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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tensorflow | tensorflow-master/tensorflow/compiler/mlir/tensorflow/tests/tf_saved_model/keras.py | # Copyright 2019 The TensorFlow Authors. 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 applica... | 1,705 | 33.816327 | 128 | py |
tensorflow | tensorflow-master/tensorflow/lite/tools/optimize/python/modify_model_interface_lib_test.py | # Copyright 2020 The TensorFlow Authors. 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 applica... | 6,651 | 39.809816 | 80 | py |
tensorflow | tensorflow-master/tensorflow/lite/testing/op_tests/prelu.py | # Copyright 2019 The TensorFlow Authors. 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 applica... | 3,567 | 34.326733 | 80 | py |
tensorflow | tensorflow-master/tensorflow/lite/python/tflite_keras_util.py | # Copyright 2020 The TensorFlow Authors. 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 applica... | 6,682 | 33.271795 | 80 | py |
tensorflow | tensorflow-master/tensorflow/lite/python/interpreter_test.py | # Copyright 2018 The TensorFlow Authors. 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 applica... | 24,036 | 41.618794 | 86 | py |
tensorflow | tensorflow-master/tensorflow/lite/python/lite_v2_test.py | # Copyright 2019 The TensorFlow Authors. 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 applica... | 198,532 | 40.13821 | 174 | py |
tensorflow | tensorflow-master/tensorflow/lite/python/tflite_convert_test.py | # Copyright 2019 The TensorFlow Authors. 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 applica... | 23,156 | 37.919328 | 80 | py |
tensorflow | tensorflow-master/tensorflow/lite/python/lite.py | # Copyright 2023 The TensorFlow Authors. 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 applica... | 125,211 | 37.432167 | 126 | py |
tensorflow | tensorflow-master/tensorflow/lite/python/interpreter.py | # Copyright 2018 The TensorFlow Authors. 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 applica... | 38,775 | 37.970854 | 119 | py |
tensorflow | tensorflow-master/tensorflow/lite/python/lite_v2_test_util.py | # Copyright 2019 The TensorFlow Authors. 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 applica... | 10,180 | 34.106897 | 80 | py |
tensorflow | tensorflow-master/tensorflow/lite/python/lite_test.py | # Copyright 2018 The TensorFlow Authors. 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 applica... | 122,961 | 40.966553 | 128 | py |
tensorflow | tensorflow-master/tensorflow/lite/python/analyzer.py | # Copyright 2021 The TensorFlow Authors. 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 applica... | 3,933 | 36.113208 | 99 | py |
tensorflow | tensorflow-master/tensorflow/lite/python/util.py | # Copyright 2018 The TensorFlow Authors. 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 applica... | 40,808 | 36.96186 | 100 | py |
tensorflow | tensorflow-master/tensorflow/lite/python/tflite_convert.py | # Copyright 2018 The TensorFlow Authors. 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 applica... | 26,390 | 36.972662 | 80 | py |
tensorflow | tensorflow-master/tensorflow/lite/python/convert.py | # Copyright 2022 The TensorFlow Authors. 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 applica... | 48,303 | 39.219817 | 102 | py |
tensorflow | tensorflow-master/tensorflow/lite/python/util_test.py | # Copyright 2019 The TensorFlow Authors. 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 applica... | 17,660 | 39.976798 | 80 | py |
tensorflow | tensorflow-master/tensorflow/lite/python/metrics/metrics_nonportable_test.py | # Copyright 2021 The TensorFlow Authors. 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 applica... | 24,544 | 40.885666 | 80 | py |
DataPoisoning_FL | DataPoisoning_FL-master/generate_default_models.py | from federated_learning.arguments import Arguments
from federated_learning.nets import Cifar10CNN
from federated_learning.nets import FashionMNISTCNN
import os
import torch
from loguru import logger
if __name__ == '__main__':
args = Arguments(logger)
if not os.path.exists(args.get_default_model_folder_path()):... | 928 | 37.708333 | 96 | py |
DataPoisoning_FL | DataPoisoning_FL-master/client.py | import torch
import torch.optim as optim
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from federated_learning.schedulers import MinCapableStepLR
import os
import numpy
import copy
class Client:
def __init__(self, args, client_idx, train_data_loader, test_data_load... | 7,303 | 33.616114 | 155 | py |
DataPoisoning_FL | DataPoisoning_FL-master/federated_learning/arguments.py | from .nets import Cifar10CNN
from .nets import FashionMNISTCNN
from .worker_selection import BeforeBreakpoint
from .worker_selection import AfterBreakpoint
from .worker_selection import PoisonerProbability
import torch.nn.functional as F
import torch
import json
# Setting the seed for Torch
SEED = 1
torch.manual_seed(... | 7,025 | 32.298578 | 152 | py |
DataPoisoning_FL | DataPoisoning_FL-master/federated_learning/nets/cifar_10_cnn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class Cifar10CNN(nn.Module):
def __init__(self):
super(Cifar10CNN, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(32)
self.conv2 = nn.Conv2d(32, 32, kernel_size=3... | 1,509 | 29.2 | 66 | py |
DataPoisoning_FL | DataPoisoning_FL-master/federated_learning/nets/fashion_mnist_cnn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class FashionMNISTCNN(nn.Module):
def __init__(self):
super(FashionMNISTCNN, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(1, 16, kernel_size=5, padding=2),
nn.BatchNorm2d(16),
nn.ReLU... | 730 | 21.84375 | 56 | py |
DataPoisoning_FL | DataPoisoning_FL-master/federated_learning/datasets/dataset.py | from abc import abstractmethod
from torch.utils.data import DataLoader
from torch.utils.data import TensorDataset
import torch
import numpy
class Dataset:
def __init__(self, args):
self.args = args
self.train_dataset = self.load_train_dataset()
self.test_dataset = self.load_test_dataset()
def get_args(self)... | 2,732 | 23.401786 | 110 | py |
DataPoisoning_FL | DataPoisoning_FL-master/federated_learning/datasets/fashion_mnist.py | from .dataset import Dataset
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader
class FashionMNISTDataset(Dataset):
def __init__(self, args):
super(FashionMNISTDataset, self).__init__(args)
def load_train_dataset(self):
self.get_args().... | 1,332 | 38.205882 | 160 | py |
DataPoisoning_FL | DataPoisoning_FL-master/federated_learning/datasets/cifar10.py | from .dataset import Dataset
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader
class CIFAR10Dataset(Dataset):
def __init__(self, args):
super(CIFAR10Dataset, self).__init__(args)
def load_train_dataset(self):
self.get_args().get_logger... | 1,729 | 36.608696 | 126 | py |
DataPoisoning_FL | DataPoisoning_FL-master/federated_learning/datasets/data_distribution/iid_equal.py | import torch
def distribute_batches_equally(train_data_loader, num_workers):
"""
Gives each worker the same number of batches of training data.
:param train_data_loader: Training data loader
:type train_data_loader: torch.utils.data.DataLoader
:param num_workers: number of workers
:type num_wo... | 607 | 29.4 | 66 | py |
DataPoisoning_FL | DataPoisoning_FL-master/federated_learning/schedulers/min_lr_step.py | class MinCapableStepLR:
def __init__(self, logger, optimizer, step_size, gamma, min_lr):
"""
:param logger: logger
:type logger: loguru.logger
:param optimizer:
:type optimizer: torch.optim
:param step_size: # of epochs between LR updates
:type step_size: int... | 1,451 | 28.632653 | 95 | py |
DataPoisoning_FL | DataPoisoning_FL-master/federated_learning/parameters/model_comparison.py | import torch
def compare_models(logger, model_1, model_2):
models_differ = 0
for key_item_1, key_item_2 in zip(model_1.state_dict().items(), model_2.state_dict().items()):
if not torch.equal(key_item_1[1], key_item_2[1]):
models_differ += 1
if (key_item_1[0] == key_item_2[0]):
... | 651 | 39.75 | 98 | py |
DataPoisoning_FL | DataPoisoning_FL-master/federated_learning/parameters/gradients.py | import numpy
def calculate_model_gradient(logger, model_1, model_2):
"""
Calculates the gradient (parameter difference) between two Torch models.
:param logger: loguru.logger
:param model_1: torch.nn
:param model_2: torch.nn
"""
model_1_parameters = list(dict(model_1.state_dict()))
mod... | 945 | 32.785714 | 88 | py |
SACD | SACD-main/SACD/inference.py | import time
import json
import torch
import torch.nn.functional as F
from collections import defaultdict
from utils import AverageMeter
def get_video_results(outputs, class_names, output_topk):
sorted_scores, locs = torch.topk(outputs, k=min(output_topk, len(class_names)))
video_results = []
for i in ran... | 2,798 | 33.555556 | 85 | py |
SACD | SACD-main/SACD/main.py | import json
import pdb
import random
import os
import numpy as np
import torch
import torchvision
import torch.multiprocessing as mp
import torch.distributed as dist
from torch.backends import cudnn
from torch.nn import CrossEntropyLoss
from torch.optim import SGD, lr_scheduler
from torch.utils.data import DataLoader
f... | 21,360 | 39.533207 | 142 | py |
SACD | SACD-main/SACD/val.py | from pathlib import Path
import json
import random
import os
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from torch.optim import SGD, lr_scheduler
import torch.multiprocessing as mp
import torch.distributed as dist
from torch.backends import cudnn
import torchvision
from opts import parse_op... | 16,681 | 37.976636 | 86 | py |
SACD | SACD-main/SACD/validation.py | import time
import torch
import torch.distributed as dist
from utils import AverageMeter, calculate_accuracy
def val_epoch(epoch,
data_loader,
model,
criterion,
device,
logger,
tb_writer=None,
distributed=False):
pri... | 2,770 | 34.987013 | 99 | py |
SACD | SACD-main/SACD/training.py | import time
import torch
import torch.distributed as dist
import torch.nn.functional as F
import random
from utils import AverageMeter, calculate_accuracy, write_to_batch_logger, write_to_epoch_logger
from graph_configure import *
def train_epoch(epoch,
data_loader,
model,
... | 9,275 | 39.330435 | 137 | py |
SACD | SACD-main/SACD/utils.py | import csv
import random
from functools import partialmethod
import torch
import numpy as np
from sklearn.metrics import precision_recall_fscore_support
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
... | 2,969 | 23.957983 | 95 | py |
SACD | SACD-main/SACD/model.py | import torch
from torch import nn
from models import r21d
from models import resnet, resnet2p1d, pre_act_resnet, wide_resnet, resnext, densenet
from models import composition_classifier
def get_module_name(name):
name = name.split('.')
if name[0] == 'module':
i = 1
else:
i = 0
if name[... | 2,433 | 28.682927 | 133 | py |
SACD | SACD-main/SACD/dataset.py | from torchvision import get_image_backend
from datasets.videodataset import VideoDataset
from datasets.videodataset_multiclips import (VideoDatasetMultiClips,
collate_fn)
from datasets.loader import VideoLoader, VideoLoaderHDF5, VideoLoaderFlowHDF5
from datasets.videodata... | 8,779 | 41.829268 | 106 | py |
SACD | SACD-main/SACD/train.py | import argparse
import os
import torch
from torch import nn
from torch.nn import functional as F
from data import data_helper
from models import model_factory
from optimizer.optimizer_helper import *
from utils.Logger import Logger
from utils.losses import *
from utils.anchor_selector import *
from tqdm import tqdm
imp... | 14,626 | 48.921502 | 155 | py |
SACD | SACD-main/SACD/spatial_transforms.py | import random
from torchvision.transforms import transforms
from torchvision.transforms import functional as F
from PIL import Image
class Compose(transforms.Compose):
def randomize_parameters(self):
for t in self.transforms:
t.randomize_parameters()
class ToTensor(transforms.ToTensor):
... | 5,663 | 24.628959 | 95 | py |
SACD | SACD-main/SACD/models/resnet2p1d.py | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
def get_inplanes():
return [64, 128, 256, 512]
def conv1x3x3(in_planes, mid_planes, stride=1):
return nn.Conv3d(in_planes,
mid_planes,
kernel_size=(1, 3, 3)... | 9,642 | 32.716783 | 80 | py |
SACD | SACD-main/SACD/models/pre_act_resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .resnet import conv3x3x3, conv1x1x1, get_inplanes, ResNet
class PreActivationBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super().__init__()
self.bn1 = nn.BatchNo... | 3,041 | 27.698113 | 79 | py |
SACD | SACD-main/SACD/models/resnet.py | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
def get_inplanes():
return [64, 128, 256, 512]
def conv3x3x3(in_planes, out_planes, stride=1):
return nn.Conv3d(in_planes,
out_planes,
kernel_size=3,
... | 7,549 | 31.12766 | 80 | py |
SACD | SACD-main/SACD/models/densenet.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class _DenseLayer(nn.Sequential):
def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
super().__init__()
self.add_module('norm1', nn.BatchNorm3d(num_input_featu... | 7,182 | 37.827027 | 95 | py |
SACD | SACD-main/SACD/models/resnext.py | import math
from functools import partial
import torch
import torch.nn as nn
import torch.nn.functional as F
from .resnet import conv1x1x1, Bottleneck, ResNet
from utils import partialclass
def get_inplanes():
return [128, 256, 512, 1024]
class ResNeXtBottleneck(Bottleneck):
expansion = 2
def __init_... | 2,402 | 31.04 | 74 | py |
SACD | SACD-main/SACD/models/wide_resnet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from . import resnet
class WideBottleneck(resnet.Bottleneck):
expansion = 2
def generate_model(model_depth, k, **kwargs):
assert model_depth in [50, 101, 152, 200]
inplanes = [x * k for x in resnet.get_inplanes()]
if model_depth ==... | 784 | 28.074074 | 80 | py |
SACD | SACD-main/SACD/models/r21d.py | """R2plus1D"""
import math
from collections import OrderedDict
import torch
import torch.nn as nn
from torch.nn.modules.utils import _triple
class SpatioTemporalConv(nn.Module):
"""Applies a factored 3D convolution over an input signal composed of several input
planes with distinct spatial and time axes, by p... | 10,212 | 43.212121 | 135 | py |
SACD | SACD-main/SACD/models/composition_classifier.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class CompositionClassifier(nn.Module):
def __init__(self, input_dim, num_classes, normalization_sign=False):
super().__init__()
half_input_dim = int(input_dim / 2)
self.mlp = nn.Linear(input_dim, half_input_dim)
s... | 1,286 | 29.642857 | 113 | py |
SACD | SACD-main/SACD/datasets/activitynet.py | import math
import json
import torch
import torch.utils.data as data
from .loader import VideoLoader
from .videodataset import VideoDataset
def get_n_frames(video_path):
return len([
x for x in video_path.iterdir()
if 'image' in x.name and x.name[0] != '.'
])
def get_class_labels(data):
... | 5,382 | 31.624242 | 80 | py |
SACD | SACD-main/SACD/datasets/videodataset_multiclips.py | import copy
import torch
from torch.utils.data.dataloader import default_collate
from .videodataset import VideoDataset
def collate_fn(batch):
batch_clips, batch_targets = zip(*batch)
batch_clips = [clip for multi_clips in batch_clips for clip in multi_clips]
batch_targets = [
target for multi_ta... | 2,303 | 33.909091 | 79 | py |
SACD | SACD-main/SACD/datasets/videodataset.py | import json
import os
import torch
import torch.utils.data as data
from pathlib import Path
from .loader import VideoLoader
from .loader import EEGFeatureLoader
from random import randrange
import numpy as np
def get_class_labels(data):
class_labels_map = {}
index = 0
for class_label in data['labels']:
... | 10,023 | 33.805556 | 99 | py |
SACD | SACD-main/SACD/grad_cam/main_swin.py | import os
import math
import numpy as np
import torch
from PIL import Image
import matplotlib.pyplot as plt
from torchvision import transforms
from utils import GradCAM, show_cam_on_image, center_crop_img
from swin_model import swin_base_patch4_window7_224
class ResizeTransform:
def __init__(self, im_h: int, im_w... | 2,632 | 31.109756 | 109 | py |
SACD | SACD-main/SACD/grad_cam/swin_model.py | """ Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`
- https://arxiv.org/pdf/2103.14030
Code/weights from https://github.com/microsoft/Swin-Transformer
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoin... | 28,233 | 40.704579 | 113 | py |
SACD | SACD-main/SACD/grad_cam/utils.py | import cv2
import numpy as np
class ActivationsAndGradients:
""" Class for extracting activations and
registering gradients from targeted intermediate layers """
def __init__(self, model, target_layers, reshape_transform):
self.model = model
self.gradients = []
self.activations = ... | 8,018 | 33.714286 | 111 | py |
SACD | SACD-main/SACD/grad_cam/main_cnn.py | import os
import numpy as np
import torch
from PIL import Image
import matplotlib.pyplot as plt
from torchvision import models
from torchvision import transforms
from utils import GradCAM, show_cam_on_image, center_crop_img
def main():
model = models.mobilenet_v3_large(pretrained=True)
target_layers = [model.... | 1,846 | 30.844828 | 109 | py |
SACD | SACD-main/SACD/grad_cam/vit_model.py | """
original code from rwightman:
https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
"""
from functools import partial
from collections import OrderedDict
import torch
import torch.nn as nn
def drop_path(x, drop_prob: float = 0., training: bool = False):
"""
Drop ... | 18,204 | 42.037825 | 126 | py |
SACD | SACD-main/SACD/grad_cam/main_vit.py | import os
import numpy as np
import torch
from PIL import Image
import matplotlib.pyplot as plt
from torchvision import transforms
from utils import GradCAM, show_cam_on_image, center_crop_img
from vit_model import vit_base_patch16_224
class ReshapeTransform:
def __init__(self, model):
input_size = model.... | 2,710 | 35.146667 | 101 | py |
SACD | SACD-main/SACD/util_scripts/remove_dataparallel.py | import argparse
from collections import OrderedDict
import torch
parser = argparse.ArgumentParser()
parser.add_argument('file_path', type=str)
parser.add_argument('--dst_file_path', default=None, type=str)
args = parser.parse_args()
if args.dst_file_path is None:
args.dst_file_path = args.file_path
x = torch.lo... | 569 | 22.75 | 62 | py |
SACD | SACD-main/SACD/loss/nce_loss.py | import torch
import torch.nn.functional as F
from torch import nn
EPISILON=1e-10
class NCELoss(torch.nn.Module):
def __init__(self, temperature=1):
super(NCELoss, self).__init__()
self.temperature = temperature
self.softmax = nn.Softmax(dim=1)
def where(self, cond, x_1, x_2):
cond = cond.type(to... | 1,346 | 28.933333 | 122 | py |
SACD | SACD-main/SACD/loss/jsd_loss.py | import torch
import torch.nn.functional as F
EPISILON=1e-10
class JSDLoss(torch.nn.Module):
def __init__(self, weight=1.0, softmax_sign=False):
super(JSDLoss, self).__init__()
self.weight = weight
self.softmax_sign = softmax_sign
def forward(self, p, q):
if self.softmax_sign is False:
p = ... | 587 | 20.777778 | 53 | py |
linkPrediction | linkPrediction-master/classification.py | import pandas as pd
import networkx as nx
import utils as ut
import numpy as np
from keras.models import Model, Sequential
from keras import optimizers
from keras.layers import LSTM, Dense
from keras.layers.normalization import BatchNormalization
from keras.layers.core import Dense, Dropout, Activation, Reshape
from sk... | 9,347 | 40.180617 | 111 | py |
linkPrediction | linkPrediction-master/versions.py | # scipy
import scipy
print('scipy: %s' % scipy.__version__)
# numpy
import numpy
print('numpy: %s' % numpy.__version__)
# matplotlib
import matplotlib
print('matplotlib: %s' % matplotlib.__version__)
# pandas
import pandas
print('pandas: %s' % pandas.__version__)
# scikit-learn
import sklearn
print('sklearn: %s' % skle... | 627 | 21.428571 | 50 | py |
Fund-EL | Fund-EL-main/Entity Linking/EDFunctions.py | from transformers import BertTokenizerFast
from torch.utils.data import TensorDataset
import torch
#Functions
ENT_START_TAG = "[unused0]"
ENT_END_TAG = "[unused1]"
def get_context_representation(
sample,
tokenizer,
max_seq_length,
mention_key="mention",
context_key="context",
ent_start_token=EN... | 3,133 | 30.979592 | 142 | py |
Fund-EL | Fund-EL-main/Entity Linking/NERFunctions.py | import pandas as pd
import numpy as np
import torch
#Add [CLS] and [SEP] tokens, pad until "pad_len" chars.
def add_and_pad(lst,pad_len,cls,sep,pad):
new_lst = []
for item in lst:
new_item = [cls] + item + [sep]
while len(new_item) != pad_len:
new_item.append(pad)
new_lst.ap... | 7,287 | 37.765957 | 135 | py |
Fund-EL | Fund-EL-main/Named Entity Recognition/NERFunctions.py | import pandas as pd
import numpy as np
import torch
#Add [CLS] and [SEP] tokens, pad until "pad_len" chars.
def add_and_pad(lst,pad_len,cls,sep,pad):
new_lst = []
for item in lst:
new_item = [cls] + item + [sep]
while len(new_item) != pad_len:
new_item.append(pad)
new_lst.ap... | 7,287 | 37.765957 | 135 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/precompute_text.py | """
Utility to precompute text features.
Notes:
If running other models than BERT, be aware of the following things:
The preprocessor function for BERT adds [SEP] and [CLS], use a different preprocessor to create different tokens
for another models. Optionally set "add_special_tokens" to True in the da... | 20,065 | 44.295711 | 119 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/mart_build_vocab.py | """
Build vocabulary for MART.
References:
Copyright (c) 2017 Jie Lei
Licensed under The MIT License, see https://choosealicense.com/licenses/mit/
@inproceedings{lei2020mart,
title={MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning},
author={Lei, Jie and W... | 4,698 | 33.551471 | 110 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/train_caption.py | """
Train captioning with MART.
Originally published by https://github.com/jayleicn/recurrent-transformer under MIT license
Reworked by https://github.com/gingsi/coot-videotext under Apache 2 license
"""
import numpy as np
from coot.configs_retrieval import ExperimentTypesConst
from mart import arguments_mart
from m... | 5,521 | 42.825397 | 121 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/mart/optimization.py | """
PyTorch optimization for BERT model. Required.
References:
Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
Licensed under the Apache License, Version 2.0, see http://www.apache.org/licenses/LICENSE-2.0
check if this is needed? Can't I just use regular Adam, AdamW, RAdam, ... | 15,322 | 35.570406 | 118 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/mart/recursive_caption_dataset.py | """
Captioning dataset.
"""
import copy
import json
import math
import os
from pathlib import Path
from typing import List, Optional, Tuple
import h5py
import nltk
import numpy as np
import torch
from torch.utils import data
from torch.utils.data.dataloader import default_collate
from tqdm import tqdm
import sys
from... | 27,806 | 34.377863 | 105 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/mart/model.py | """
MART model.
"""
import copy
import logging
import math
from pathlib import Path
import sys
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from torch.utils.tensorboard.summary import video
from mart.configs_mart import MartConfig, MartPathConst
from mart.masked_transformer imp... | 35,697 | 36.03112 | 320 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/mart/loss_caption.py | """
MART loss.
"""
import torch
import torch.nn.functional as F
from torch import nn
class LabelSmoothingLoss(nn.Module):
"""
With label smoothing,
KL-divergence between q_{smoothed ground truth prob.}(w)
and p_{prob. computed by model}(w) is minimized.
"""
def __init__(self, label_smoothing... | 1,719 | 32.076923 | 96 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/mart/masked_transformer.py | """
Classic Vanilla Transformer (BERT-like).
References:
Copyright (c) 2018, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause, see https://opensource.org/licenses/BSD-3-Clause
History:
https://github.com/salesforce/densecap
https://github.com/jayleicn/recurrent-t... | 10,247 | 32.6 | 113 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/mart/beam_search.py | """
https://github.com/OpenNMT/OpenNMT-py/blob/master/onmt/translate/beam_search.py
References:
Copyright (c) 2017 Adam Lerer
Licensed under The MIT License, see https://choosealicense.com/licenses/mit/
@inproceedings{klein-etal-2017-opennmt,
title = "{O}pen{NMT}: Open-Source Toolkit for Neural Mac... | 17,745 | 36.438819 | 88 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/mart/trainer_caption.py | """
Trainer for retrieval training and validation. Holds the main training loop.
"""
import json
import logging
import os
from collections import defaultdict
from collections.abc import Mapping
from glob import glob
from pathlib import Path
from timeit import default_timer as timer
from typing import Dict, List, Optio... | 41,225 | 39.220488 | 118 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/mart/translator.py | """
Text generation, greedy or beam search.
References:
Copyright (c) 2017 Jie Lei
Licensed under The MIT License, see https://choosealicense.com/licenses/mit/
@inproceedings{lei2020mart,
title={MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning},
author={L... | 22,226 | 37.256454 | 109 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/lr_scheduler.py | """
LR Schedulers completely rewritten from scratch.
These fit better to some use cases than the PyTorch LR schedulers.
Features:
All required information is passed to the schedulers:
(total number of epochs, training steps per epoch, validation improvements)
Option for warmup per step or per epoch in... | 17,000 | 36.039216 | 116 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/optimization.py | """
Optimizers.
"""
import math
from typing import Dict, Iterable
import torch as th
from torch.optim import Adam
from torch.optim.optimizer import Optimizer
from nntrainer import typext
class OptimizerConst(typext.ConstantHolder):
"""
Optimizer name constants.
"""
ADAM = "adam"
RADAM = "radam"... | 10,552 | 38.524345 | 110 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/data.py | """
Dataset utilities.
"""
from typing import Any, Callable, List, Optional
from torch.utils import data
from nntrainer import trainer_configs, typext
class DataSplitConst(typext.ConstantHolder):
"""
Store dataset splits.
"""
TRAIN = "train"
VAL = "val"
TEST = "test"
def create_loader(dat... | 966 | 25.135135 | 105 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/typext.py | """
Custom typing extension.
Classes:
ConstantHolder: Base class for storing constants and avoiding to hardcode everything.
SaveableBaseModel: Child class of pydantic.BaseModel which enables saving and loading that BaseModel.
TypedNamedTuple: Child class of SaveableBaseModel, can be used similarly to a Nam... | 16,825 | 31.671845 | 118 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/retrieval.py | """
Utility code for doing retrieval.
"""
from timeit import default_timer as timer
from typing import Callable, Dict, Tuple
import numpy as np
import torch as th
VALKEYS = ["r1", "r5", "r10", "r50", "medr", "meanr", "sum"]
VALHEADER = "Retriev | R@1 | R@5 | R@10 | R@50 | MeanR | MedR | Sum"
def retriev... | 3,505 | 34.414141 | 114 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/initialization.py | """
Network initialization.
"""
import torch as th
from torch import nn
from nntrainer import utils_torch, typext, utils
def init_weight_(w: th.Tensor, init_type="uniform", init_std=1) -> None:
"""
Initialize given tensor.
Args:
w: Tensor to initialize in-place.
init_type: Distribution t... | 3,871 | 33.571429 | 114 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/trainer_base.py | """
Generic Deep Learning trainer that automates tasks required for all kinds of training.
"""
import datetime
import logging
import os
from pathlib import Path
from timeit import default_timer as timer
from typing import Any, Dict, List, Optional, Tuple
import torch as th
from torch import nn
from torch.backends impo... | 32,989 | 43.104278 | 120 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/utils_torch.py | """
Utilities for randomness.
"""
import ctypes
import multiprocessing
import os
import random
import subprocess
import time
import traceback
from timeit import default_timer as timer
from typing import Any, Dict, List, Tuple
import GPUtil
import numpy as np
import psutil
import torch as th
import torch.backends.cudnn... | 8,025 | 32.302905 | 118 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/metric.py | """
Metric writing and reading utilities.
This automates the following:
- Logging the same metrics both to text files and to tensorboard.
- Reload old metrics when resuming training.
- Only save metrics where something was logged to.
"""
import json
import logging
from collections import defaultdict
from ... | 16,453 | 36.825287 | 117 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/examples/mlp_mnist.py | """
Setup a simple 2-layer MLP experiment on the MNIST dataset.
"""
import logging
from functools import partial
from timeit import default_timer as timer
from typing import Dict, Optional, Tuple
import torch as th
from torch import nn
from torch.utils import data as th_data
from tqdm import tqdm
from nntrainer impo... | 11,933 | 36.29375 | 120 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/examples/run_mlp_mnist.py | """
Script to run the MLP MNIST example.
Examples:
python -m nntrainer.examples.run_mlp_mnist -e mnist
python -m nntrainer.examples.run_mlp_mnist -c config/mlp/default/mnist.yaml
python -m nntrainer.examples.run_mlp_mnist -e mnist -n 3
Notes:
Training workflow will look like this:
1. Load config... | 4,849 | 35.466165 | 120 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/models/mlp.py | """
Fully connected network model.
"""
from functools import partial
from typing import Any, Dict, List, Optional
from torch import nn
from nntrainer import models, typext, utils
class ResidualsEnum(typext.ConstantHolder):
"""
Residuals.
None: No residual.
Passthrough: Pass input directly as the re... | 6,203 | 36.373494 | 118 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/models/transformer_legacy.py | """
Transformer implementation.
Similar inference speed to pytorch built-in transformers.
"""
from typing import Any, Dict, List, Optional, cast
import numpy as np
import torch as th
from torch import nn
import nntrainer.trainer_configs
import nntrainer.typext
import nntrainer.utils_torch
from nntrainer.initializat... | 22,104 | 36.915952 | 119 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/models/encoder.py | """
Positional encoding for transformer input.
"""
from __future__ import annotations
from typing import Any, Dict, Optional, Union
import torch as th
from torch import nn
from nntrainer import typext, utils
def make_encoder_module(dim: int, name: str, cfg: Optional[EncoderConfig] = None) -> Optional[nn.Module]:
... | 3,839 | 30.735537 | 115 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/models/poolers.py | """
Pooling modules.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Union
import torch
from torch import nn
from nntrainer import typext
from nntrainer.models.activations import ActivationConfig, make_activation_module
from nntrainer.typext import INF
def make_pooler_module(no... | 10,048 | 33.771626 | 119 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/models/activations.py | """
Activation functions.
"""
from __future__ import annotations
from typing import Any, Dict, Optional, Union
from torch import nn
from nntrainer import typext, utils
def make_activation_module(name: str, cfg: Optional[ActivationConfig] = None) -> nn.Module:
"""
Get activation module instance given by nam... | 1,923 | 28.151515 | 110 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/models/normalizations.py | """
Normalization functions.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Union
import torch as th
from torch import nn
import nntrainer.utils
from nntrainer.typext import ConfigClass, ConstantHolder
def make_normalization_module(normalized_shape: Union[int, List[int], th.Si... | 3,479 | 33.117647 | 120 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/nntrainer/models/model_manager_base.py | """
Base class for the model manager that handles generic model-related tasks.
This way, trainer and model can be separated in the code.
"""
from typing import Any, Dict, List, Tuple
import numpy as np
import torch as th
from torch import nn
import nntrainer.trainer_configs
class BaseModelManager:
def __init_... | 6,006 | 36.779874 | 105 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/coot/features_loader.py | """
Feature loading.
"""
import json
import os
from pathlib import Path
from typing import List, Tuple
import h5py
import numpy as np
from tqdm import tqdm
from nntrainer.utils_torch import create_shared_array
class VideoFeatureLoader:
"""
Helper class to load video features (h5) format.
Args:
... | 8,017 | 39.908163 | 117 | py |
RelationalFutureCaptioningModel | RelationalFutureCaptioningModel-main/coot/loss_fn.py | """
Loss functions.
"""
from typing import Callable, Dict
import torch as th
from torch import nn
from nntrainer import typext
from nntrainer.typext import INF
class LossesConst(typext.ConstantHolder):
CONTRASTIVE = "contrastive"
CROSSENTROPY = "crossentropy"
def cosine_sim(visual_emb: th.Tensor, text_em... | 15,912 | 40.012887 | 120 | py |
BNPG | BNPG-main/BN_MAPPO_Aloha/onpolicy/config.py | import argparse
def get_config():
"""
The configuration parser for common hyperparameters of all environment.
Please reach each `scripts/train/<env>_runner.py` file to find private hyperparameters
only used in <env>.
Prepare parameters:
--algorithm_name <algorithm_name>
specifi... | 16,575 | 55.767123 | 261 | py |
BNPG | BNPG-main/BN_MAPPO_Aloha/onpolicy/envs/env_wrappers.py | """
Modified from OpenAI Baselines code to work with multi-agent envs
"""
import numpy as np
import torch
from multiprocessing import Process, Pipe
from abc import ABC, abstractmethod
from onpolicy.utils.util import tile_images
class CloudpickleWrapper(object):
"""
Uses cloudpickle to serialize contents (other... | 28,231 | 33.262136 | 118 | py |
BNPG | BNPG-main/BN_MAPPO_Aloha/onpolicy/envs/aloha/aloha.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from .multiagentenv import MultiAgentEnv
import atexit
from operator import attrgetter
from copy import deepcopy
import numpy as np
import enum
import math
from absl import logging
import random
from gym imp... | 6,394 | 30.658416 | 172 | py |
BNPG | BNPG-main/BN_MAPPO_Aloha/onpolicy/algorithms/r_mappo/r_mappo.py | import numpy as np
import torch
import torch.nn as nn
from onpolicy.utils.util import get_gard_norm, huber_loss, mse_loss
from onpolicy.algorithms.utils.util import _h_A
from onpolicy.utils.popart import PopArt
from onpolicy.algorithms.utils.util import check
import json
class R_MAPPO():
"""
Trainer class for ... | 11,581 | 47.258333 | 266 | py |
BNPG | BNPG-main/BN_MAPPO_Aloha/onpolicy/algorithms/r_mappo/algorithm/r_actor_critic.py | import torch
import torch.nn as nn
from onpolicy.algorithms.utils.util import init, check
from onpolicy.algorithms.utils.cnn import CNNBase
from onpolicy.algorithms.utils.mlp import MLPBase
from onpolicy.algorithms.utils.rnn import RNNLayer
from onpolicy.algorithms.utils.act import ACTLayer
from onpolicy.utils.util imp... | 11,876 | 51.321586 | 137 | py |
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