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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Alpha-IoU | Alpha-IoU-main/utils/metrics.py | # Model validation metrics
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import torch
from . import general
def fitness(x):
# Model fitness as a weighted combination of metrics
w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
return (x[:, :4] * w).sum(... | 8,969 | 39.044643 | 120 | py |
Alpha-IoU | Alpha-IoU-main/utils/activations.py | # Activation functions
import torch
import torch.nn as nn
import torch.nn.functional as F
# SiLU https://arxiv.org/pdf/1606.08415.pdf ----------------------------------------------------------------------------
class SiLU(nn.Module): # export-friendly version of nn.SiLU()
@staticmethod
def forward(x):
... | 2,248 | 29.808219 | 120 | py |
Alpha-IoU | Alpha-IoU-main/utils/general.py | # General utils
import glob
import logging
import math
import os
import platform
import random
import re
import subprocess
import time
from pathlib import Path
import cv2
import numpy as np
import torch
import torchvision
import yaml
from utils.google_utils import gsutil_getsize
from utils.metrics import fitness
fro... | 27,286 | 41.569423 | 120 | py |
Alpha-IoU | Alpha-IoU-main/utils/google_utils.py | # Google utils: https://cloud.google.com/storage/docs/reference/libraries
import os
import platform
import subprocess
import time
from pathlib import Path
import requests
import torch
def gsutil_getsize(url=''):
# gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
s = subprocess.... | 4,870 | 38.601626 | 118 | py |
Alpha-IoU | Alpha-IoU-main/utils/wandb_logging/wandb_utils.py | import json
import shutil
import sys
from datetime import datetime
from pathlib import Path
import torch
sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path
from utils.general import colorstr, xywh2xyxy
try:
import wandb
except ImportError:
wandb = None
print(f"{colorstr('wand... | 6,892 | 46.212329 | 117 | py |
Alpha-IoU | Alpha-IoU-main/utils/aws/resume.py | # Resume all interrupted trainings in yolov5/ dir including DPP trainings
# Usage: $ python utils/aws/resume.py
import os
import sys
from pathlib import Path
import torch
import yaml
sys.path.append('./') # to run '$ python *.py' files in subdirectories
port = 0 # --master_port
path = Path('').resolve()
for last ... | 1,114 | 28.342105 | 119 | py |
CorrI2P | CorrI2P-main/pointnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def square_distance(src, dst):
"""
Calculate Euclid distance between each two points.
src^T * dst = xn * xm + yn * ym + zn * zm；
sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn;
sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm;
dist = (x... | 10,653 | 35.865052 | 126 | py |
CorrI2P | CorrI2P-main/train_nuscenes.py | import os
os.environ["CUDA_VISIBLE_DEVICES"]="3"
import torch
import torch.nn as nn
import argparse
from network3 import DenseI2P
from nuscenes_pc_img_dataloader import nuScenesLoader
import loss2
import numpy as np
import logging
import math
import nuScenes.options as options
import cv2
from scipy.spatial.transform im... | 16,760 | 50.41411 | 222 | py |
CorrI2P | CorrI2P-main/nuscenes_pc_img_dataloader.py | import open3d
import torch.utils.data as data
import random
import numbers
import os
import os.path
import numpy as np
import struct
import math
import torch
import torchvision
import cv2
from PIL import Image
from torchvision import transforms
import pickle
from pyquaternion import Quaternion
from nuScenes import opt... | 14,620 | 35.921717 | 112 | py |
CorrI2P | CorrI2P-main/network.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import layers_pc
import imagenet
from imagenet import ResidualConv,ImageUpSample
from pointnet import FPS
import pointnet2
from options import Options
class CorrI2P(nn.Module):
def __init__(self,opt:Options):
super(CorrI2P, self).__init__()... | 14,508 | 57.504032 | 206 | py |
CorrI2P | CorrI2P-main/eval_all.py | import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import torch
import argparse
from network import DenseI2P
from kitti_pc_img_dataloader import kitti_pc_img_dataset
#from loss2 import kpt_loss, kpt_loss2, eval_recall
import datetime
import logging
import math
import numpy as np
import options
if __name__=='__main__':
... | 5,958 | 53.172727 | 173 | py |
CorrI2P | CorrI2P-main/loss.py | from numpy import positive
import torch
import torch.nn.functional as F
import numpy as np
def desc_loss(img_features,pc_features,mask,pos_margin=0.1,neg_margin=1.4,log_scale=10,num_kpt=512):
pos_mask=mask
neg_mask=1-mask
#dists=torch.sqrt(torch.sum((img_features.unsqueeze(-1)-pc_features.unsqueeze(-2))**... | 5,553 | 54.54 | 182 | py |
CorrI2P | CorrI2P-main/layers_pc.py | import torch
import torch.nn as nn
import math
from typing import Tuple, List
import operations
class Swish(nn.Module):
def __init__(self):
"""
Swish activation function
"""
super(Swish, self).__init__()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
... | 38,392 | 41.801561 | 145 | py |
CorrI2P | CorrI2P-main/pointnet2.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math
import time
import operations
from layers_pc import *
from options import Options
import index_max
class PCEncoder(nn.Module):
def __init__(self, opt: Options, Ca: int, Cb: int, Cg: int):
super(PCEncoder, s... | 6,833 | 48.165468 | 132 | py |
CorrI2P | CorrI2P-main/options.py | import numpy as np
import math
import torch
class Options:
def __init__(self):
self.is_debug = False
self.is_fine_resolution = True
self.is_remove_ground = False
self.accumulation_frame_num = 3
self.accumulation_frame_skip = 6
self.delta_ij_max = 40
self.tr... | 1,651 | 26.081967 | 59 | py |
CorrI2P | CorrI2P-main/kitti_pc_img_dataloader.py | import os
import torch
import torch.utils.data as data
from torchvision import transforms
import numpy as np
from PIL import Image
import random
import math
import open3d as o3d
import cv2
import struct
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from scipy.sparse import coo_matrix
class KittiCal... | 16,785 | 37.856481 | 151 | py |
CorrI2P | CorrI2P-main/train.py | import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import torch
import argparse
from network import CorrI2P
from kitti_pc_img_dataloader import kitti_pc_img_dataset
import loss
import numpy as np
import datetime
import logging
import math
import options
import cv2
from scipy.spatial.transform import Rotation
def get_P_... | 15,572 | 52.332192 | 222 | py |
CorrI2P | CorrI2P-main/imagenet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models.utils import load_state_dict_from_url
import numpy as np
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_re... | 18,388 | 39.238512 | 184 | py |
CorrI2P | CorrI2P-main/operations.py | import time
import numpy as np
import math
import torch
# generalized batch size
CUDA_SHARED_MEM_DIM_X = 24
# size of SOM
CUDA_SHARED_MEM_DIM_Y = 512
def knn_gather_wrapper(som_node, som_node_knn_I):
'''
:param som_node: Bx3xN
:param som_node_knn_I: BxNxK
:param som_node_neighbors: Bx3xNxK
:retu... | 1,267 | 20.862069 | 96 | py |
CorrI2P | CorrI2P-main/nuScenes/options.py | import numpy as np
import math
import torch
import random
class Options:
def __init__(self):
self.dataroot = '/extssd/jiaxin/nuscenes'
# self.dataroot = '/data/personal/jiaxin/datasets/kitti'
self.checkpoints_dir = 'checkpoints'
self.version = '3.3'
self.is_debug = False
... | 2,257 | 29.513514 | 68 | py |
CorrI2P | CorrI2P-main/nuScenes_script/make_dataset.py | import open3d
import torch.utils.data as data
import random
import numbers
import os
import os.path
import numpy as np
import struct
import math
import torch
import torchvision
import cv2
from PIL import Image
from torchvision import transforms
import pickle
from pyquaternion import Quaternion
import matplotlib
# matp... | 11,184 | 35.914191 | 126 | py |
CorrI2P | CorrI2P-main/nuScenes_script/nuscenes/prediction/models/backbone.py | # nuScenes dev-kit.
# Code written by Freddy Boulton 2020.
from typing import Tuple
import torch
from torch import nn
from torchvision.models import (mobilenet_v2, resnet18, resnet34, resnet50,
resnet101, resnet152)
def trim_network_at_index(network: nn.Module, index: int = -1) -> nn.... | 3,274 | 34.597826 | 103 | py |
CorrI2P | CorrI2P-main/nuScenes_script/nuscenes/prediction/models/covernet.py | # nuScenes dev-kit.
# Code written by Freddy Boulton, Tung Phan 2020.
from typing import List, Tuple, Callable, Union
import numpy as np
import torch
from torch import nn
from torch.nn import functional as f
from nuscenes.prediction.models.backbone import calculate_backbone_feature_dim
# Number of entries in Agent S... | 4,935 | 39.793388 | 112 | py |
CorrI2P | CorrI2P-main/nuScenes_script/nuscenes/prediction/models/mtp.py | # nuScenes dev-kit.
# Code written by Freddy Boulton, Elena Corina Grigore 2020.
import math
import random
from typing import List, Tuple
import torch
from torch import nn
from torch.nn import functional as f
from nuscenes.prediction.models.backbone import calculate_backbone_feature_dim
# Number of entries in Agent... | 11,960 | 44.135849 | 117 | py |
CorrI2P | CorrI2P-main/nuScenes_script/nuscenes/prediction/tests/test_mtp_loss.py |
import math
import unittest
try:
import torch
except ModuleNotFoundError:
raise unittest.SkipTest('Skipping test as torch was not found!')
from nuscenes.prediction.models import mtp
class TestMTPLoss(unittest.TestCase):
"""
Test each component of MTPLoss as well as the
__call__ method.
"""
... | 7,907 | 41.28877 | 106 | py |
CorrI2P | CorrI2P-main/nuScenes_script/nuscenes/prediction/tests/test_covernet.py | # nuScenes dev-kit.
# Code written by Freddy Boulton, 2020.
import math
import unittest
try:
import torch
from torch.nn.functional import cross_entropy
except ModuleNotFoundError:
raise unittest.SkipTest('Skipping test as torch was not found!')
from nuscenes.prediction.models.backbone import ResNetBackbo... | 3,212 | 36.8 | 105 | py |
CorrI2P | CorrI2P-main/nuScenes_script/nuscenes/prediction/tests/run_covernet.py | # nuScenes dev-kit.
# Code written by Freddy Boulton, 2020.
"""
Regression test to see if CoverNet implementation can overfit on a single example.
"""
import argparse
import math
import numpy as np
import torch
import torch.optim as optim
from torch.utils.data import DataLoader, IterableDataset
from nuscenes.predic... | 2,733 | 28.397849 | 112 | py |
CorrI2P | CorrI2P-main/nuScenes_script/nuscenes/prediction/tests/test_mtp.py | import unittest
try:
import torch
except ModuleNotFoundError:
raise unittest.SkipTest('Skipping test as torch was not found!')
from nuscenes.prediction.models import backbone
from nuscenes.prediction.models import mtp
class TestMTP(unittest.TestCase):
def setUp(self):
self.image = torch.ones((1... | 2,085 | 32.111111 | 92 | py |
CorrI2P | CorrI2P-main/nuScenes_script/nuscenes/prediction/tests/test_backbone.py | import unittest
try:
import torch
from torchvision.models.resnet import BasicBlock, Bottleneck
except ModuleNotFoundError:
raise unittest.SkipTest('Skipping test as torch was not found!')
from nuscenes.prediction.models.backbone import ResNetBackbone, MobileNetBackbone
class TestBackBones(unittest.TestC... | 1,925 | 34.018182 | 82 | py |
CorrI2P | CorrI2P-main/nuScenes_script/nuscenes/prediction/tests/run_image_generation.py | import argparse
from typing import List
import torch
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from nuscenes import NuScenes
from nuscenes.prediction import PredictHelper
from nuscenes.prediction.input_representation.static_layers import StaticLayerRasterizer
from nuscenes.predictio... | 4,227 | 34.830508 | 99 | py |
CorrI2P | CorrI2P-main/nuScenes_script/nuscenes/prediction/tests/run_mtp.py | # nuScenes dev-kit.
# Code written by Freddy Boulton, 2020.
"""
Regression test to see if MTP can overfit on a single example.
"""
import argparse
import numpy as np
import torch
import torch.optim as optim
from torch.utils.data import DataLoader, IterableDataset
from nuscenes.prediction.models.backbone import ResN... | 3,074 | 26.954545 | 107 | py |
be_great | be_great-main/be_great/great_utils.py | import typing as tp
import numpy as np
import pandas as pd
import torch
from transformers import AutoTokenizer
def _array_to_dataframe(
data: tp.Union[pd.DataFrame, np.ndarray], columns=None
) -> pd.DataFrame:
"""Converts a Numpy Array to a Pandas DataFrame
Args:
data: Pandas DataFrame or Numpy... | 5,134 | 28.176136 | 121 | py |
be_great | be_great-main/be_great/great_trainer.py | import random
import numpy as np
import torch
from torch.utils.data import DataLoader
from transformers import Trainer
def _seed_worker(_):
"""
Helper function to set worker seed during Dataloader initialization.
"""
worker_seed = torch.initial_seed() % 2**32
random.seed(worker_seed)
np.rand... | 1,416 | 28.520833 | 111 | py |
be_great | be_great-main/be_great/great.py | import os
import warnings
import json
import typing as tp
import logging
import numpy as np
import pandas as pd
from tqdm import tqdm
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
from be_great.great_dataset import GReaTDataset, GReaTDataCollator
from be_great.great_st... | 22,761 | 42.028355 | 208 | py |
FLAC | FLAC-main/flac.py | import torch
import numpy as np
def pairwise_distances(a, b=None, eps=1e-6):
"""
Calculates the pairwise distances between matrices a and b (or a and a, if b is not set)
:param a:
:param b:
:return:
"""
if b is None:
b = a
aa = torch.sum(a**2, dim=1)
bb = torch.sum(b**2, d... | 1,798 | 27.109375 | 92 | py |
FLAC | FLAC-main/train_imagenet.py | import argparse
import datetime
import logging
import os
import time
from pathlib import Path
import numpy as np
import torch
from torch import nn
from datasets.imagenet import get_imagenet
from models.imagenet_models import resnet18
from utils.logging import set_logging
from utils.utils import AverageMeter, pretty_d... | 8,376 | 29.461818 | 149 | py |
FLAC | FLAC-main/train_biased_mnist.py | import argparse
import datetime
import logging
import os
import time
from pathlib import Path
import numpy as np
import torch
from torch import nn, optim
from flac import flac_loss
from datasets.biased_mnist import get_color_mnist
from models.simple_conv import SimpleConvNet
from utils.logging import set_logging
from... | 7,471 | 30.394958 | 146 | py |
FLAC | FLAC-main/train_celeba.py | import argparse
import datetime
import logging
import os
import time
from pathlib import Path
import numpy as np
import torch
from torch import nn
from flac import flac_loss
from datasets.celeba import get_celeba
from models.resnet import ResNet18
from utils.logging import set_logging
from utils.utils import (
Ave... | 7,494 | 30.893617 | 146 | py |
FLAC | FLAC-main/train_utk_face.py | import argparse
import datetime
import logging
import os
import time
from pathlib import Path
import numpy as np
import torch
from torch import nn
from flac import flac_loss
from datasets.utk_face import get_utk_face
from models.resnet import ResNet18
from utils.logging import set_logging
from utils.utils import (
... | 7,226 | 31.263393 | 146 | py |
FLAC | FLAC-main/get_imagenet_bias_features.py | import argparse
import os
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
from sklearn.metrics.pairwise import cosine_similarity
from datasets.imagenet import get_imagenet
from models.imagenet_models import bagnet18
from utils.utils import AverageMeter, accuracy, set_seed
from... | 3,966 | 30.23622 | 92 | py |
FLAC | FLAC-main/models/imagenet_models.py | """ResNet and BagNet implementations.
original codes
- https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
- https://github.com/wielandbrendel/bag-of-local-features-models/blob/master/bagnets/pytorchnet.py
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from to... | 17,605 | 30.161062 | 159 | py |
FLAC | FLAC-main/models/resnet.py | import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import resnet18
class ResNet18(nn.Module):
def __init__(self, num_classes=2, pretrained=True, model=None):
super().__init__()
if model == None:
model = resnet18(pretrained=pretrained)
modules = l... | 1,043 | 32.677419 | 80 | py |
FLAC | FLAC-main/models/simple_conv.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class SimpleConvNet(nn.Module):
def __init__(self, kernel_size=7, **kwargs):
super(SimpleConvNet, self).__init__()
padding = kernel_size // 2
layers = [
nn.Conv2d(3, 16, kernel_size=kernel_siz... | 1,805 | 31.836364 | 86 | py |
FLAC | FLAC-main/models/bagnets/utils.py | import numpy as np
import matplotlib.pyplot as plt
from skimage import feature, transform
def plot_heatmap(heatmap, original, ax, cmap='RdBu_r',
percentile=99, dilation=0.5, alpha=0.25):
"""
Plots the heatmap on top of the original image
(which is shown by most important edges).
... | 4,133 | 36.926606 | 112 | py |
FLAC | FLAC-main/models/bagnets/pytorchnet.py | import torch.nn as nn
import math
import torch
from collections import OrderedDict
from torch.utils import model_zoo
import torch.nn.functional as F
import os
dir_path = os.path.dirname(os.path.realpath(__file__))
__all__ = ['bagnet9', 'bagnet17', 'bagnet33']
model_urls = {
'bagnet9': 'https://bitbucket... | 6,272 | 37.722222 | 167 | py |
FLAC | FLAC-main/models/bagnets/kerasnet.py | import keras
from keras.models import load_model
__all__ = ['bagnet9', 'bagnet17', 'bagnet33']
model_urls = {
'bagnet9': 'https://bitbucket.org/wielandbrendel/bag-of-feature-pretrained-models/raw/d413271344758455ac086992beb579e256447839/bagnet8.h5',
'bagnet17': 'https://bitbucket.org/wieland... | 1,466 | 37.605263 | 154 | py |
FLAC | FLAC-main/datasets/utk_face.py | import logging
import os
import pickle
from pathlib import Path
import PIL
import numpy as np
import torch
import torch.utils.data
from datasets.utils import TwoCropTransform, get_confusion_matrix
from torch.utils.data.sampler import WeightedRandomSampler
from torchvision import transforms
class UTKFace:
def __i... | 13,188 | 31.168293 | 119 | py |
FLAC | FLAC-main/datasets/utils.py | import torch
class TwoCropTransform:
"""Create two crops of the same image"""
def __init__(self, transform):
self.transform = transform
def __call__(self, x):
return [self.transform(x), self.transform(x)]
def get_confusion_matrix(num_classes, targets, biases):
confusion_matrix_org ... | 1,814 | 38.456522 | 86 | py |
FLAC | FLAC-main/datasets/celeba.py | import logging
import pickle
from pathlib import Path
import numpy as np
import torch
from datasets.utils import TwoCropTransform, get_confusion_matrix
from torch.utils.data import WeightedRandomSampler
from torch.utils.data.dataloader import DataLoader
from torchvision import transforms as T
from torchvision.datasets... | 6,228 | 31.612565 | 112 | py |
FLAC | FLAC-main/datasets/biased_mnist.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
Python implementation of Biased-MNIST.
"""
import logging
import os
import pickle
from pathlib import Path
import numpy as np
import torch
from PIL import Image
from datasets.utils import (
TwoCropTransform,
get_confusion_matrix,
get_unsup_confu... | 14,204 | 32.661137 | 124 | py |
FLAC | FLAC-main/datasets/imagenet.py | """ReBias
Copyright (c) 2020-present NAVER Corp.
MIT license
9-Class ImageNet wrapper. Many codes are borrowed from the official torchvision dataset.
https://github.com/pytorch/vision/blob/master/torchvision/datasets/imagenet.py
The following nine classes are selected to build the subset:
dog, cat, frog, turtle, ... | 9,982 | 28.361765 | 88 | py |
FLAC | FLAC-main/utils/utils.py | from __future__ import print_function
import logging
import math
import os
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import torch.optim as optim
class MultiDimAverageMeter(object):
def __init__(self, dims=(2, 2)):
self.dims = dims
self.cum = torch.zeros(n... | 3,121 | 24.801653 | 88 | py |
fce | fce-main/fuzzy_binning.py | from utils import *
import argparse
import warnings
import pickle
import numpy as np
import os
import torch
import pandas as pd
from tqdm import tqdm
from calibration_utils import *
warnings.filterwarnings("ignore")
# ----------------------------------------------------------------------------------------------------... | 2,962 | 29.546392 | 118 | py |
fce | fce-main/paper_demo/get_predictions.py | from datasets import load_dataset
from pytorch_lightning import Trainer, seed_everything
from utils import *
import argparse
import warnings
import pickle
import os
import torch
from calibration_utils import *
warnings.filterwarnings("ignore")
# ------------------------------------------------------------------------... | 3,417 | 30.072727 | 118 | py |
fce | fce-main/paper_demo/utils.py | import os
import datasets
import evaluate
import numpy as np
import pandas as pd
import torch
from datasets import DatasetDict, Dataset
from pytorch_lightning import LightningDataModule, LightningModule, Trainer, seed_everything
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import (
... | 9,031 | 33.212121 | 120 | py |
fce | fce-main/paper_demo/calibration_metrics.py | import pickle
import numpy as np
import os
import torch
import pandas as pd
import skfuzzy
from tqdm import tqdm
def expected_calibration_error(y_true, y_pred, num_bins):
ece_vals = []
pred_y = np.argmax(y_pred, axis=-1)
correct = (pred_y == y_true).astype(np.float32)
prob_y = np.max(y_pred, axis=-1)
... | 4,061 | 24.074074 | 92 | py |
fce | fce-main/paper_demo/binning.py | from utils import *
import argparse
import warnings
import pickle
import numpy as np
import os
import torch
import pandas as pd
from tqdm import tqdm
from calibration_utils import *
warnings.filterwarnings("ignore")
# ----------------------------------------------------------------------------------------------------... | 3,474 | 33.068627 | 118 | py |
Unsupervised_Anomaly_Detection_Brain_MRI | Unsupervised_Anomaly_Detection_Brain_MRI-master/models/adversarial_autoencoder.py | import numpy as np
import tensorflow as tf
from tensorflow.compat.v1.layers import Dense
from tensorflow.nn import leaky_relu
from tensorflow.python.keras.layers import Flatten, Conv2D, Dropout
from models.customlayers import build_unified_decoder, build_unified_encoder
def adversarial_autoencoder(z, x, dropout_rate... | 2,383 | 31.657534 | 111 | py |
Unsupervised_Anomaly_Detection_Brain_MRI | Unsupervised_Anomaly_Detection_Brain_MRI-master/models/customlayers.py | import math
import tensorflow as tf
from tensorflow.compat.v1.layers import Conv2D, Conv2DTranspose, BatchNormalization
from tensorflow.keras.layers import LeakyReLU, ReLU, LayerNormalization
def sample(dec_dense, decoder, reshape, tensor, zDim):
sampled = tf.random.normal(shape=(tf.shape(tensor)[0], zDim))
... | 1,884 | 47.333333 | 146 | py |
Unsupervised_Anomaly_Detection_Brain_MRI | Unsupervised_Anomaly_Detection_Brain_MRI-master/models/constrained_adversarial_autoencoder_Chen.py | import numpy as np
import tensorflow as tf
from bunch import Bunch
from tensorflow.compat.v1.layers import Dense
from tensorflow.nn import leaky_relu
from tensorflow.python.keras.layers import AvgPool2D, ReLU, Add, LayerNormalization
from tensorflow.python.layers.convolutional import Conv2D, Conv2DTranspose
from tensor... | 7,781 | 46.742331 | 146 | py |
Unsupervised_Anomaly_Detection_Brain_MRI | Unsupervised_Anomaly_Detection_Brain_MRI-master/models/constrained_autoencoder.py | import numpy as np
import tensorflow as tf
from tensorflow.compat.v1.layers import Dense
from tensorflow.python.keras.layers import Conv2D, Flatten, Dropout
from models.customlayers import build_unified_encoder, build_unified_decoder
def constrained_autoencoder(x, dropout_rate, dropout, config):
outputs = {}
... | 1,813 | 36.020408 | 111 | py |
Unsupervised_Anomaly_Detection_Brain_MRI | Unsupervised_Anomaly_Detection_Brain_MRI-master/models/fanogan.py | import numpy as np
import tensorflow as tf
from tensorflow import sigmoid
from tensorflow.compat.v1.layers import Conv2D, Flatten
from tensorflow.compat.v1.layers import Dense
from tensorflow.python.keras.layers import Conv2D, Dropout, Flatten
from models.customlayers import build_unified_decoder, build_unified_encode... | 3,394 | 38.941176 | 134 | py |
Unsupervised_Anomaly_Detection_Brain_MRI | Unsupervised_Anomaly_Detection_Brain_MRI-master/models/autoencoder.py | import numpy as np
import tensorflow as tf
from tensorflow.compat.v1.layers import Dense
from tensorflow.python.keras.layers import Conv2D, Flatten, Dropout
from models.customlayers import build_unified_encoder, build_unified_decoder
def autoencoder(x, dropout_rate, dropout, config):
outputs = {}
with tf.va... | 1,485 | 35.243902 | 111 | py |
Unsupervised_Anomaly_Detection_Brain_MRI | Unsupervised_Anomaly_Detection_Brain_MRI-master/models/constrained_adversarial_autoencoder.py | import numpy as np
import tensorflow as tf
from tensorflow.compat.v1.layers import Dense
from tensorflow.nn import leaky_relu
from tensorflow.python.keras.layers import Flatten, Conv2D, Dropout
from models.customlayers import build_unified_decoder, build_unified_encoder
def constrained_adversarial_autoencoder(z, x, ... | 2,658 | 32.2375 | 111 | py |
Unsupervised_Anomaly_Detection_Brain_MRI | Unsupervised_Anomaly_Detection_Brain_MRI-master/models/fanogan_schlegl.py | import numpy as np
import tensorflow as tf
from bunch import Bunch
from tensorflow.compat.v1.layers import Conv2D, Conv2DTranspose, Dense
from tensorflow.keras.layers import ReLU, Add, LayerNormalization, AvgPool2D
from tensorflow.python.keras.layers import Flatten
from models.customlayers import build_unified_encoder... | 8,423 | 51 | 154 | py |
Unsupervised_Anomaly_Detection_Brain_MRI | Unsupervised_Anomaly_Detection_Brain_MRI-master/models/autoencoder_spatial.py | import tensorflow as tf
from tensorflow.python.keras.layers import Dropout
from models.customlayers import build_unified_encoder, build_unified_decoder
def autoencoder_spatial(x, dropout_rate, dropout, config):
outputs = {}
with tf.variable_scope('Encoder'):
encoder = build_unified_encoder(x.get_sha... | 900 | 31.178571 | 111 | py |
Unsupervised_Anomaly_Detection_Brain_MRI | Unsupervised_Anomaly_Detection_Brain_MRI-master/models/anovaegan.py | import numpy as np
import tensorflow as tf
from tensorflow.compat.v1.layers import Conv2D, Flatten
from tensorflow.compat.v1.layers import Dense
from tensorflow.python.keras.layers import Conv2D, Dropout, Flatten
from models.customlayers import build_unified_decoder, build_unified_encoder
def anovaegan(x, dropout_ra... | 3,258 | 39.234568 | 154 | py |
Unsupervised_Anomaly_Detection_Brain_MRI | Unsupervised_Anomaly_Detection_Brain_MRI-master/models/gaussian_mixture_variational_autoencoder.py | import numpy as np
import tensorflow as tf
from tensorflow.compat.v1.layers import Conv2D
from tensorflow.compat.v1.layers import Dense
from tensorflow.nn import relu
from tensorflow.python.keras.layers import Flatten, Dropout
from models.customlayers import build_unified_encoder, build_unified_decoder
def gaussian_... | 3,390 | 43.618421 | 149 | py |
Unsupervised_Anomaly_Detection_Brain_MRI | Unsupervised_Anomaly_Detection_Brain_MRI-master/models/variational_autoencoder.py | import numpy as np
import tensorflow as tf
from tensorflow.compat.v1.layers import Dense
from tensorflow.python.keras.layers import Conv2D, Dropout, Flatten
from models.customlayers import build_unified_decoder, build_unified_encoder
def variational_autoencoder(x, dropout_rate, dropout, config):
outputs = {}
... | 1,834 | 37.229167 | 111 | py |
Unsupervised_Anomaly_Detection_Brain_MRI | Unsupervised_Anomaly_Detection_Brain_MRI-master/models/context_encoder_variational_autoencoder.py | import numpy as np
import tensorflow as tf
from tensorflow.compat.v1.layers import Dense
from tensorflow.python.keras.layers import Conv2D, Dropout, Flatten
from models.customlayers import build_unified_decoder, build_unified_encoder
def context_encoder_variational_autoencoder(x, x_ce, dropout_rate, dropout, config)... | 2,347 | 38.133333 | 119 | py |
MIAT | MIAT-main/train_MI_estimator_only_max.py | # This version max Natural MI of x and max Adversarial MI of x_adv
import os
import argparse
import numpy as np
import torch.optim as optim
from torch.optim import lr_scheduler, Adam
from tqdm import tqdm
import torch
import torch.nn.functional as F
from torchvision import datasets, transforms
import torch.backends.c... | 17,182 | 37.440716 | 123 | py |
MIAT | MIAT-main/train_MIAT.py | import os
import argparse
import numpy as np
import torch.optim as optim
from torch.optim import lr_scheduler, Adam
from tqdm import tqdm
import torch
import torch.nn.functional as F
from torchvision import datasets, transforms
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from data import ... | 22,429 | 40.080586 | 127 | py |
MIAT | MIAT-main/train_MI_estimator.py | # This version use cosine distance to enhance the difference between the MI of adv and the MI of nat.
import os
import argparse
import numpy as np
import torch.optim as optim
from torch.optim import lr_scheduler, Adam
from tqdm import tqdm
import torch
import torch.nn.functional as F
from torchvision import datasets,... | 23,492 | 40.580531 | 153 | py |
MIAT | MIAT-main/compute_MI.py | import torch
from functions.dim_losses import donsker_varadhan_loss, infonce_loss, fenchel_dual_loss
def compute_loss(args, former_input, latter_input, encoder, dim_local, dim_global, v_out=False, with_latent=False,
fake_relu=False, no_relu=False):
if no_relu and (not with_latent):
prin... | 3,956 | 29.206107 | 114 | py |
MIAT | MIAT-main/data.py | import numpy as np
import torch.utils.data as Data
from PIL import Image
# import tools
import torch
class data_noise_dataset(Data.Dataset):
def __init__(self, img_path, noisy_label_path, clean_label_path):
self.train_data = np.load(img_path).astype(np.float32) # B C H W
self.train_nois... | 2,595 | 31.45 | 144 | py |
MIAT | MIAT-main/test_comparison.py | from __future__ import print_function
import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
import torch.backends.cudnn as cudnn
from data import data_dataset# , data_noise_dataset, distilled_dataset
from models.vggnet import VGGNet19
from models... | 8,587 | 33.629032 | 118 | py |
MIAT | MIAT-main/train_standard.py | from __future__ import print_function
import os
import argparse
import torch
# import torch.nn as nn
import torch.nn.functional as F
# import torchvision
import torch.optim as optim
from torchvision import datasets, transforms
from models.wideresnet import WideResNet
# from models.resnet import ResNet18
from utils.sta... | 10,812 | 38.900369 | 116 | py |
MIAT | MIAT-main/functions/gan_losses.py | '''Losses for training basic GANs.
Most of this was taken out of the f-GAN paper. WGAN (IPM-style) is also supported.
'''
import math
import torch
import torch.nn.functional as F
from functions.misc import log_sum_exp
def raise_measure_error(measure):
supported_measures = ['GAN', 'JSD', 'X2', 'KL', 'RKL', 'D... | 3,171 | 26.344828 | 83 | py |
MIAT | MIAT-main/functions/dim_losses.py | '''cortex_DIM losses.
'''
import math
import torch
import torch.nn.functional as F
from functions.gan_losses import get_positive_expectation, get_negative_expectation
def fenchel_dual_loss(l, m, measure=None):
'''Computes the f-divergence distance between positive and negative joint distributions.
Note t... | 5,148 | 30.981366 | 111 | py |
MIAT | MIAT-main/functions/dim_losses_post.py | '''cortex_DIM losses.
'''
import math
import torch
import torch.nn.functional as F
from functions.gan_losses import get_positive_expectation, get_negative_expectation
def fenchel_dual_loss(l, m, measure=None):
'''Computes the f-divergence distance between positive and negative joint distributions.
Note t... | 5,264 | 30.716867 | 111 | py |
MIAT | MIAT-main/functions/gradient_penalty.py | '''Gradient penalty functions.
'''
import torch
from torch import autograd
def contrastive_gradient_penalty(network, input, penalty_amount=1.):
"""Contrastive gradient penalty.
This is essentially the loss introduced by Mescheder et al 2018.
Args:
network: Network to apply penalty through.
... | 1,238 | 27.813953 | 72 | py |
MIAT | MIAT-main/functions/misc.py | """Miscilaneous functions.
"""
import math
import torch
def log_sum_exp(x, axis=None):
"""Log sum exp function
Args:
x: Input.
axis: Axis over which to perform sum.
Returns:
torch.Tensor: log sum exp
"""
x_max = torch.max(x, axis)[0]
y = torch.log((torch.exp(x - x... | 2,417 | 25.571429 | 81 | py |
MIAT | MIAT-main/models/discriminators.py | import numpy as np
import torch
import torch.nn as nn
class PriorDisc(nn.Module):
def __init__(self):
super().__init__()
self.layer0 = nn.Sequential(
nn.Linear(64, 1000),
nn.ReLU(),
)
self.layer1 = nn.Sequential(
nn.Linear(1000, 200),
... | 7,661 | 25.42069 | 92 | py |
MIAT | MIAT-main/models/resnet.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jun 6 22:46:26 2020
@author: pc-3
"""
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
class Linear(nn.Module):
def __init__(self, in_features, out_features):
super(Linear, self).__init__()
... | 5,724 | 35.006289 | 102 | py |
MIAT | MIAT-main/models/resnet_new.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jun 6 22:46:26 2020
@author: pc-3
"""
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
class Linear(nn.Module):
def __init__(self, in_features, out_features):
super(Linear, self).__init__()
... | 4,523 | 33.8 | 102 | py |
MIAT | MIAT-main/models/wideresnet_new.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu1 = nn.ReLU(inplace=True)
se... | 4,162 | 39.813725 | 116 | py |
MIAT | MIAT-main/models/extractor.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
... | 6,178 | 33.138122 | 104 | py |
MIAT | MIAT-main/models/vggnet.py | import torch.nn as nn
def conv_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
init.xavier_uniform(m.weight, gain=np.sqrt(2))
init.constant(m.bias, 0)
def cfg(depth):
depth_lst = [11, 13, 16, 19]
assert (depth in depth_lst), "Error : VGGnet depth should be ... | 2,135 | 23.837209 | 95 | py |
MIAT | MIAT-main/models/estimator.py | import numpy as np
import torch
import torch.nn as nn
class Estimator(nn.Module):
def __init__(self, n_output, cnn_input=128):
n_input = cnn_input
n_units = n_output
super().__init__()
self.layer0 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=4, stride=2, padding=1, bia... | 5,189 | 26.315789 | 92 | py |
MIAT | MIAT-main/models/wideresnet.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu1 = nn.ReLU(inplace=True)
se... | 3,898 | 40.924731 | 116 | py |
MIAT | MIAT-main/utils/mart_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
def mart_loss(model, x_natural, y, x_adv, beta=6.0):
kl = nn.KLDivLoss(reduction='none')
batch_size = len(x_natural)
logits = model(x_natural)
logits_adv = model(x_adv)
adv_probs = F.softmax(l... | 901 | 27.1875 | 104 | py |
MIAT | MIAT-main/utils/trades_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.optim as optim
def squared_l2_norm(x):
flattened = x.view(x.unsqueeze(0).shape[0], -1)
return (flattened ** 2).sum(1)
def l2_norm(x):
return squared_l2_norm(x).sqrt()
def trades_loss(mod... | 5,958 | 39.815068 | 114 | py |
MIAT | MIAT-main/utils/mma_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.optim as optim
def squared_l2_norm(x):
flattened = x.view(x.unsqueeze(0).shape[0], -1)
return (flattened ** 2).sum(1)
def l2_norm(x):
return squared_l2_norm(x).sqrt()
def mma_loss(model,... | 3,073 | 31.702128 | 91 | py |
MIAT | MIAT-main/utils/standard_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.optim as optim
def standard_loss(model,
x_natural,
y,
optimizer,
step_size=0.007,
epsilon=0.031,
perturb_steps=10,
... | 3,813 | 33.36036 | 112 | py |
MIAT | MIAT-main/utils/dataload.py | import torch
from torch.utils.data import Dataset, DataLoader
import re
import pickle
from PIL import Image
import os
import numpy as np
def sort_key(s):
re_digits = re.compile(r'(\d+)')
pieces = re_digits.split(s)
pieces[1::2] = map(int, pieces[1::2])
return pieces
def load_variavle(filename):
... | 12,207 | 29.292804 | 92 | py |
STR | STR-master/main.py | import os
import pathlib
import random
import shutil
import time
import json
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
from torch.utils.tensorboard import SummaryWriter
from utils.logg... | 16,340 | 32.213415 | 122 | py |
STR | STR-master/trainer.py | import time
import torch
import tqdm
from utils.eval_utils import accuracy
from utils.logging import AverageMeter, ProgressMeter
__all__ = ["train", "validate"]
def train(train_loader, model, criterion, optimizer, epoch, args, writer):
batch_time = AverageMeter("Time", ":6.3f")
data_time = AverageMeter("Da... | 3,536 | 29.491379 | 83 | py |
STR | STR-master/models/resnet.py | import torch.nn as nn
from utils.builder import get_builder
from args import args
# BasicBlock {{{
class BasicBlock(nn.Module):
M = 2
expansion = 1
def __init__(self, builder, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = builder.conv3x3(inp... | 4,838 | 28.506098 | 80 | py |
STR | STR-master/models/mobilenetv1.py | import torch.nn as nn
from utils.builder import get_builder
class MobileNetV1(nn.Module):
def __init__(self):
super(MobileNetV1, self).__init__()
builder = get_builder()
def conv_bn(inp, oup, stride):
return nn.Sequential(
builder.conv2d(inp, oup, 3, stride, 1,... | 1,526 | 27.277778 | 79 | py |
STR | STR-master/utils/bn_type.py | import torch.nn as nn
LearnedBatchNorm = nn.BatchNorm2d
class NonAffineBatchNorm(nn.BatchNorm2d):
def __init__(self, dim):
super(NonAffineBatchNorm, self).__init__(dim, affine=False)
| 198 | 21.111111 | 67 | py |
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