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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episodic-curiosity | episodic-curiosity-master/episodic_curiosity/keras_checkpoint.py | # coding=utf-8
# Copyright 2019 Google LLC.
#
# 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 or agreed ... | 3,669 | 38.891304 | 79 | py |
OrcaNet | OrcaNet-master/orcanet/core.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Core scripts for the OrcaNet package.
"""
import os
import toml
import warnings
import time
from datetime import timedelta
import tensorflow as tf
import orcanet.backend as backend
from orcanet.utilities.visualization import update_summary_plot
from orcanet.in_out imp... | 37,957 | 38.05144 | 116 | py |
OrcaNet | OrcaNet-master/orcanet/h5_generator.py | import h5py
import time
import numpy as np
import tensorflow as tf
import tensorflow.keras as ks
class Hdf5BatchGenerator(ks.utils.Sequence):
def __init__(self, files_dict,
batchsize=64,
key_x_values="x",
key_y_values="y",
sample_modifier=None,
... | 19,171 | 35.798464 | 93 | py |
OrcaNet | OrcaNet-master/orcanet/logging.py | """
Scripts for writing the logfiles.
"""
import numpy as np
import os
import tensorflow.keras as ks
from datetime import datetime
from shutil import move
import orcanet
class TrainfileLogger:
def __init__(self, log_file, column_names):
"""
For writing the training log file in a nice format.
... | 20,985 | 34.690476 | 124 | py |
OrcaNet | OrcaNet-master/orcanet/in_out.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Utility code regarding user input.
"""
import os
import shutil
import h5py
import numpy as np
from inspect import signature
# moved into IOHandler.get_batch for speed up; tensorflow import is slow!
# from orcanet.h5_generator import Hdf5BatchGenerator
def get_subfol... | 28,848 | 32.236175 | 97 | py |
OrcaNet | OrcaNet-master/orcanet/model_builder.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Scripts for making specific models.
"""
import warnings
import toml
from datetime import datetime
import tensorflow as tf
import tensorflow.keras as ks
import tensorflow.keras.layers as layers
from orcanet.builder_util.builders import BlockBuilder
class ModelBuilder... | 14,663 | 34.678832 | 107 | py |
OrcaNet | OrcaNet-master/orcanet/backend.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Code for training and validating NN's, as well as evaluating them.
"""
import time
import h5py
import numpy as np
import os
import orcanet
from orcanet.logging import BatchLogger
import orcanet.utilities.nn_utilities as nn_utilities
from orcanet.in_out import h5_get_nu... | 11,537 | 34.721362 | 89 | py |
OrcaNet | OrcaNet-master/orcanet/builder_util/layer_blocks.py | import tensorflow as tf
import tensorflow.keras.backend as K
import tensorflow.keras as ks
import tensorflow.keras.layers as layers
import medgeconv
from orcanet.misc import get_register
# for loading via toml and orcanet custom objects
blocks, register = get_register()
# edge conv blocks
register(medgeconv.DisjointEd... | 29,127 | 34.306667 | 106 | py |
OrcaNet | OrcaNet-master/orcanet/builder_util/builders.py | import inspect
import warnings
import tensorflow.keras as ks
import tensorflow.keras.layers as layers
import orcanet.builder_util.layer_blocks as layer_blocks
class BlockBuilder:
"""
Builds single-input block-wise sequential neural network.
Parameters
----------
defaults : dict or None
D... | 8,125 | 31.374502 | 84 | py |
OrcaNet | OrcaNet-master/orcanet/utilities/nn_utilities.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Utility functions used for training a NN."""
import warnings
import numpy as np
import h5py
import os
import time
import tensorflow.keras as ks
from functools import reduce
class RaiseOnNaN(ks.callbacks.Callback):
"""
Callback that terminates training when a N... | 9,113 | 32.630996 | 80 | py |
OrcaNet | OrcaNet-master/orcanet/tests/test_logging.py | from unittest import TestCase
from unittest.mock import MagicMock
import os
from tensorflow.keras.models import Model
import tensorflow.keras.layers as layers
import numpy as np
import shutil
from orcanet.logging import SummaryLogger, merge_arrays, BatchLogger, gen_line_str
from orcanet.core import Organizer
class T... | 11,911 | 36.815873 | 133 | py |
OrcaNet | OrcaNet-master/orcanet/tests/test_core.py | import tempfile
from unittest import TestCase
from unittest.mock import MagicMock, patch
import os
import shutil
import tensorflow as tf
from tensorflow.keras.models import Model
import tensorflow.keras.layers as layers
from orcanet.core import Organizer, Configuration, _extract_filepaths
class TestOrganizer(tf.test... | 13,945 | 33.952381 | 112 | py |
OrcaNet | OrcaNet-master/orcanet/tests/test_nn_utilities.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import os
import shutil
import h5py
from unittest import TestCase
from tensorflow.keras.models import Model
import tensorflow.keras.layers as layers
from orcanet.core import Organizer
from orcanet.utilities.nn_utilities import load_zero_center_data
cl... | 5,791 | 33.47619 | 85 | py |
OrcaNet | OrcaNet-master/orcanet/tests/test_in_out.py | from unittest import TestCase
from unittest.mock import MagicMock
import os
import h5py
import numpy as np
import shutil
from pathlib import Path
from tensorflow.keras.models import Model
import tensorflow.keras.layers as layers
from orcanet.core import Configuration
from orcanet.in_out import IOHandler, split_name_of... | 23,989 | 32.044077 | 118 | py |
OrcaNet | OrcaNet-master/orcanet/tests/test_model_builder.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
from unittest import TestCase
from unittest.mock import MagicMock, patch
import tensorflow.keras as ks
import tensorflow.keras.layers as layers
from orcanet.core import Organizer
from orcanet.model_builder import ModelBuilder
from orcanet_contrib.custom_objects ... | 4,117 | 34.5 | 91 | py |
OrcaNet | OrcaNet-master/orcanet/tests/test_backend.py | import tempfile
from unittest import TestCase
from unittest.mock import MagicMock
import os
import warnings
import h5py
import numpy as np
import tensorflow as tf
import tensorflow.keras as ks
import tensorflow.keras.layers as layers
from orcanet.core import Organizer
from orcanet.backend import train_model, validate_... | 9,186 | 28.445513 | 105 | py |
OrcaNet | OrcaNet-master/orcanet/tests/test_layer_blocks.py | from unittest import TestCase
import tensorflow as tf
import orcanet.builder_util.layer_blocks as layer_blocks
class TestInceptionBlockV2(TestCase):
def setUp(self):
self.inp_2d = tf.keras.layers.Input(shape=(10, 10, 1))
self.inp_3d = tf.keras.layers.Input(shape=(10, 10, 10, 1))
def test_sha... | 6,942 | 30.559091 | 94 | py |
OrcaNet | OrcaNet-master/orcanet/tests/test_builders.py | import tensorflow as tf
from tensorflow.keras.models import Model
import tensorflow.keras.layers as layers
from orcanet.builder_util.builders import BlockBuilder
class TestSequentialBuilder(tf.test.TestCase):
def test_input_names_and_shapes_full_model(self):
defaults = {"type": "conv_block", "conv_dim":... | 4,727 | 35.9375 | 111 | py |
OrcaNet | OrcaNet-master/orcanet/tests/test_model_setup.py | # removed, as testing whether changing dropout works requires
# get layer output, which doesn't work properly in tf 2.1
import unittest
import numpy as np
import tensorflow.keras as ks
import tensorflow.keras.layers as layers
from orcanet.model_builder import _change_dropout_rate
@unittest.skip("skipped, as testing... | 3,731 | 43.428571 | 81 | py |
OrcaNet | OrcaNet-master/orcanet/tests/test_h5_generator.py | import tempfile
from unittest import TestCase
from unittest.mock import MagicMock
import os
import numpy as np
from orcanet.core import Organizer
from orcanet.h5_generator import get_h5_generator
from orcanet.tests.test_backend import save_dummy_h5py, assert_dict_arrays_equal, assert_equal_struc_array
class TestBatc... | 6,914 | 34.64433 | 106 | py |
OrcaNet | OrcaNet-master/orcanet/tests/test_losses.py | import numpy as np
import tensorflow as tf
import orcanet.lib.losses as on_losses
class TestLklNormal(tf.test.TestCase):
def setUp(self):
self.loss_func = on_losses.lkl_normal
self.y_pred = tf.constant([
[[0], [1]],
[[1], [1]],
[[2], [2]],
], dtype="floa... | 1,576 | 29.326923 | 79 | py |
OrcaNet | OrcaNet-master/orcanet/lib/label_modifiers.py | import warnings
import numpy as np
import orcanet.misc as misc
# for loading via toml
lmods, register = misc.get_register()
class ColumnLabels:
"""
Label of each model output is column with the same name in the h5 file.
This is the default label modifier.
Example
-------
Model has output "en... | 9,650 | 30.642623 | 162 | py |
OrcaNet | OrcaNet-master/examples/full_example/full_example.py | import numpy as np
import h5py
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.models import Model
import os
from orcanet.core import Organizer
def make_dummy_data(path):
"""
Save a train and a val h5 file with random numbers as samples,
and the sum as labels.
"""
def get_... | 1,557 | 21.257143 | 66 | py |
OrcaNet | OrcaNet-master/orcanet_contrib/custom_objects.py | import tensorflow.keras.backend as K
import tensorflow as tf
import math
def get_custom_objects():
"""
Functions that returns a dict with all relevant loss functions in this file.
"""
custom_objects = {
'loss_direction': loss_direction,
'loss_uncertainty_mse': loss_uncertainty_mse,
... | 11,457 | 43.239382 | 153 | py |
OrcaNet | OrcaNet-master/orcanet_contrib/parser_orcatrain.py | """
Use orga.train with a parser.
Usage:
parser_orcatrain.py [options] FOLDER LIST CONFIG MODEL
parser_orcatrain.py (-h | --help)
Arguments:
FOLDER Path to the folder where everything gets saved to, e.g. the
summary.txt, the plots, the trained models, etc.
LIST A .toml file which conta... | 4,173 | 34.372881 | 90 | py |
OrcaNet | OrcaNet-master/docs/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 ------------------------------------------------------------... | 6,834 | 28.847162 | 79 | py |
OrcaNet | OrcaNet-master/scraps/conv4d.py | from __future__ import division
import tensorflow as tf
import numpy as np
def conv4d(
input_tensor,
filters,
kernel_size=(3, 3, 3, 3),
strides=(1, 1, 1, 1),
padding='valid',
data_format='channels_first',
dilation_rate=(1, 1, 1, 1),
activation=None,
... | 5,958 | 30.86631 | 76 | py |
OrcaNet | OrcaNet-master/scraps/test_model.py | from tensorflow.keras.models import Model, clone_model
import tensorflow.keras.layers as layers
import numpy as np
from tensorflow.keras import backend as K
def build_double_inp(compile=False):
inp_1 = layers.Input((1,), name="inp_0")
inp_2 = layers.Input((1,), name="inp_1")
x = layers.Concatenate()([inp... | 2,716 | 27.6 | 76 | py |
OrcaNet | OrcaNet-master/scraps/multi_gpu/multi_gpu_mixin_models.py | """
Multi-gpu code for Keras/TF.
From https://github.com/avolkov1/keras_experiments
"""
from keras.legacy import interfaces
from keras import callbacks as cbks
__all__ = ('ModelDataflowMixin',)
class ModelDataflowMixin(object):
@interfaces.legacy_generator_methods_support
def fit_dataflow(self, dflow,
... | 11,545 | 43.751938 | 79 | py |
OrcaNet | OrcaNet-master/scraps/multi_gpu/multi_gpu_patch_tf_backend.py | """
Multi-gpu code for Keras/TF.
From https://github.com/avolkov1/keras_experiments
"""
import sys
import numpy as np
import tensorflow as tf
from keras.backend import tensorflow_backend as tfb
from keras.backend.tensorflow_backend import (get_session, is_sparse)
import atexit
atexit.register(tfb.clear_session)
# F... | 3,824 | 38.43299 | 79 | py |
OrcaNet | OrcaNet-master/scraps/multi_gpu/multi_gpu.py | # -*- coding: utf-8 -*-
"""
Multi-gpu code for Keras/TF.
From https://github.com/avolkov1/keras_experiments
"""
# MODIFIED. Inspiration taken from the ref link below.
# ref: https://raw.githubusercontent.com/kuza55/keras-extras/master/utils/multi_gpu.py @IgnorePep8
# The inspirational one carried license:
# Apache ... | 39,536 | 42.209836 | 134 | py |
OrcaNet | OrcaNet-master/scraps/old_model_scripts/wide_resnet.py | # -*- coding: utf-8 -*-
"""Wide Residual Network models for Keras.
Loosely based on https://github.com/titu1994/Wide-Residual-Networks/blob/master/wide_residual_network.py
# Reference
- [Wide Residual Networks](https://arxiv.org/abs/1605.07146)
"""
from keras.models import Model
from keras.layers import Input, Add, Ac... | 12,278 | 44.64684 | 169 | py |
OrcaNet | OrcaNet-master/scraps/old_model_scripts/short_cnn_models.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Functions for creating VGG-like models (including VGG-LSTM)"""
import keras as ks
from keras.models import Model
from keras.layers import Input, Dense, Dropout, Activation, Flatten, Convolution3D, BatchNormalization, MaxPooling3D,\
Convolution2D... | 35,658 | 44.252538 | 212 | py |
ilm | ilm-master/acl20_repro.py | PREMASKED_DATA = {
'train': {
'sto_mixture': 'https://drive.google.com/open?id=1LxlyPqz3OvAZsYRRC8yRdSoaCKGB0Ucg',
'abs_mixture': 'https://drive.google.com/open?id=1rw45GKP4iRJLzXnRtX-rnk_NeGXOqWkU',
'lyr_mixture': 'https://drive.google.com/open?id=1jGCjboxlFUF0jqvB0_-L0eeylhKWfZJV',
},
'valid': {
... | 6,718 | 54.528926 | 360 | py |
ilm | ilm-master/acl20_repro_eval.py | from acl20_repro import PREMASKED_DATA, PRETRAINED_MODELS, PRETRAINED_MODEL_CONFIG_JSON, PAPER_TASK_TO_INTERNAL
# NOTE: https://chrisdonahue.com/gdrive-wget
_CMD_TEMPL = """
mkdir -p {eval_tmp_dir}/data
mkdir -p {eval_tmp_dir}/models/{model_tag}
# Download pre-masked data
wget -nc --load-cookies /tmp/cookies.txt "htt... | 2,610 | 38.560606 | 397 | py |
ilm | ilm-master/train_ilm.py | from enum import Enum
from collections import defaultdict
import multiprocessing
import os
import pickle
import random
import time
import warnings
import numpy as np
import torch
import torch.nn.functional as F
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler, TensorDataset)
from tqdm import ... | 26,721 | 35.159675 | 168 | py |
ilm | ilm-master/ilm/infer.py | import copy
import torch
import torch.nn.functional as F
def sample_from_logits(
logits,
temp=1.,
topk=None,
nucleus=1.):
if temp == 0:
return torch.argmax(logits, dim=-1).unsqueeze(-1)
elif temp != 1:
logits /= temp
probs = F.softmax(logits, dim=-1)
if topk is not None:
top... | 3,227 | 26.355932 | 110 | py |
An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs | An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs-master/Setups/Attacks.py | """
This Script allows to run attacks to nets trained on either ImageNet or Cifar10 with or without the
adversary defence by MadryLab.
"""
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
sys.path.append("./")
import tensorflow as tf
import torchvision.models as models_ch
import torch as ch
import numpy... | 22,096 | 50.269142 | 150 | py |
An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs | An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs-master/Setups/Data_and_Model/setup_mnist.py | ## setup_mnist.py -- mnist data and model loading code
##
## Copyright (C) IBM Corp, 2017-2018
## Copyright (C) 2016, Nicholas Carlini <nicholas@carlini.com>.
##
## This program is licenced under the BSD 2-Clause licence,
## contained in the LICENCE file in this directory.
import tensorflow as tf
import numpy as np
im... | 3,939 | 36.52381 | 123 | py |
An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs | An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs-master/Setups/Data_and_Model/setup_cifar.py | ## setup_cifar.py -- cifar data and model loading code
##
## Copyright (C) IBM Corp, 2017-2018
## Copyright (C) 2016, Nicholas Carlini <nicholas@carlini.com>.
##
## This program is licenced under the BSD 2-Clause licence,
## contained in the LICENCE file in this directory.
import tensorflow as tf
import numpy as np
i... | 4,260 | 30.330882 | 118 | py |
An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs | An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs-master/Setups/Data_and_Model/wrapper_model_loss_f.py | """
This scripts allows to wrap the different instances of models and loss functions
for the different attacks
"""
import numpy as np
import torch as ch
# Patch for single output
def patch_single_output(x, single_output):
if single_output:
return x,0
return x
#Models
class Model_Class_combi():
d... | 8,836 | 34.207171 | 99 | py |
An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs | An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs-master/Setups/Data_and_Model/save_MNIST_CIFAR_data.py | ## train_models.py -- train the neural network models for attacking
##
## Copyright (C) IBM Corp, 2017-2018
## Copyright (C) 2016, Nicholas Carlini <nicholas@carlini.com>.
##
## This program is licenced under the BSD 2-Clause licence,
## contained in the LICENCE file in this directory.
import numpy as np
from keras.m... | 1,343 | 31.780488 | 107 | py |
An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs | An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs-master/Attack_Code/Combinatorial/tools/imagenet_labels.py | """This script is borrowed from https://github.com/labsix/limited-blackbox-attacks"""
_lut = [
'tench, Tinca tinca',
'goldfish, Carassius auratus',
'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
'tiger shark, Galeocerdo cuvieri',
'hammerhead, hammerhead shark... | 28,838 | 27.581764 | 128 | py |
An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs | An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs-master/Attack_Code/Square_Attack/data.py | import torch
import numpy as np
from torchvision import transforms
from torchvision.datasets import ImageFolder
from torch.utils.data import DataLoader, Dataset
def load_mnist(n_ex):
from tensorflow.keras.datasets import mnist as mnist_keras
x_test, y_test = mnist_keras.load_data()[1]
x_test = x_test.ast... | 1,623 | 30.843137 | 88 | py |
An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs | An-Empirical-Study-of-DFO-Algorithms-for-Targeted-Black-Box-Attacks-in-DNNs-master/Attack_Code/Square_Attack/models.py | import torch
import tensorflow as tf
import numpy as np
import math
from torchvision import models as torch_models
from torch.nn import DataParallel
from madry_mnist.model import Model as madry_model_mnist
from madry_cifar10.model import Model as madry_model_cifar10
from logit_pairing.models import LeNet as lp_model_mn... | 6,688 | 44.195946 | 116 | py |
MaxStyle | MaxStyle-main/src/test_basic_segmentation_solver.py | # Created by cc215 at 02/05/19
# Modified by cc215 at 11/12/19
# This code is for testing basic segmentation networks
# Steps:
# 1. get the segmentation network and the path of checkpoint
# 2. fetch images tuples from the disk to test the segmentation
# 3. get the prediction result
# 4. update the metric
# 5. sav... | 14,577 | 47.431894 | 144 | py |
MaxStyle | MaxStyle-main/src/train_adv_supervised_segmentation_triplet.py |
from __future__ import print_function
import sys
import os
import argparse
import shutil
from os.path import join, exists
import gc
import random
from matplotlib import style
import numpy as np
import torch
import torchio as tio
import pandas as pd
from torch.utils.tensorboard import SummaryWriter
from torch.utils.dat... | 60,272 | 61.719043 | 246 | py |
MaxStyle | MaxStyle-main/src/advanced/rand_conv_aug.py | '''
Robust and Generalizable Visual Representation
Learning via Random Convolutions
https://arxiv.org/pdf/2007.13003.pdf
'''
import random
import torch
import numpy as np
class RandConvAug():
def __init__(self, kernel_size_candidates=[1, 3, 5, 7], prob=0.5, mix=True):
self.kernel_size_candidates = kerne... | 1,732 | 34.367347 | 96 | py |
MaxStyle | MaxStyle-main/src/advanced/mixup.py | # Created by cc215 at 13/12/19
import numpy as np
import torch
import sys
sys.path.append('../')
from src.models.custom_loss import One_Hot, cross_entropy_2D
class MixUP():
def __init__(self, alpha=0.4, preserve_order=False, use_gpu=False, opt=None):
'''
MIXUP training
reference: https://g... | 5,053 | 38.484375 | 119 | py |
MaxStyle | MaxStyle-main/src/advanced/mixstyle.py | import torch
import torch.nn as nn
import random
class MixStyle(nn.Module):
"""MixStyle.
code is adapted from the orginal mixstyle paper.
Reference:
Zhou et al. Domain Generalization with MixStyle. ICLR 2021.
"""
def __init__(self, p=0.5, alpha=0.1, eps=1e-8, mix='random', lmda=None, zero_i... | 4,147 | 35.385965 | 153 | py |
MaxStyle | MaxStyle-main/src/advanced/random_window_masking.py | import random
import torch
def random_inpainting(image, cnt=5):
"""[summary]
masking images with random window blocks, where values are randomly draw.
code is adapted from Model Genesis: https://github.com/MrGiovanni/ModelsGenesis/blob/master/pytorch/utils.py
Args:
image ([ torch tensor]): a b... | 3,142 | 38.78481 | 137 | py |
MaxStyle | MaxStyle-main/src/advanced/maxstyle.py | import torch
import torch.nn as nn
import random
class MaxStyle(nn.Module):
"""MaxStyle
Official implementation of MaxStyle: [MICCAI 2022] MaxStyle: Adversarial Style Composition for Robust Medical Image Segmentation
code is adapted based on the orginal mixstyle implementation: https://github.com/KaiyangZ... | 11,614 | 46.995868 | 237 | py |
MaxStyle | MaxStyle-main/src/models/custom_loss.py | import torchvision
import math
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import sys
sys.path.append('../')
from src.common_utils.morphology import Dilation2d
def basic_loss_fn(pred, target, loss_type='cross_entropy', class_weights=None, u... | 52,752 | 37.171491 | 139 | py |
MaxStyle | MaxStyle-main/src/models/init_weight.py | from torch.nn import init
import torch
import torch.nn as nn
import torch.nn as nn
def reset_bn(model):
# reset to reestimate bn normalization stats
for n, m in model.named_modules():
if n.find('BatchNorm') != -1:
m.reset_running_stats()
def init_bn(model):
# reset to reestimate bn n... | 2,877 | 31.704545 | 94 | py |
MaxStyle | MaxStyle-main/src/models/advanced_triplet_recon_segmentation_model.py | # this segmentation model is composed of 2 subnetworks at least, an encoder and an decoder
import itertools
import random
import os
from os.path import join
from tkinter import E
from numpy import True_
from numpy.core.fromnumeric import shape
import torch.nn as nn
import torch
import torch.optim as optim
import gc
i... | 57,362 | 50.585432 | 244 | py |
MaxStyle | MaxStyle-main/src/models/model_util.py | import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.optim import lr_scheduler
import numpy as np
import contextlib
import math
import sys
sys.path.append('../')
# from ... | 29,948 | 37.445443 | 177 | py |
MaxStyle | MaxStyle-main/src/models/custom_layers.py | import math
import torch.nn as nn
from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn.parameter import Parameter
from torch.nn import functional as F
import torch
from torch.nn.utils import spectral_norm
class Fixable2DDropout(nn.Module):
"""
_summary_method = torch.nn.Dropout2d.__init__
based... | 16,379 | 36.568807 | 163 | py |
MaxStyle | MaxStyle-main/src/models/base_segmentation_model.py | # Created by cc215 at 02/05/19
# segmentation model definition goes here
import os
from os.path import join
import torch.nn as nn
import torch
import torch.optim as optim
import gc
import traceback
import sys
sys.path.append('../')
from src.models.init_weight import init_weights
from src.models.segmentation_models.fc... | 14,320 | 42.135542 | 140 | py |
MaxStyle | MaxStyle-main/src/models/segmentation_models/fcn.py | import torch.nn as nn
import torch.nn.functional as F
import torch
import sys
sys.path.append('../../')
from src.models.init_weight import init_weights
from src.models.segmentation_models.unet_parts import conv2DBatchNormRelu
class FCN(nn.Module):
# Wenjia Bai's FCN pytorch Implementation, for more details, pleas... | 8,305 | 37.813084 | 118 | py |
MaxStyle | MaxStyle-main/src/models/segmentation_models/resconvunet.py | # Created by cc215 at 07/06/19
# same unet but use convolutional kernel to do downsampling and upsampling; plus residual connection after downsampling and upsampling
# Enter scenario name here
# Enter steps here
import math
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import sys
sys.pa... | 8,312 | 38.966346 | 168 | py |
MaxStyle | MaxStyle-main/src/models/segmentation_models/unetr.py |
## the code is adapted from MONAI: https://github.com/Project-MONAI/MONAI/blob/dev/monai/networks/nets/unetr.py
# Copyright (c) MONAI Consortium
# 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... | 19,323 | 39.174636 | 156 | py |
MaxStyle | MaxStyle-main/src/models/segmentation_models/unet_parts.py | #!/usr/bin/python
# sub-parts of the U-Net models
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.utils import spectral_norm
import sys
sys.path.append('../../')
from src.models.init_weight import init_weights
from src.models.custom_layers import Fixable2DDropout
class CodeFilter(nn.M... | 26,161 | 35.539106 | 150 | py |
MaxStyle | MaxStyle-main/src/models/segmentation_models/unet.py | import math
import sys
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
import sys
sys.path.append('../../')
from src.models.model_util import _disable_tracking_bn_stats
from src.models.segmentation_models.unet_parts import *
class UnetEncoder(nn.Module):
def __init__(self, input_ch... | 22,575 | 40.047273 | 167 | py |
MaxStyle | MaxStyle-main/src/models/ebm/encoder_decoder.py |
import math
import torch
import torch.nn as nn
from torch.autograd import Variable
import numpy as np
from torch.nn.utils import spectral_norm
import torch.nn.functional as F
import sys
sys.path.append('../../')
from src.models.custom_layers import DomainSpecificBatchNorm2d
from src.models.custom_layers import Fixable... | 26,403 | 37.211288 | 178 | py |
MaxStyle | MaxStyle-main/src/common_utils/load_model.py | import os
from os.path import join
import torch
def resume_model_from_file(file_path):
start_epoch = 1
optimizer_state = None
state_dict = None
checkpoint = None
assert os.path.isfile(file_path)
if '.pkl' in file_path:
print("Loading models and optimizer from checkpoint '{}'".format(f... | 1,776 | 33.843137 | 96 | py |
MaxStyle | MaxStyle-main/src/common_utils/basic_operations.py | # Created by cc215 at 27/12/19
# Enter feature description here
# Enter scenario name here
# Enter steps here
from numpy.lib.function_base import copy
import os
import shutil
import random
import os
import torch
from torch._C import device
import torch
import random
import numpy as np
import SimpleITK as sitk
import ... | 11,521 | 32.300578 | 167 | py |
MaxStyle | MaxStyle-main/src/common_utils/vis.py | import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable
palette = [128, 64, 128, 244, 35, 232, 70, 70, 70, 102, 102, 156, 190, 153, 153, 153, 153, 153, 250, 170, 30,
220, 220, 0, 107, 142, 35, 152, 251, 152, 70, 130, 180, ... | 5,867 | 30.891304 | 147 | py |
MaxStyle | MaxStyle-main/src/common_utils/save.py | import os
import SimpleITK as sitk
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
from skimage.transform import resize
import seaborn as sns
from os.path import join
import time
import pickle
import logging
import torch
import scipy.misc
from src.common_utils.basic_operations import check_di... | 13,492 | 34.885638 | 181 | py |
MaxStyle | MaxStyle-main/src/common_utils/metrics.py | # Adapted from score written by wkentaro
# https://github.com/wkentaro/pytorch-fcn/blob/master/torchfcn/utils.py
import numpy as np
from medpy.metric.binary import dc
import pandas as pd
from IPython.display import display, HTML
from src.common_utils.measure import hd, hd_2D_stack, asd, volumesimilarity, VolumeSimInd... | 15,120 | 36.8025 | 152 | py |
MaxStyle | MaxStyle-main/src/common_utils/morphology.py | import math
import pdb
import torch
import torch.nn as nn
import torch.nn.functional as F
class Morphology(nn.Module):
'''
code is adapted from https://github.com/lc82111/pytorch_morphological_dilation2d_erosion2d/blob/master/morphology.py
Base class for morpholigical operators
For now, only supports... | 4,149 | 37.425926 | 160 | py |
MaxStyle | MaxStyle-main/src/dataset_loader/cardiac_general_dataset.py | # Created by cc215 at 27/1/20
# this dataset is available at '/vol/medic01/users/cc215/data/MedicalDecathlon/Task05_Prostate/preprocessed'
# from Medical Decathlon challenge dataset, we use T2 as input for the task.
# Note: All images have been preprocessed (resampled to the 0.625 x 0.625 x 3.6 mm, the median value of ... | 12,091 | 45.152672 | 195 | py |
MaxStyle | MaxStyle-main/src/dataset_loader/base_segmentation_dataset.py | # Created by cc215 at 11/12/19
# Enter feature description here
# Enter scenario name here
# Enter steps here
import os
import sys
import torch.utils.data as data
import torch
from torch.utils.data import Dataset
import random
import numpy as np
sys.path.append('../')
from src.common_utils.basic_operations import swi... | 19,478 | 37.802789 | 172 | py |
MaxStyle | MaxStyle-main/src/dataset_loader/generate_artefacted_data.py | import numpy as np
import os
from os.path import join
import glob
import matplotlib
import matplotlib.pyplot as plt
import torch
import pandas as pd
from random import randint
import SimpleITK as sitk
from torchio.transforms import RandomMotion, RandomSpike, RandomGhosting, RandomBiasField
sys.path.append('../')
from ... | 4,393 | 38.232143 | 128 | py |
MaxStyle | MaxStyle-main/src/dataset_loader/prostate_Decathlon_dataset.py | # Created by cc215 at 27/1/20
# this dataset is available at '/vol/medic01/users/cc215/data/MedicalDecathlon/Task05_Prostate/preprocessed'
# from Medical Decathlon challenge dataset, we use T2 as input for the task.
# Note: All images have been preprocessed (resampled to the 0.625 x 0.625 x 3.6 mm, the median value of ... | 12,452 | 46.530534 | 258 | py |
MaxStyle | MaxStyle-main/src/dataset_loader/transform.py | import torchsample.transforms as ts
from src.dataset_loader._utils.affine_transform import MyRandomFlip, MySpecialCrop, MyPad, MyRandomChoiceRotate,MyRandomAffine
from src.dataset_loader._utils.intensity_transform import RandomGamma, MyNormalizeMedicPercentile, MyRandomPurtarbation, MyRandomPurtarbationV2, RandomBright... | 13,679 | 40.96319 | 172 | py |
MaxStyle | MaxStyle-main/src/dataset_loader/cardiac_ACDC_dataset.py | # Created by cc215 at 27/1/20
# this dataset is available at '/vol/medic01/users/cc215/Dropbox/projects/DeformADA/Data/ACDC'
# from ACDC challenge dataset, containing ED/ES whole stack (3D).
# Note: All images have been preprocessed and cropped to 224 by 224, following the preprocessing steps in
# "Semi-Supervised and ... | 11,085 | 46.174468 | 211 | py |
MaxStyle | MaxStyle-main/src/dataset_loader/_utils/elastic_transform.py | # Created by cc215 at 27/05/19
# perform elastic transform on 2D image data
# for data augmentation
# Enter steps here
# Function to distort image
import SimpleITK as sitk
import numpy as np
from scipy.ndimage.interpolation import map_coordinates
from scipy.ndimage.filters import gaussian_filter
from skimage import t... | 7,216 | 40.716763 | 123 | py |
MaxStyle | MaxStyle-main/src/dataset_loader/_utils/affine_transform.py | import SimpleITK as sitk
import torch
from scipy import ndimage
import math
import cv2
import numpy as np
from skimage import transform as sktform
import torch as th
from torch.autograd import Variable
from skimage.exposure import equalize_adapthist
from skimage.filters import gaussian
import random
from torchsample.ut... | 30,417 | 36.004866 | 174 | py |
MaxStyle | MaxStyle-main/src/dataset_loader/_utils/intensity_transform.py | import numpy as np
from skimage.exposure import equalize_adapthist
import torch
from scipy.ndimage import gaussian_filter
import scipy
import random
import torch as th
from PIL import Image
from scipy.interpolate import RectBivariateSpline
class MyRandomImageContrastTransform(object):
def __init__(self, random_s... | 23,854 | 42.531022 | 191 | py |
explainable-iqa | explainable-iqa-master/custom_gradcam.py | """
Created on Thu Oct 26 11:06:51 2017
@author: Utku Ozbulak - github.com/utkuozbulak
@editor: Caner Ozer - github.com/canerozer
"""
import argparse
import yaml
import os
import tqdm
import numpy as np
import pandas as pd
from PIL import Image
import torch
import torch.nn as nn
from torch.nn import Sequential
from t... | 16,845 | 38.731132 | 109 | py |
explainable-iqa | explainable-iqa-master/custom_normgrad.py | """
Created on Tue Sep 8 11:06:51 2020
@author: Sylvestre Rebuffi - github.com/srebuffi
@editor: Caner Ozer - github.com/canerozer
"""
import argparse
import yaml
import os
import tqdm
import numpy as np
import pandas as pd
from PIL import Image
from copy import deepcopy
import torch
import torch.nn as nn
from torch.... | 14,594 | 39.654596 | 109 | py |
explainable-iqa | explainable-iqa-master/custom_guided_gradcam.py | """
Created on Thu Oct 23 11:27:15 2017
@author: Utku Ozbulak - github.com/utkuozbulak
@editor: Caner Ozer - github.com/canerozer
"""
import argparse
import yaml
import os
import tqdm
import numpy as np
import pandas as pd
import torch
from src.misc_functions import (get_example_params, convert_to_grayscale,
... | 7,505 | 38.505263 | 109 | py |
explainable-iqa | explainable-iqa-master/custom_guided_backprop.py | """
Created on Thu Oct 26 11:23:47 2017
@author: Utku Ozbulak - github.com/utkuozbulak
@editor: Caner Ozer - github.com/canerozer
"""
import argparse
import yaml
import os
import tqdm
import numpy as np
import pandas as pd
from PIL import Image
import torch
from torch.nn import ReLU, SiLU, Sequential
from torchvision... | 14,452 | 38.925414 | 109 | py |
explainable-iqa | explainable-iqa-master/custom_grad_times_image.py | """
Created on Wed Jun 19 17:12:04 2019
@author: Utku Ozbulak - github.com/utkuozbulak
"""
import argparse
import yaml
import os
import tqdm
import pandas as pd
import torch
from src.misc_functions import (get_example_params, convert_to_grayscale,
preprocess_image, DictAsMember,
... | 6,763 | 37.873563 | 109 | py |
explainable-iqa | explainable-iqa-master/src/guided_backprop.py | """
Created on Thu Oct 26 11:19:58 2017
@author: Utku Ozbulak - github.com/utkuozbulak
@editor: Caner Ozer - github.com/canerozer
"""
import argparse
import yaml
import os
import numpy as np
from PIL import Image
import torch
from torch.nn import ReLU, Sequential
from torchvision.models.resnet import BasicBlock, Bott... | 4,286 | 34.139344 | 91 | py |
explainable-iqa | explainable-iqa-master/src/misc_functions.py | """
Created on Thu Oct 21 11:09:09 2017
@author: Utku Ozbulak - github.com/utkuozbulak
@editor: Caner Ozer - github.com/canerozer
"""
import os
import copy
import math
from functools import reduce
import numpy as np
import pandas as pd
from PIL import Image, ImageFilter, UnidentifiedImageError, ImageDraw
import matpl... | 18,743 | 31.485269 | 91 | py |
explainable-iqa | explainable-iqa-master/src/normgrad.py | """
Created on Thu Oct 26 11:19:58 2017
@author: Utku Ozbulak - github.com/utkuozbulak
@editor: Caner Ozer - github.com/canerozer
"""
import argparse
import yaml
import os
import numpy as np
from PIL import Image
import torch
import torch.nn.functional as F
from torch.nn import Sequential
from torchvision.models.resn... | 5,405 | 38.459854 | 95 | py |
explainable-iqa | explainable-iqa-master/src/gradcam.py | """
Created on Thu Oct 26 11:19:58 2017
@author: Utku Ozbulak - github.com/utkuozbulak
@editor: Caner Ozer - github.com/canerozer
"""
import argparse
import yaml
import os
import numpy as np
from PIL import Image
import torch
import torch.nn.functional as F
from torch.nn import Sequential
from torchvision.models.resn... | 8,221 | 38.152381 | 98 | py |
explainable-iqa | explainable-iqa-master/src/backprop.py | """
Created on Thu Oct 26 11:19:58 2017
@author: Utku Ozbulak - github.com/utkuozbulak
@editor: Caner Ozer - github.com/canerozer
"""
import argparse
import yaml
import os
import numpy as np
from PIL import Image
import torch
class VanillaBackprop():
"""
Produces gradients generated with vanilla back pr... | 1,757 | 28.3 | 81 | py |
explainable-iqa | explainable-iqa-master/src/pointing_game.py | r"""
A Basic Implementation of the Pointing Game
Some parts of these implementation were obtained from
https://github.com/facebookresearch/TorchRay/blob/master/torchray/benchmark/pointing_game.py
"""
import random
import numpy as np
import pandas as pd
from skimage.feature import peak_local_max
class PointingGame:... | 5,754 | 32.654971 | 92 | py |
explainable-iqa | explainable-iqa-master/src/models/__init__.py | import torch.nn as nn
from torchvision import models as tvmodels
def _get_classification_model(model_meta):
name = model_meta.name
n_classes = model_meta.n_classes
pretrained = model_meta.pretrained
model = None
if name == "resnet18":
model = tvmodels.resnet18(pretrained=pretrained)
... | 961 | 32.172414 | 57 | py |
robbing_the_fed | robbing_the_fed-main/attacks/base_attack.py | """Implementation for base attacker class.
Inherit from this class for a consistent interface with attack cases."""
import torch
from collections import defaultdict
import copy
from .common import optimizer_lookup
import logging
log = logging.getLogger(__name__)
class _BaseAttacker:
"""This is a template cl... | 16,305 | 48.865443 | 123 | py |
robbing_the_fed | robbing_the_fed-main/attacks/common.py | """Common subfunctions to multiple modules."""
import torch
def optimizer_lookup(params, optim_name, step_size, scheduler=None, warmup=0, max_iterations=10_000):
if optim_name.lower() == "adam":
optimizer = torch.optim.Adam(params, lr=step_size)
elif optim_name.lower() == "momgd":
optimizer ... | 6,682 | 42.116129 | 152 | py |
robbing_the_fed | robbing_the_fed-main/attacks/analytic_attack.py | """Simple analytic attack that works for (dumb) fully connected models."""
import torch
from .base_attack import _BaseAttacker
class AnalyticAttacker(_BaseAttacker):
"""Implements a sanity-check analytic inversion
Only works for a torch.nn.Sequential model with input-sized FC layers."""
def __init__(s... | 5,346 | 47.609091 | 111 | py |
robbing_the_fed | robbing_the_fed-main/modifications/imprint.py | """Implements a malicious block that can be inserted at the front on normal models to break them."""
from statistics import NormalDist
import torch
import math
from scipy.stats import laplace
class ImprintBlock(torch.nn.Module):
structure = "cumulative"
def __init__(self, data_size, num_bins, connection="lin... | 7,090 | 39.988439 | 122 | py |
robbing_the_fed | robbing_the_fed-main/utils/breaching_utils.py | from collections import namedtuple
import torch
def plot_data(cfg, user_data, setup, scale=False, print_labels=False):
"""Plot user data to output. Probably best called from a jupyter notebook."""
import matplotlib.pyplot as plt # lazily import this here
dm = torch.as_tensor(cfg.mean, **setup)[None, :, N... | 2,290 | 35.951613 | 99 | py |
robbing_the_fed | robbing_the_fed-main/utils/analysis.py | """Simple report function based on PSNR and maybe SSIM and maybe better ideas..."""
import torch
from .metrics import psnr_compute, registered_psnr_compute, image_identifiability_precision
#cw_ssim - can uncomment if you want ...
import logging
log = logging.getLogger(__name__)
def report(
reconstructed_user... | 7,822 | 39.117949 | 119 | py |
robbing_the_fed | robbing_the_fed-main/utils/metrics.py | """Various metrics."""
import torch
from functools import partial
def cw_ssim(img_batch, ref_batch, scales=5, skip_scales=None, K=1e-6):
"""Batched complex wavelet structural similarity.
As in Zhou Wang and Eero P. Simoncelli, "TRANSLATION INSENSITIVE IMAGE SIMILARITY IN COMPLEX WAVELET DOMAIN"
Ok, not q... | 14,082 | 45.022876 | 122 | py |
BOCF | BOCF-master/doc/source/conf.py | # -*- coding: utf-8 -*-
#
# GPy documentation build configuration file, created by
# sphinx-quickstart on Fri Sep 18 18:16:28 2015.
#
# 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
# autogenerated file.
#
# All c... | 12,260 | 30.198473 | 113 | py |
numpy | numpy-main/numpy/core/_add_newdocs.py | """
This is only meant to add docs to objects defined in C-extension modules.
The purpose is to allow easier editing of the docstrings without
requiring a re-compile.
NOTE: Many of the methods of ndarray have corresponding functions.
If you update these docstrings, please keep also the ones in
core/fromnum... | 204,444 | 28.42925 | 128 | py |
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