repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
wenet | wenet-main/wenet/efficient_conformer/encoder.py | # Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
# 2022 58.com(Wuba) Inc AI Lab.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a c... | 25,877 | 44.4 | 88 | py |
wenet | wenet-main/wenet/efficient_conformer/convolution.py | # Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
# 2022 58.com(Wuba) Inc AI Lab.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/lic... | 5,749 | 36.096774 | 78 | py |
wenet | wenet-main/wenet/efficient_conformer/attention.py | # Copyright (c) 2019 Shigeki Karita
# 2020 Mobvoi Inc (Binbin Zhang)
# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
# 2022 58.com(Wuba) Inc AI Lab.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the ... | 10,873 | 42.670683 | 88 | py |
wenet | wenet-main/wenet/squeezeformer/conv2d.py | # Copyright (c) 2022 Ximalaya Inc. (authors: Yuguang Yang)
#
# 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... | 2,570 | 37.373134 | 84 | py |
wenet | wenet-main/wenet/squeezeformer/encoder_layer.py | # Copyright (c) 2022 Ximalaya Inc. (authors: Yuguang Yang)
#
# 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... | 4,712 | 37.631148 | 80 | py |
wenet | wenet-main/wenet/squeezeformer/positionwise_feed_forward.py | # Copyright (c) 2019 Shigeki Karita
# 2020 Mobvoi Inc (Binbin Zhang)
# 2022 Ximalaya Inc (Yuguang Yang)
#
# 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:... | 2,988 | 36.3625 | 74 | py |
wenet | wenet-main/wenet/squeezeformer/subsampling.py | # Copyright (c) 2022 Ximalaya Inc. (authors: Yuguang Yang)
#
# 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... | 10,976 | 35.959596 | 82 | py |
wenet | wenet-main/wenet/squeezeformer/encoder.py | # Copyright (c) 2022 Ximalaya Inc. (authors: Yuguang Yang)
#
# 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... | 21,841 | 45.080169 | 85 | py |
wenet | wenet-main/wenet/squeezeformer/convolution.py | # Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
# 2022 Ximalaya Inc. (authors: Yuguang Yang)
#
# 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.a... | 6,541 | 36.815029 | 83 | py |
wenet | wenet-main/wenet/squeezeformer/attention.py | # Copyright (c) 2019 Shigeki Karita
# 2020 Mobvoi Inc (Binbin Zhang)
# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
# 2022 Ximalaya Inc. (Yuguang Yang)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with ... | 10,291 | 45.152466 | 80 | py |
wenet | wenet-main/wenet/cif/predictor.py | # Copyright (c) 2023 ASLP@NWPU (authors: He Wang, Fan Yu)
#
# 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 la... | 10,356 | 42.154167 | 80 | py |
wenet | wenet-main/wenet/ssl/bestrq/bestqr_model.py | import math
from typing import Optional, Tuple
import torch
from wenet.ssl.bestrq.mask import compute_mask_indices_v2
from wenet.utils.mask import make_pad_mask
from wenet.transformer.attention import RelPositionMultiHeadedAttention
from wenet.transformer.encoder_layer import ConformerEncoderLayer
def quantize_vecto... | 10,662 | 36.946619 | 79 | py |
wenet | wenet-main/wenet/ssl/bestrq/mask.py | import torch
import numpy as np
def _sampler(pdf: torch.Tensor, num_samples: int,
device=torch.device('cpu')) -> torch.Tensor:
size = pdf.size()
z = -torch.log(torch.rand(size, device=device))
_, indices = torch.topk(pdf + z, num_samples)
return indices
def compute_mask_indices(
... | 5,780 | 35.13125 | 79 | py |
wenet | wenet-main/wenet/utils/checkpoint.py | # Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang)
#
# 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 l... | 3,737 | 33.934579 | 78 | py |
wenet | wenet-main/wenet/utils/ctc_util.py | # Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
#
# 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... | 3,177 | 36.833333 | 85 | py |
wenet | wenet-main/wenet/utils/executor.py | # Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
#
# 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... | 10,151 | 48.521951 | 150 | py |
wenet | wenet-main/wenet/utils/scheduler.py | # Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
# 2022 Ximalaya Inc (Yuguang Yang)
#
# 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.... | 23,578 | 34.297904 | 80 | py |
wenet | wenet-main/wenet/utils/common.py | # Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
#
# 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... | 7,880 | 29.546512 | 79 | py |
wenet | wenet-main/wenet/utils/init_model.py | # Copyright (c) 2022 Binbin Zhang (binbzha@qq.com)
#
# 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 ... | 6,002 | 45.898438 | 79 | py |
wenet | wenet-main/wenet/utils/mask.py | # Copyright (c) 2019 Shigeki Karita
# 2020 Mobvoi Inc (Binbin Zhang)
#
# 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 ... | 10,542 | 34.26087 | 78 | py |
wenet | wenet-main/wenet/transformer/embedding.py | # Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
#
# 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 appli... | 6,177 | 36.90184 | 79 | py |
wenet | wenet-main/wenet/transformer/encoder_layer.py | # Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
#
# 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.... | 9,594 | 39.146444 | 79 | py |
wenet | wenet-main/wenet/transformer/label_smoothing_loss.py | # Copyright (c) 2019 Shigeki Karita
# 2020 Mobvoi Inc (Binbin Zhang)
#
# 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 ... | 3,459 | 34.670103 | 77 | py |
wenet | wenet-main/wenet/transformer/positionwise_feed_forward.py | # Copyright (c) 2019 Shigeki Karita
# 2020 Mobvoi Inc (Binbin Zhang)
#
# 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 ... | 1,887 | 33.962963 | 74 | py |
wenet | wenet-main/wenet/transformer/ctc.py | # Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
#
# 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 appli... | 3,002 | 35.180723 | 77 | py |
wenet | wenet-main/wenet/transformer/subsampling.py | # Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
#
# 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... | 8,265 | 33.298755 | 74 | py |
wenet | wenet-main/wenet/transformer/encoder.py | # Copyright (c) 2021 Mobvoi Inc (Binbin Zhang, Di Wu)
# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
#
# 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.... | 20,080 | 43.525499 | 82 | py |
wenet | wenet-main/wenet/transformer/convolution.py | # Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
#
# 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 appli... | 5,185 | 34.765517 | 78 | py |
wenet | wenet-main/wenet/transformer/cmvn.py | # Copyright (c) 2020 Mobvoi Inc (Binbin Zhang)
#
# 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... | 1,487 | 30.659574 | 74 | py |
wenet | wenet-main/wenet/transformer/decoder.py | # Copyright (c) 2021 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
#
# 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 appli... | 12,583 | 41.37037 | 83 | py |
wenet | wenet-main/wenet/transformer/swish.py | # Copyright (c) 2020 Johns Hopkins University (Shinji Watanabe)
# 2020 Northwestern Polytechnical University (Pengcheng Guo)
# 2020 Mobvoi Inc (Binbin Zhang)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.... | 1,003 | 36.185185 | 74 | py |
wenet | wenet-main/wenet/transformer/attention.py | # Copyright (c) 2019 Shigeki Karita
# 2020 Mobvoi Inc (Binbin Zhang)
# 2022 Xingchen Song (sxc19@mails.tsinghua.edu.cn)
#
# 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 Licens... | 14,071 | 43.958466 | 80 | py |
wenet | wenet-main/wenet/transformer/decoder_layer.py | # Copyright (c) 2019 Shigeki Karita
# 2020 Mobvoi Inc (Binbin Zhang)
#
# 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 ... | 4,807 | 35.150376 | 79 | py |
wenet | wenet-main/wenet/transformer/asr_model.py | # Copyright (c) 2020 Mobvoi Inc. (authors: Binbin Zhang, Di Wu)
#
# 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 appli... | 41,398 | 42.079084 | 87 | py |
wenet | wenet-main/wenet/branchformer/encoder_layer.py | # Copyright (c) 2022 Yifan Peng (Carnegie Mellon University)
# 2023 Voicecomm Inc (Kai Li)
#
# 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/L... | 9,799 | 40.176471 | 86 | py |
wenet | wenet-main/wenet/branchformer/encoder.py | # Copyright (c) 2022 Yifan Peng (Carnegie Mellon University)
# 2023 Voicecomm Inc (Kai Li)
#
# 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/L... | 16,358 | 41.162371 | 83 | py |
wenet | wenet-main/wenet/branchformer/cgmlp.py | # Copyright (c) 2022 Yifan Peng (Carnegie Mellon University)
# 2023 Voicecomm Inc (Kai Li)
#
# 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/L... | 6,397 | 31.810256 | 82 | py |
simple-dftd3 | simple-dftd3-main/doc/conf.py | import os
import sys
sys.path.insert(0, os.path.join(os.path.abspath(".."), "python"))
import dftd3
project = "s-dftd3"
author = "Sebastian Ehlert"
copyright = f"2019-2022, {author}"
version = dftd3.__version__
release = version
extensions = [
"sphinx_design",
"sphinx_copybutton",
"sphinx.ext.autosumma... | 1,091 | 21.75 | 80 | py |
MUD-HoG_Federated_Learning | MUD-HoG_Federated_Learning-main/clients.py | from __future__ import print_function
from copy import deepcopy
from collections import deque
import torch
import torch.nn.functional as F
import logging
from utils import utils
class Client():
def __init__(self, cid, model, dataLoader, optimizer, criterion=F.nll_loss, device='cpu', inner_epochs=1):
sel... | 4,847 | 37.47619 | 110 | py |
MUD-HoG_Federated_Learning | MUD-HoG_Federated_Learning-main/_main.py | from __future__ import print_function
import torch
import torch.nn.functional as F
import torch.optim as optim
from tensorboardX import SummaryWriter
import os
import logging
from datetime import datetime
from clients_attackers import *
from server import Server
def main(args):
log_dir = f'logfiles/{args.AR}/{... | 10,913 | 42.309524 | 156 | py |
MUD-HoG_Federated_Learning | MUD-HoG_Federated_Learning-main/dataloader.py | from __future__ import print_function
import numpy as np
import torch
from collections import defaultdict
class Partition(torch.utils.data.Dataset):
""" Dataset-like object, but only access a subset of it. """
def __init__(self, data, index):
self.data = data
self.index = index
self.c... | 5,866 | 38.113333 | 161 | py |
MUD-HoG_Federated_Learning | MUD-HoG_Federated_Learning-main/server.py | from __future__ import print_function
from copy import deepcopy
import torch
import torch.nn.functional as F
import logging
from datetime import datetime
import numpy as np
from sklearn.cluster import KMeans, AgglomerativeClustering, DBSCAN
from collections import defaultdict, Counter
from utils import utils
from ut... | 32,220 | 43.01776 | 139 | py |
MUD-HoG_Federated_Learning | MUD-HoG_Federated_Learning-main/clients_attackers.py | from __future__ import print_function
import torch
import torch.nn.functional as F
import logging
from utils import utils
from utils.backdoor_semantic_utils import SemanticBackdoor_Utils
from utils.backdoor_utils import Backdoor_Utils
from clients import *
from utils.blur_images import GaussianSmoothing
class Unreli... | 12,133 | 45.312977 | 185 | py |
MUD-HoG_Federated_Learning | MUD-HoG_Federated_Learning-main/utils/blur_images.py | import math
import numbers
import torch
from torch import nn
from torch.nn import functional as F
# Thanks to: Adrian Sahlman
#https://discuss.pytorch.org/t/is-there-anyway-to-do-gaussian-filtering-for-an-image-2d-3d-in-pytorch/12351/9
class GaussianSmoothing(nn.Module):
"""
Apply gaussian smoothing on a
... | 2,656 | 34.426667 | 109 | py |
MUD-HoG_Federated_Learning | MUD-HoG_Federated_Learning-main/utils/backdoor_utils.py | from __future__ import print_function
import torch
'''
Modified upon
https://github.com/howardmumu/Attack-Resistant-Federated-Learning/blob/70db1edde5b4b9dfb75633ca5dd5a5a7303c1f4c/FedAvg/Update.py#L335
Reference:
Fu, Shuhao, et al. "Attack-Resistant Federated Learning with Residual-based Reweighting." arXiv prepri... | 4,603 | 38.689655 | 133 | py |
MUD-HoG_Federated_Learning | MUD-HoG_Federated_Learning-main/utils/utils.py | from copy import deepcopy
import torch
def getTrainableParameters(model) -> list:
'''
model: torch module
'''
trainableParam = []
for name, param in model.named_parameters():
if param.requires_grad:
trainableParam.append(name)
return trainableParam
def getFloatSubModules... | 4,409 | 28.013158 | 137 | py |
MUD-HoG_Federated_Learning | MUD-HoG_Federated_Learning-main/utils/backdoor_semantic_utils.py | from __future__ import print_function
import torch
from tasks import cifar
'''
Generate batches of backdoored cifar10 images
the list of images for semantic backdoor is retrieved from
https://github.com/ebagdasa/backdoor_federated_learning/blob/master/utils/params_runner.yaml
Reference:
Bagdasaryan, Eugene, et al... | 3,121 | 41.189189 | 145 | py |
MUD-HoG_Federated_Learning | MUD-HoG_Federated_Learning-main/utils/allocateGPU.py | import os
lim = 2
limit = str(lim)
os.environ["OMP_NUM_THREADS"] = limit # export OMP_NUM_THREADS=1
os.environ["OPENBLAS_NUM_THREADS"] = limit # export OPENBLAS_NUM_THREADS=1
os.environ["MKL_NUM_THREADS"] = limit # export MKL_NUM_THREADS=1
os.environ["VECLIB_MAXIMUM_THREADS"] = limit # export VECLIB_MAXIMUM_THREAD... | 2,515 | 34.43662 | 99 | py |
MUD-HoG_Federated_Learning | MUD-HoG_Federated_Learning-main/tasks/imdb_bert.py | from __future__ import print_function
import pickle
import torch
import torch.nn as nn
import torchtext
from torchtext.experimental.vocab import vocab_from_file_object
from transformers import MobileBertTokenizer, MobileBertModel
from dataloader import *
class BERTGRUSentiment(nn.Module):
'''
Model retriev... | 6,786 | 30.567442 | 128 | py |
MUD-HoG_Federated_Learning | MUD-HoG_Federated_Learning-main/tasks/imdb.py | from __future__ import print_function
import collections
import pickle
import torch
import torch.nn as nn
import torchtext
import torchtext.experimental
import torchtext.experimental.vectors
import torchtext.experimental.vocab
from torchtext.experimental.datasets.raw.text_classification import RawTextIterableDataset
... | 10,096 | 33.343537 | 112 | py |
MUD-HoG_Federated_Learning | MUD-HoG_Federated_Learning-main/tasks/fashion_mnist.py | from __future__ import print_function
import os,sys,inspect
current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parent_dir = os.path.dirname(current_dir)
sys.path.insert(0, parent_dir)
import pickle
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision... | 6,626 | 34.063492 | 112 | py |
MUD-HoG_Federated_Learning | MUD-HoG_Federated_Learning-main/tasks/cifar.py | from __future__ import print_function
import os,sys,inspect
current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parent_dir = os.path.dirname(current_dir)
sys.path.insert(0, parent_dir)
import pickle
import torch
import torch.nn as nn
from torchvision import datasets, transforms
fro... | 3,417 | 34.237113 | 119 | py |
MUD-HoG_Federated_Learning | MUD-HoG_Federated_Learning-main/tasks/mnist.py | from __future__ import print_function
import pickle
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
from dataloader import *
class Net(nn.Module):
'''
LeNet
retrieved from the pytorch tutorial
https://pytorch.org/tutorials/beginner/bl... | 4,218 | 31.960938 | 112 | py |
ddim | ddim-main/main.py | import argparse
import traceback
import shutil
import logging
import yaml
import sys
import os
import torch
import numpy as np
import torch.utils.tensorboard as tb
from runners.diffusion import Diffusion
torch.set_printoptions(sci_mode=False)
def parse_args_and_config():
parser = argparse.ArgumentParser(descrip... | 7,498 | 31.184549 | 96 | py |
ddim | ddim-main/functions/losses.py | import torch
def noise_estimation_loss(model,
x0: torch.Tensor,
t: torch.LongTensor,
e: torch.Tensor,
b: torch.Tensor, keepdim=False):
a = (1-b).cumprod(dim=0).index_select(0, t).view(-1, 1, 1, 1)
x = x0 * ... | 594 | 27.333333 | 67 | py |
ddim | ddim-main/functions/denoising.py | import torch
def compute_alpha(beta, t):
beta = torch.cat([torch.zeros(1).to(beta.device), beta], dim=0)
a = (1 - beta).cumprod(dim=0).index_select(0, t + 1).view(-1, 1, 1, 1)
return a
def generalized_steps(x, seq, model, b, **kwargs):
with torch.no_grad():
n = x.size(0)
seq_next = [... | 2,363 | 33.764706 | 93 | py |
ddim | ddim-main/functions/__init__.py | import torch.optim as optim
def get_optimizer(config, parameters):
if config.optim.optimizer == 'Adam':
return optim.Adam(parameters, lr=config.optim.lr, weight_decay=config.optim.weight_decay,
betas=(config.optim.beta1, 0.999), amsgrad=config.optim.amsgrad,
... | 727 | 44.5 | 100 | py |
ddim | ddim-main/models/diffusion.py | import math
import torch
import torch.nn as nn
def get_timestep_embedding(timesteps, embedding_dim):
"""
This matches the implementation in Denoising Diffusion Probabilistic Models:
From Fairseq.
Build sinusoidal embeddings.
This matches the implementation in tensor2tensor, but differs slightly
... | 12,846 | 36.564327 | 95 | py |
ddim | ddim-main/models/ema.py | import torch.nn as nn
class EMAHelper(object):
def __init__(self, mu=0.999):
self.mu = mu
self.shadow = {}
def register(self, module):
if isinstance(module, nn.DataParallel):
module = module.module
for name, param in module.named_parameters():
if param.... | 1,723 | 33.48 | 81 | py |
ddim | ddim-main/datasets/utils.py | import os
import os.path
import hashlib
import errno
from torch.utils.model_zoo import tqdm
def gen_bar_updater():
pbar = tqdm(total=None)
def bar_update(count, block_size, total_size):
if pbar.total is None and total_size:
pbar.total = total_size
progress_bytes = count * block_si... | 5,665 | 29.299465 | 109 | py |
ddim | ddim-main/datasets/lsun.py | from .vision import VisionDataset
from PIL import Image
import os
import os.path
import io
from collections.abc import Iterable
import pickle
from torchvision.datasets.utils import verify_str_arg, iterable_to_str
class LSUNClass(VisionDataset):
def __init__(self, root, transform=None, target_transform=None):
... | 5,503 | 30.272727 | 90 | py |
ddim | ddim-main/datasets/__init__.py | import os
import torch
import numbers
import torchvision.transforms as transforms
import torchvision.transforms.functional as F
from torchvision.datasets import CIFAR10
from datasets.celeba import CelebA
from datasets.ffhq import FFHQ
from datasets.lsun import LSUN
from torch.utils.data import Subset
import numpy as np... | 6,931 | 31.092593 | 83 | py |
ddim | ddim-main/datasets/vision.py | import os
import torch
import torch.utils.data as data
class VisionDataset(data.Dataset):
_repr_indent = 4
def __init__(self, root, transforms=None, transform=None, target_transform=None):
if isinstance(root, torch._six.string_classes):
root = os.path.expanduser(root)
self.root = ... | 3,266 | 37.435294 | 86 | py |
ddim | ddim-main/datasets/celeba.py | import torch
import os
import PIL
from .vision import VisionDataset
from .utils import download_file_from_google_drive, check_integrity
class CelebA(VisionDataset):
"""`Large-scale CelebFaces Attributes (CelebA) Dataset <http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html>`_ Dataset.
Args:
root (string)... | 7,794 | 46.530488 | 120 | py |
ddim | ddim-main/datasets/ffhq.py | from io import BytesIO
import lmdb
from PIL import Image
from torch.utils.data import Dataset
class FFHQ(Dataset):
def __init__(self, path, transform, resolution=8):
self.env = lmdb.open(
path,
max_readers=32,
readonly=True,
lock=False,
readahea... | 1,055 | 24.756098 | 80 | py |
ddim | ddim-main/runners/diffusion.py | import os
import logging
import time
import glob
import numpy as np
import tqdm
import torch
import torch.utils.data as data
from models.diffusion import Model
from models.ema import EMAHelper
from functions import get_optimizer
from functions.losses import loss_registry
from datasets import get_dataset, data_transfo... | 13,294 | 33.532468 | 98 | py |
cunet | cunet-main/test.py | import argparse
import os
import torch
import math
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from torch.optim import lr_scheduler
import time
import sys
from utils import Logger, display_config, set_rand... | 3,421 | 38.333333 | 124 | py |
cunet | cunet-main/latency_vox.py | import torch
from data.face_scape_voxel_dataset import FaceScapeVoxelDataset
from torchsparse.utils.collate import sparse_collate
from sampling.voxelize import Voxelizer
from models.cunet import CUNet
from torchsparse import SparseTensor
from time import time
from traditional.devoxelize import DevoxelizeInterpolation
... | 6,293 | 35.593023 | 121 | py |
cunet | cunet-main/patch.py | import numpy
import torch
import numpy as np
from sklearn.neighbors import NearestNeighbors
import torchsparse.nn.functional as F
from tqdm import tqdm
from sklearn.cluster import KMeans, MiniBatchKMeans
from data.quantize import sparse_quantize
def knn_index(source, target, k=1):
nbrs = NearestNeighbors(n_neighbo... | 9,305 | 41.3 | 135 | py |
cunet | cunet-main/latency_traditional.py | import torch
from data.face_scape_voxel_dataset import FaceScapeVoxelDataset
from sampling.voxelize import Voxelizer
from torchsparse.utils.collate import sparse_collate
from time import time
from traditional.knn import KNNVoxelInterpolation
from traditional.fractional import FractionalSRInterpolation
import numpy as n... | 2,953 | 35.925 | 97 | py |
cunet | cunet-main/utils.py | import numpy as np
import open3d as o3d
import torch
import random
import os
import sys
import errno
import os.path as osp
def normalize(points, scale=1.0):
centroid = np.mean(points, axis=0)
points_new = points - centroid
m = np.max(np.sqrt(np.sum(points_new ** 2, axis=1)))
points_new = scale * points... | 3,238 | 25.120968 | 91 | py |
cunet | cunet-main/mpeg8i.py | import numpy as np
import torch.cuda
from utils import read_point_cloud_ply, draw_point_cloud, set_random_seed, read_mesh_ply, draw_mesh, normalize
from traditional.knn import KNNVoxelInterpolation
from metric.psnr import compute_psnr_numpy_rgb
from data.quantize import sparse_quantize
from sampling.voxelize import vox... | 6,298 | 44.644928 | 130 | py |
cunet | cunet-main/baseline.py | import numpy as np
from data.face_scape_voxel_dataset import FaceScapeVoxelDataset
from utils import set_random_seed
from metric.psnr import compute_psnr_numpy_rgb
from sampling.voxelize import voxelize_avg_numpy, Voxelizer
from tqdm import tqdm
from traditional.devoxelize import devoxelize_interp
from traditional.knn ... | 2,299 | 32.823529 | 107 | py |
cunet | cunet-main/train.py | import argparse
import os
import torch
import math
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from torch.optim import lr_scheduler
import time
import sys
from utils import Logger, display_config, set_rand... | 4,966 | 46.304762 | 124 | py |
cunet | cunet-main/deep_interp.py | import copy
import torch
import numpy as np
from models.cunet import CUNet
from torchsparse import SparseTensor
class DeepInterpolation:
def __init__(self, num_channel, num_block, voxel_size, model_path, use_gpu=True):
super(DeepInterpolation, self).__init__()
self.model = CUNet(voxel_size, num_cha... | 1,493 | 33.744186 | 85 | py |
cunet | cunet-main/metric/psnr.py | import numpy as np
import torch
def compute_psnr_numpy_rgb(pred, gt):
'''
:param pred: (N, 3)
:param gt: (N, 3)
:return: float
'''
pred = pred.astype(float) * 255. + 1e-6 # for numerical stability
gt = gt.astype(float) * 255.
mse = np.mean((pred - gt) ** 2)
psnr = 10 * np.log10(25... | 688 | 21.225806 | 70 | py |
cunet | cunet-main/models/cunet.py | import sys
sys.path.append("..")
from typing import List, Tuple, Union
from torchsparse import SparseTensor
from torchsparse import nn as spnn
from torchsparse.utils.collate import sparse_collate_fn
from torchsparse.utils.quantize import sparse_quantize
import torchsparse.nn.functional as F
from utils import read_point... | 5,487 | 35.832215 | 112 | py |
cunet | cunet-main/models/knn.py | from torch import nn
import torch
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 = (xn ... | 2,031 | 33.440678 | 114 | py |
cunet | cunet-main/sampling/random_sample.py | import numpy as np
import random
import torch
# randomly drop colors
# torch
class RandomColorSampler:
def __init__(self, sample_rate):
super(RandomColorSampler, self).__init__()
self.sample_rate = sample_rate
def __call__(self, points, colors):
'''
:param points: (B, N, 3)
... | 1,838 | 27.734375 | 74 | py |
cunet | cunet-main/sampling/voxelize.py | import sys
sys.path.append("..")
import numpy as np
import torch
import torchsparse.nn.functional as F
from torchsparse import SparseTensor
class Voxelizer:
def __init__(self, voxel_size, avg_color=True):
super(Voxelizer, self).__init__()
self.voxel_size = voxel_size
self.avg_color = avg_c... | 3,788 | 40.184783 | 102 | py |
cunet | cunet-main/traditional/devoxelize.py | import torch
import torchsparse.nn.functional as F
def devoxelize_interp(points_lr, colors_lr, points_hr, idx_lr2hr):
result = colors_lr[idx_lr2hr, :]
return result
# devoxlization without pre-computed LR-to-HR mapping
class DevoxelizeInterpolation:
def __init__(self, voxel_size):
self.voxel_size... | 935 | 39.695652 | 125 | py |
cunet | cunet-main/traditional/knn.py | import numpy as np
from sklearn.neighbors import NearestNeighbors
# from knn_cuda import KNN
import torch
def knn_index(source, target, k=1):
nbrs = NearestNeighbors(n_neighbors=k, algorithm='auto').fit(source)
distances, indices = nbrs.kneighbors(target)
return indices
# nearest neighbor interpolation o... | 1,388 | 27.346939 | 73 | py |
cunet | cunet-main/runner/voxel.py | from tqdm import tqdm
import torch
import os
from torch import optim
from torch.optim import lr_scheduler
import sys
from metric.psnr import compute_psnr_torch_rgb
from torch.cuda import amp
def run_voxel(args, train_loader, valid_loader, test_loader, sampler, model):
criterion = torch.nn.MSELoss()
criterion ... | 2,899 | 39.84507 | 121 | py |
cunet | cunet-main/data/face_scape_voxel_dataset.py | import sys
sys.path.append("..")
import json
from torch.utils.data import Dataset
import os
from torchvision import transforms
import torch
import torch.nn.functional as F
import numpy as np
from utils import read_point_cloud_ply, normalize
from data.quantize import sparse_quantize
from torchsparse import SparseTensor
... | 3,782 | 36.088235 | 114 | py |
interpretable_nn_attribution | interpretable_nn_attribution-master/run_spotify.py | """
Code for the sequential skip prediction task
using the preprocessed spotify parquet files.
"""
import gc
import time
import sys
import glob
import numpy as np
import pandas as pd
sys.path.append('models')
import tensorflow as tf
from tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, TensorBoar... | 13,383 | 30.125581 | 122 | py |
interpretable_nn_attribution | interpretable_nn_attribution-master/run_movielens.py | """
Code for the movielens experiments.
"""
import argparse
import os
import time
import random
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Lambda, Concatenate, Reshape, Embedding, Flatten
from tensorflow.keras.optimizers impor... | 14,606 | 37.138381 | 151 | py |
interpretable_nn_attribution | interpretable_nn_attribution-master/models/interpretable_utils_list.py | '''
Interpretable model utilities.
Those functions are used to define interpretable models using lists of Layers.
The other way to do it is by using mask_constraints, as done
in interpretable_utils_matrix.py.
'''
import tensorflow as tf
from tensorflow.keras.layers import Concatenate, Lambda
# -- layer wrappers
de... | 6,941 | 29.31441 | 110 | py |
interpretable_nn_attribution | interpretable_nn_attribution-master/models/multi_head_attention.py | '''
Multi-head attention layer + other utils class for the transformer model.
'''
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Dense, Lambda, Reshape, Layer
from tensorflow.keras.constraints import Constraint
from tensorflow.keras import activations
class LayerNormalization(Layer):
... | 6,876 | 33.908629 | 96 | py |
interpretable_nn_attribution | interpretable_nn_attribution-master/models/multilayered_nn.py | import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Lambda
from tensorflow.keras.optimizers import Adam
from interpretable_utils_list import *
from interpretable_utils_matrix import *
class Attr_Binary_FF_list(Model):
"""
-- Attribute-interpreta... | 5,284 | 34.709459 | 81 | py |
interpretable_nn_attribution | interpretable_nn_attribution-master/models/spotify.py | '''
Interpretable deep neural networks using boosted jointed restricted models
'''
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense, Dropout, Lambda, Concatenate, Flatten, Activation, BatchNormalization, LeakyReLU, Add, GRU, Reshape, Bidirectional, Embe... | 9,012 | 37.517094 | 175 | py |
TiSASRec | TiSASRec-master/modules.py | # -*- coding: utf-8 -*-
#/usr/bin/python2
'''
June 2017 by kyubyong park.
kbpark.linguist@gmail.com.
https://www.github.com/kyubyong/transformer
'''
from __future__ import print_function
import tensorflow as tf
import numpy as np
def positional_encoding(dim, sentence_length, dtype=tf.float32):
encoded_vec = np... | 10,741 | 37.779783 | 163 | py |
fashion-compatibility | fashion-compatibility-master/main.py | from __future__ import print_function
import argparse
import os
import sys
import shutil
import json
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import transforms
from torch.autograd import Variable
import torch.backends.cudnn as c... | 13,249 | 41.332268 | 130 | py |
fashion-compatibility | fashion-compatibility-master/Resnet_18.py | import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
__all__ = ['ResNet', 'resnet18']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
}
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes... | 3,796 | 29.620968 | 76 | py |
fashion-compatibility | fashion-compatibility-master/tripletnet.py | import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
def make_fc_1d(f_in, f_out):
return nn.Sequential(nn.Linear(f_in, f_out),
nn.BatchNorm1d(f_out,eps=0.001,momentum=0.01),
nn.ReLU(inplace=True))... | 6,822 | 43.594771 | 123 | py |
fashion-compatibility | fashion-compatibility-master/type_specific_network.py | import torch
import torch.nn as nn
import numpy as np
from torch.autograd import Variable
class ListModule(nn.Module):
def __init__(self, *args):
super(ListModule, self).__init__()
idx = 0
for module in args:
self.add_module(str(idx), module)
idx += 1
def __geti... | 6,073 | 40.60274 | 104 | py |
fashion-compatibility | fashion-compatibility-master/polyvore_outfits.py | from PIL import Image
import os
import os.path
import torch.utils.data
import torchvision.transforms as transforms
import numpy as np
import json
import torch
import pickle
import h5py
from sklearn.metrics import roc_auc_score
from torch.autograd import Variable
def default_image_loader(path):
return Image.open(pa... | 13,907 | 37.740947 | 109 | py |
DraftRec | DraftRec-master/src/common/logger.py | import wandb
import torch
import os
import json
class LoggerService(object):
def __init__(self, args):
self.train_logger = WandbLogger(prefix='train')
self.val_logger = WandbLogger(prefix='val')
self.test_logger = WandbLogger(prefix='test')
project_name = args.wandb_projec... | 3,768 | 29.152 | 107 | py |
DraftRec | DraftRec-master/src/common/train_utils.py | import torch
import torch.nn as nn
import numpy as np
import math
def init_transformer_weights(module, init_range=0.0625):
""" Initialize the weights """
if isinstance(module, (nn.Embedding, nn.Linear)):
# Slightly different from the TF version which uses truncated_normal for initialization
# ... | 561 | 32.058824 | 95 | py |
DraftRec | DraftRec-master/src/common/initialization.py | import random
import torch
import torch.backends.cudnn as cudnn
import numpy as np
import torch.nn as nn
def fix_random_seed(random_seed):
random.seed(random_seed)
torch.manual_seed(random_seed)
torch.cuda.manual_seed_all(random_seed)
np.random.seed(random_seed)
cudnn.deterministic = True
... | 829 | 28.642857 | 64 | py |
DraftRec | DraftRec-master/src/models/base.py | import torch.nn as nn
from abc import *
class BaseModel(nn.Module, metaclass=ABCMeta):
def __init__(self, args):
super().__init__()
self.args = args
@classmethod
@abstractmethod
def code(cls):
pass | 240 | 17.538462 | 46 | py |
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