import OpRun
from onnx.reference.ops.op_concat_from_sequence import _concat_from_sequence
from onnx.reference.ops.op_dft import _cfft as _dft
f... | 5,591 | 31.511628 | 97 | py |
onnx | onnx-main/onnx/reference/ops/op_blackman_window.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=W0221
import numpy as np
from onnx.reference.ops._op_common_window import _CommonWindow
class BlackmanWindow(_CommonWindow):
"""
Returns
:math:`\\omega_n = 0.42 - 0.5 \\cos \\left( \\frac{2\\pi n}{N-1} \\... | 932 | 27.272727 | 79 | py |
onnx | onnx-main/onnx/reference/ops/op_grid_sample.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# pylint: disable=R0912,R0913,R0914,R0915,R1702,R1716,W0221
import numbers
from typing import List
import numpy as np
from onnx.reference.op_run import OpRun
from onnx.reference.ops.op_resize import _get_all_coords
class GridSample(Op... | 13,472 | 35.217742 | 108 | py |
onnx | onnx-main/onnx/backend/test/case/node/gridsample.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
class GridSample(Base):
@staticmethod
def export_gridsample() -> None:
node = onnx.helper.make_... | 19,682 | 29.658879 | 87 | py |
onnx | onnx-main/onnx/backend/test/case/node/resize.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import onnx
from onnx.backend.test.case.base import Base
from onnx.backend.test.case.node import expect
from onnx.reference.ops.op_resize import _cubic_coeffs as cubic_coeffs
from onnx.reference.ops.op_resize import (... | 51,616 | 29.114936 | 104 | py |
onnx | onnx-main/onnx/backend/test/runner/__init__.py | # Copyright (c) ONNX Project Contributors
#
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
import functools
import glob
import os
import re
import shutil
import sys
import tarfile
import tempfile
import time
import unittest
from collections import defaultdict
from typing import Any, Callable... | 19,487 | 39.181443 | 104 | py |
onnx | onnx-main/onnx/test/reference_evaluator_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# type: ignore
# pylint: disable=C3001,C0302,C0415,R0904,R0913,R0914,R0915,W0221,W0707
"""
You can run a specific test by using the following syntax.
::
python onnx/test/reference_evaluator_test.py TestReferenceEvaluator.test_functio... | 145,760 | 37.469517 | 115 | py |
onnx | onnx-main/onnx/test/reference_evaluator_backend_test.py | # Copyright (c) ONNX Project Contributors
# SPDX-License-Identifier: Apache-2.0
# type: ignore
# pylint: disable=C0415,R0912,R0913,R0914,R0915,W0613,W0640,W0703
"""
These test evaluates the python runtime (class ReferenceEvaluator) against
all the backend tests (in onnx/backend/test/case/node) and checks
the runtime p... | 34,178 | 38.106407 | 127 | py |
adagrid | adagrid-main/uniform-negative-sampling/adagrid_uniform_negative_sampling.py | from pathlib import Path
import torch.multiprocessing
torch.multiprocessing.set_sharing_strategy('file_system')
import argparse
import copy
import math
import random
import numpy as np
import pandas as pd
from sklearn.metrics import *
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoade... | 14,995 | 30.241667 | 132 | py |
adagrid | adagrid-main/community-ratio-based-negative-sampling/adagrid_community_ratio_based_negative_sampling.py | from pathlib import Path
import torch.multiprocessing
torch.multiprocessing.set_sharing_strategy('file_system')
import argparse
import copy
import math
import random
import numpy as np
import pandas as pd
from sklearn.metrics import *
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoade... | 19,954 | 30.979167 | 132 | py |
SMERF | SMERF-main/scripts/adv_train.py | """
This is an example of how to use ART and Keras to perform adversarial training using data generators for CIFAR10
"""
import keras
import numpy as np
from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D, Input, BatchNormalization
from keras.models import Model
from keras.preprocessing.image import ImageData... | 9,861 | 43.224215 | 128 | py |
SMERF | SMERF-main/smerf/explanations.py | import numpy as np
import imp
textcolorutils = imp.load_source('textcolor_utils', '../smerf/textcolor_utils.py')
import innvestigate
import keras
import keras.backend as K
import tensorflow as tf
import cv2
import pickle
import os
from tqdm import tqdm
# NOTE Helper functions for saliency methods that are not support... | 13,998 | 45.97651 | 156 | py |
SMERF | SMERF-main/smerf/grad_cam_utils.py | """
Source code adapted from https://github.com/wawaku/grad-cam-keras
"""
from keras.preprocessing import image
from tensorflow.python.framework import ops
import keras.backend as K
import tensorflow as tf
import numpy as np
import keras
import cv2
import os, gc
from .models import TextBoxCNN as TextBoxCNN
from .mode... | 5,486 | 33.727848 | 117 | py |
SMERF | SMERF-main/smerf/shap_utils.py | import shap
import tensorflow as tf
import keras.backend as K
import numpy as np
from smerf.models import *
import gc
# Uses SHAP library to obtain feature attributions
def shap_run(model, x_sample, y_sample, x_train, exp_no, model_type):
## NOTE due to complications in keras and TF versions (this code works only ... | 1,980 | 40.270833 | 97 | py |
SMERF | SMERF-main/smerf/textbox_data.py | import numpy as np
import PIL
from PIL import Image, ImageDraw, ImageFont, ImageEnhance
from torch.utils.data import Dataset, DataLoader
import os
from smerf.eval import setup_bboxes
import pickle
DATA_DIR = '../data/'
class TextBoxDataset(Dataset):
def __init__(self, X, y):
self.X = X
self.y = y
... | 9,823 | 38.773279 | 200 | py |
SMERF | SMERF-main/smerf/models.py | import keras
from keras.layers.pooling import GlobalAveragePooling1D, GlobalAveragePooling2D
import numpy as np
from keras.utils.np_utils import to_categorical
import os
import keras.backend as K
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.metrics import categorical_accuracy
from keras.applica... | 10,558 | 45.928889 | 159 | py |
SMERF | SMERF-main/smerf/lime_utils.py | import lime
from lime import lime_image
from lime.wrappers.scikit_image import SegmentationAlgorithm
import tensorflow as tf
import keras.backend as K
import numpy as np
from smerf.models import *
# Uses LIME library to obtain feature attributions
def lime_run(model, x_sample, y_sample, x_train, exp_no):
n, h, w, ... | 1,723 | 45.594595 | 101 | py |
cpnest | cpnest-master/docs/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# CPNest documentation build configuration file, created by
# sphinx-quickstart on Thu Dec 7 13:57:03 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# aut... | 5,836 | 30.896175 | 85 | py |
CR-VAE | CR-VAE-main/CRVAE_demo.py | # -*- coding: utf-8 -*-
"""
Created on Sat Aug 6 20:00:04 2022
@author: 61995
"""
import torch
import numpy as np
import matplotlib.pyplot as plt
from models.cgru_error import CRVAE, VRAE4E, train_phase1, train_phase2
import scipy.io
device = torch.device('cuda')
X_np = np.load('henon.npy').T
dim = X_np.shape[-... | 2,116 | 23.333333 | 99 | py |
CR-VAE | CR-VAE-main/models/cgru_error.py | # -*- coding: utf-8 -*-
"""
Created on Sat Aug 6 20:01:33 2022
@author: 61995
"""
import torch
import torch.nn as nn
import numpy as np
from copy import deepcopy
import matplotlib.pyplot as plt
from metrics.visualization_metrics import visualization
import torch.optim as optim
class GRU(nn.Module):
def __init_... | 20,309 | 30.734375 | 132 | py |
ulysses | ulysses-master/doc/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 5,441 | 28.737705 | 79 | py |
Tatoeba-Challenge | Tatoeba-Challenge-master/scripts/convert_marian_tatoeba_to_pytorch.py | # Copyright 2020 The HuggingFace Team. 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 applicabl... | 34,591 | 25.94081 | 175 | py |
defend_framework | defend_framework-main/DPA_certified_train.py | import torch
import os
import json
import time
import numpy as np
from utils.data_processing import MNIST17DataPreprocessor, MNISTDataPreprocessor, IMDBDataPreprocessor, \
EmberDataPreProcessor, EMBER_DATASET, EmberPoisonDataPreProcessor, MNIST01DataPreprocessor, \
MNIST17LimitedDataPreprocessor, FMNISTDataPr... | 2,833 | 39.485714 | 115 | py |
defend_framework | defend_framework-main/gen_attack_dataset.py | import random
import os
import json
import time
import numpy as np
import tensorflow as tf
from tensorflow import keras
from utils.data_processing import MNISTDataPreprocessor, MNIST17DataPreprocessor, MNIST01DataPreprocessor, \
CIFAR02DataPreprocessor
from models.MNISTModel import MNISTModel, MNIST17Model, MNIST... | 5,498 | 42.642857 | 118 | py |
defend_framework | defend_framework-main/models/LiRPAModel.py | from abc import ABC, abstractmethod
from torch.utils.data import TensorDataset, DataLoader
import torch.optim as optim
from torch.nn import CrossEntropyLoss
from auto_LiRPA import BoundedModule, BoundedTensor
from auto_LiRPA.perturbations import *
from auto_LiRPA.eps_scheduler import LinearScheduler, AdaptiveScheduler... | 12,734 | 53.191489 | 123 | py |
defend_framework | defend_framework-main/models/EmberLiRPAModel.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from models.LiRPAModel import LiRPAModel
class EmberModel(LiRPAModel):
def __init__(self, n_features, n_classes, args, device, lr=1e-3):
super(EmberModel, self).__init__([1, 1, n_features], n_classes, args, device, mlp_... | 1,310 | 30.214286 | 112 | py |
defend_framework | defend_framework-main/models/MNISTModel.py | from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Dropout
from tensorflow.keras.layers import Conv2D, AveragePooling2D, MaxPooling2D
from tensorflow.keras.regularizers import l2
from models.Model import Model
class MNISTModel(Model):
d... | 4,251 | 38.37037 | 94 | py |
defend_framework | defend_framework-main/models/MNISTLiRPAModel.py | from torchvision import transforms
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from models.LiRPAModel import LiRPAModel
class MNISTModel(LiRPAModel):
def __init__(self, n_features, n_classes, args, device, lr=1e-3):
super(MNISTModel, self).__init__(n_features, n_classes, args... | 1,294 | 30.585366 | 93 | py |
defend_framework | defend_framework-main/models/Model.py | from abc import ABC, abstractmethod
import os
import numpy as np
from tensorflow.keras.models import load_model
class Model(ABC):
def __init__(self, input_shape, n_classes, lr):
self.input_shape = input_shape
self.n_classes = n_classes
self.lr = lr
self.callback = None
sel... | 1,351 | 30.44186 | 113 | py |
defend_framework | defend_framework-main/models/IMDBTransformerModel.py | import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from models.Model import Model
class TransformerBlock(layers.Layer):
def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):
super(TransformerBlock, self).__init__()
self.att = layers.MultiHeadAttention(n... | 2,535 | 38.015385 | 84 | py |
defend_framework | defend_framework-main/models/EmberModel.py | from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Input, Dropout, BatchNormalization, Activation
from tensorflow.keras.layers import Conv2D, AveragePooling2D
from tensorflow.keras.regularizers import l2
from models.Model import Model
class EmberMod... | 2,009 | 36.924528 | 95 | py |
defend_framework | defend_framework-main/models/CIFAR10Model.py | from tensorflow import keras
from tensorflow.keras.layers import BatchNormalization, Activation, Conv2D, Input, AveragePooling2D, Flatten, Dense
from tensorflow.keras.regularizers import l2
from tensorflow.keras.optimizers import Adam
import numpy as np
from tensorflow.keras.callbacks import LearningRateScheduler, Redu... | 6,009 | 33.342857 | 115 | py |
defend_framework | defend_framework-main/utils/dataaug.py | from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow import keras
import numpy as np
import torch
from torchvision import transforms
def DataGeneratorForMNIST():
datagen = ImageDataGenerator(
# set input mean to 0 over the dataset
featurewise_center=False,
# ... | 5,425 | 38.318841 | 119 | py |
defend_framework | defend_framework-main/utils/data_processing.py | import os
import pickle
import numpy as np
from tensorflow.keras.datasets import mnist, imdb, cifar10, fashion_mnist
from tensorflow import keras
import ember
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, KBinsDiscretizer, MinMaxScaler
from ... | 29,834 | 46.132701 | 118 | py |
defend_framework | defend_framework-main/utils/train_utils.py | import numpy as np
from tensorflow import keras
from tqdm import trange
import os
from utils.dataaug import DataGeneratorForMNIST, MNISTDataGenerator, EmberDataGenerator, CIFARDataGenerator
from utils import EMBER_DATASET, IMAGE_DATASET
def train_many(data_loader, model, args, aggregate_result, aggregate_noise_resul... | 4,535 | 50.545455 | 120 | py |
secml-zoo | secml-zoo-master/models/mnist/mnist_cnn.py | """
.. module:: MNISTCNN
:synopsis: A CNN model for MNIST
.. moduleauthor:: Ambra Demontis <ambra.demontis@diee.unica.it>
"""
from collections import OrderedDict
import torch
from torch import nn, optim
from secml.ml.classifiers import CClassifierPyTorch
class Flatten(nn.Module):
def __init__(self):
... | 6,209 | 36.409639 | 88 | py |
secml-zoo | secml-zoo-master/models/mnist/mnist3c_cnn.py | """
.. module:: MNIST3cCNN
:synopsis: CNN to be trained on MNIST 3-classes dataset
.. moduleauthor:: Maura Pintor <maura.pintor@unica.it>
.. moduleauthor:: Marco Melis <marco.melis@unica.it>
"""
import torch
from torch import nn, optim
from secml.ml.classifiers import CClassifierPyTorch
class MNIST3cCNN(nn.Modu... | 1,486 | 29.346939 | 75 | py |
secml-zoo | secml-zoo-master/models/iCubWorld/utils.py | import numpy as np
from secml.array import CArray
from secml.ml import CNormalizerDNN
from .alexnet import alexnet
def attach_alexnet(clf):
"""Attach AlexNet (as a preprocessor) to input CClassifier.
The output of `classifier:4` layer is used as input for the classifier.
"""
clf.preprocess = CNor... | 1,021 | 30.9375 | 76 | py |
secml-zoo | secml-zoo-master/models/iCubWorld/alexnet.py | """
.. module:: AlexNet
:synopsis: AlexNet Convolutional Neural Network
.. moduleauthor:: Marco Melis <marco.melis@unica.it>
"""
from torchvision import models
from secml.ml.classifiers import CClassifierPyTorch
from secml.ml.features.normalization import CNormalizerMeanStd
def alexnet():
"""CClassifierPyTo... | 803 | 27.714286 | 79 | py |
secml-zoo | secml-zoo-master/models/iCubWorld/iCubWorld28/tests/test_models_icubworld28.py | from models.iCubWorld.tests import CICubWorldTestCases
from secml.ml.classifiers.multiclass import CClassifierMulticlassOVA
from secml.utils import fm, pickle_utils
from models.iCubWorld.utils import ds_numpy_to_pytorch
class TestModelsICubWorld28(CICubWorldTestCases):
"""Unittests for iCubWorld28 models."""
... | 1,080 | 29.027778 | 73 | py |
secml-zoo | secml-zoo-master/models/iCubWorld/iCubWorld28/_exporters/icubworld28-day4-svm.py | import sys
sys.path.insert(0, '../../../')
from svm_ova import svm_ova
from iCubWorld.utils import attach_alexnet, ds_numpy_to_pytorch
from secml.data.loader import CDataLoaderICubWorld28
from secml.data.splitter import CDataSplitter
from secml.ml.peval.metrics import CMetricAccuracy
dl = CDataLoaderICubWorld28()
dl... | 1,418 | 20.5 | 76 | py |
secml-zoo | secml-zoo-master/models/iCubWorld/iCubWorld28/_exporters/icubworld28-day4-svm-rbf.py | import sys
sys.path.insert(0, '../../../')
from svm_rbf_ova import svm_rbf_ova
from iCubWorld.utils import attach_alexnet, ds_numpy_to_pytorch
from secml.data.loader import CDataLoaderICubWorld28
from secml.data.splitter import CDataSplitter
from secml.ml.peval.metrics import CMetricAccuracy
dl = CDataLoaderICubWorl... | 1,536 | 21.602941 | 78 | py |
secml-zoo | secml-zoo-master/models/iCubWorld/iCubWorld7/tests/test_models_icubworld7.py | from models.iCubWorld.tests import CICubWorldTestCases
from secml.ml.classifiers.multiclass import CClassifierMulticlassOVA
from secml.utils import fm, pickle_utils
from models.iCubWorld.utils import ds_numpy_to_pytorch
class TestModelsICubWorld7(CICubWorldTestCases):
"""Unittests for iCubWorld7 models."""
... | 1,074 | 28.861111 | 73 | py |
secml-zoo | secml-zoo-master/models/iCubWorld/iCubWorld7/_exporters/icubworld7-day4-svm.py | import sys
sys.path.insert(0, '../../../')
from svm_ova import svm_ova
from iCubWorld.utils import attach_alexnet, ds_numpy_to_pytorch
from secml.data.loader import CDataLoaderICubWorld28
from secml.data.splitter import CDataSplitter
from secml.ml.peval.metrics import CMetricAccuracy
dl = CDataLoaderICubWorld28()
dl... | 1,439 | 20.818182 | 76 | py |
secml-zoo | secml-zoo-master/models/iCubWorld/iCubWorld7/_exporters/icubworld7-day4-svm-rbf.py | import sys
sys.path.insert(0, '../../../')
from svm_rbf_ova import svm_rbf_ova
from iCubWorld.utils import attach_alexnet, ds_numpy_to_pytorch
from secml.data.loader import CDataLoaderICubWorld28
from secml.ml.peval.metrics import CMetricAccuracy
dl = CDataLoaderICubWorld28()
dl.verbose = 2
tr = dl.load(
ds_typ... | 1,513 | 21.597015 | 78 | py |
ActiveLearningForHumanPose | ActiveLearningForHumanPose-main/code/main.py | import os
import copy
import logging
import cv2
import numpy as np
import pandas as pd
from tqdm import tqdm
from matplotlib import pyplot as plt
import torch
import torch.utils.data
from torch.utils.tensorboard import SummaryWriter
from torch.optim.lr_scheduler import ReduceLROnPlateau as ReduceLROnPlateau
from con... | 41,564 | 42.342023 | 168 | py |
ActiveLearningForHumanPose | ActiveLearningForHumanPose-main/code/activelearning.py | import os
import cv2
import torch
import torch.utils.data
import numpy as np
import logging
from tqdm import tqdm
from pathlib import Path
from sklearn.metrics import pairwise_distances
from skimage.feature import peak_local_max
from scipy.special import softmax as softmax_fn
from scipy.stats import entropy as entropy_... | 43,791 | 39.774674 | 160 | py |
ActiveLearningForHumanPose | ActiveLearningForHumanPose-main/code/dataloader.py | import os
import copy
import logging
from pathlib import Path
import cv2
import scipy.io
import numpy as np
from tqdm import tqdm
from matplotlib import pyplot as plt
from matplotlib.patches import Circle
import torch
import torch.utils.data
import albumentations as albu
from utils import heatmap_generator
from util... | 52,432 | 41.182623 | 137 | py |
ActiveLearningForHumanPose | ActiveLearningForHumanPose-main/code/utils.py | import os
import sys
import copy
import math
from pathlib import Path
import torch
import scipy.io
import numpy as np
from tqdm import tqdm
from adjustText import adjust_text
from matplotlib import pyplot as plt
from matplotlib.patches import Circle, Rectangle
import umap
from sklearn.decomposition import PCA
plt.st... | 14,742 | 36.513995 | 130 | py |
ActiveLearningForHumanPose | ActiveLearningForHumanPose-main/code/activelearning_viz.py | import os
import cv2
import math
import torch
import torch.utils.data
import numpy as np
import logging
from tqdm import tqdm
from pathlib import Path
from sklearn.metrics import pairwise_distances
from skimage.feature import peak_local_max
from scipy.special import softmax as softmax_fn
from scipy.stats import entropy... | 89,213 | 43.056296 | 162 | py |
ActiveLearningForHumanPose | ActiveLearningForHumanPose-main/code/autograd_hacks.py | """
Credits: https://github.com/cybertronai/autograd-hacks
Library for extracting interesting quantites from autograd, see README.md
Not thread-safe because of module-level variables
Notation:
o: number of output classes (exact Hessian), number of Hessian samples (sampled Hessian)
n: batch-size
do: output dimension (... | 9,972 | 33.628472 | 133 | py |
ActiveLearningForHumanPose | ActiveLearningForHumanPose-main/code/models/stacked_hourglass/StackedHourglass.py | '''
Baseline Architecture: Stacked Hourglass
https://github.com/princeton-vl/pytorch_stacked_hourglass
'''
import torch
from torch import nn
from .layers import Conv, Hourglass, Pool, Residual
class Merge(nn.Module):
'''
'''
def __init__(self, x_dim, y_dim):
super(Merge, self).__init__()
... | 5,127 | 40.024 | 122 | py |
ActiveLearningForHumanPose | ActiveLearningForHumanPose-main/code/models/stacked_hourglass/layers.py | import torch
from torch import nn
Pool = nn.MaxPool2d
def batchnorm(x):
return nn.BatchNorm2d(x.size()[1])(x)
class Conv(nn.Module):
'''
Initializes: Conv, Conv-Relu or Conv-Relu-BN combinaton
'''
def __init__(self, inp_dim, out_dim, kernel_size, stride=1, bn=False, relu=True):
'''
... | 4,697 | 31.625 | 114 | py |
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