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 |
|---|---|---|---|---|---|---|
politihop | politihop-master/Transformer-XH/transformer-xh/model/model.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
from dgl import DGLGraph
import dgl.function as fn
from pytorch_transformers import *
from pytorch_transformers.modeling_bert import BertModel, BertEncoder, BertPreTra... | 8,979 | 35.803279 | 179 | py |
politihop | politihop-master/Transformer-XH/data/base.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from torch.utils.data import Dataset, DataLoader
from .utils import load_data
class TransformerXHDataset(Dataset):
def __init__(self, filename, config_model, isTrain=False, bert_tokenizer=None):
self.config_model = config_model
... | 574 | 26.380952 | 83 | py |
politihop | politihop-master/Transformer-XH/data/hotpotqa.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# specific stuff for hotpot qa
import torch
from torch.utils.data import Dataset, DataLoader
import dgl
from dgl import DGLGraph
import dgl.function as fn
from .base import TransformerXHDataset
from .utils import truncate_input_sequence
def bat... | 4,691 | 31.811189 | 207 | py |
politihop | politihop-master/Transformer-XH/data/fever.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
# the specific stuff of fever
import torch
from torch.utils.data import Dataset, DataLoader
import dgl
from dgl import DGLGraph
import dgl.function as fn
from .base import TransformerXHDataset
from .utils import truncate_input_sequence
def ba... | 3,794 | 28.648438 | 143 | py |
causalpodnn | causalpodnn-main/causalPODNN.py | import warnings
warnings.filterwarnings("ignore")
import os
import psutil
import numpy as np
import tensorflow as tf
tf.keras.backend.set_floatx('float32')
from tensorflow.keras import layers, Model
import matplotlib.pyplot as plt
import cv2
import podnn_tensorflow_train
tf.get_logger().setLevel('INFO')
from scipy.spat... | 27,982 | 35.014157 | 153 | py |
causalpodnn | causalpodnn-main/podnn_tensorflow_train.py | """
This file provides five different classes for creating PODNN architectures in Tensorflow.
The five modules are as follows:
- InputLayer: Prepares data in parallel form to be consumable by the upcoming layers
- ParallelLayer: creates a parallel sub-layer formed from unit it receives.
- OrthogonalLayer1D: makes... | 12,836 | 32.256477 | 122 | py |
PaddleSlim-develop | PaddleSlim-develop/demo/models/pvanet.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
from paddle.nn.initializer import KaimingUniform
from collections import namedtuple
BLOCK_TYPE_MCRELU = 'BLOCK_TYPE_MCRELU'
BLOCK_TYPE_INCEP = 'BLOCK_TYPE_INCEP'
BlockConfig = namedtuple('BlockCon... | 17,865 | 35.165992 | 83 | py |
PaddleSlim-develop | PaddleSlim-develop/docs/en/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 5,438 | 30.622093 | 79 | py |
PaddleSlim-develop | PaddleSlim-develop/docs/zh_cn/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 5,429 | 30.754386 | 79 | py |
PaddleSlim-develop | PaddleSlim-develop/example/post_training_quantization/pytorch_yolo_series/fine_tune.py | # Copyright (c) 2022 PaddlePaddle 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 appli... | 3,994 | 30.96 | 77 | py |
PaddleSlim-develop | PaddleSlim-develop/example/post_training_quantization/pytorch_yolo_series/post_quant.py | # Copyright (c) 2022 PaddlePaddle 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 appli... | 3,089 | 30.212121 | 80 | py |
NIPS2019DeepGamblers | NIPS2019DeepGamblers-master/main.py | from __future__ import print_function
import argparse
import os
import shutil
import time
import random
import math
import numpy
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torchvision.transforms as... | 21,901 | 44.916143 | 235 | py |
NIPS2019DeepGamblers | NIPS2019DeepGamblers-master/dataset_utils.py | import os
import torch
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms.functional as F
from torchvision import transforms, utils
from PIL import Image
class resized_dataset(Dataset):
def __init__(self, dataset, transform=None, start=None, end=None, resize=None):
self.data=[]
... | 1,020 | 33.033333 | 105 | py |
NIPS2019DeepGamblers | NIPS2019DeepGamblers-master/models/cifar/preresnet.py | from __future__ import absolute_import
'''Resnet for cifar dataset.
Ported form
https://github.com/facebook/fb.resnet.torch
and
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
(c) YANG, Wei
'''
import torch.nn as nn
import math
__all__ = ['preresnet']
def conv3x3(in_planes, out_planes, st... | 4,624 | 28.08805 | 77 | py |
NIPS2019DeepGamblers | NIPS2019DeepGamblers-master/models/cifar/resnet.py | from __future__ import absolute_import
'''Resnet for cifar dataset.
Ported form
https://github.com/facebook/fb.resnet.torch
and
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
(c) YANG, Wei
'''
import torch.nn as nn
import math
__all__ = ['resnet']
def conv3x3(in_planes, out_planes, strid... | 4,662 | 28.14375 | 77 | py |
NIPS2019DeepGamblers | NIPS2019DeepGamblers-master/models/cifar/vgg.py | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
import math
__all__ = [
'VGG', 'vgg16', 'vgg16_bn'
]
class VGG(nn.Module):
def __init__(self, features, num_classes=1000, input_size = 32):
super(VGG, self).__init__()
self.features = features
if input_size == 32:
... | 2,762 | 32.289157 | 133 | py |
NIPS2019DeepGamblers | NIPS2019DeepGamblers-master/models/cifar/densenet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
__all__ = ['densenet']
from torch.autograd import Variable
class Bottleneck(nn.Module):
def __init__(self, inplanes, expansion=4, growthRate=12, dropRate=0):
super(Bottleneck, self).__init__()
planes = expansion * gr... | 4,724 | 30.711409 | 99 | py |
NIPS2019DeepGamblers | NIPS2019DeepGamblers-master/models/cifar/resnext.py | from __future__ import division
"""
Creates a ResNeXt Model as defined in:
Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2016).
Aggregated residual transformations for deep neural networks.
arXiv preprint arXiv:1611.05431.
import from https://github.com/prlz77/ResNeXt.pytorch/blob/master/models/model.py
"""
i... | 5,597 | 43.428571 | 144 | py |
NIPS2019DeepGamblers | NIPS2019DeepGamblers-master/models/cifar/__init__.py | from __future__ import absolute_import
"""The models subpackage contains definitions for the following model for CIFAR10/CIFAR100
architectures:
- `AlexNet`_
- `VGG`_
- `ResNet`_
- `SqueezeNet`_
- `DenseNet`_
You can construct a model with random weights by calling its constructor:
.. code:: python
import... | 2,250 | 30.704225 | 90 | py |
NIPS2019DeepGamblers | NIPS2019DeepGamblers-master/models/cifar/alexnet.py | '''AlexNet for CIFAR10. FC layers are removed. Paddings are adjusted.
Without BN, the start learning rate should be 0.01
(c) YANG, Wei
'''
import torch.nn as nn
__all__ = ['alexnet']
class AlexNet(nn.Module):
def __init__(self, num_classes=10):
super(AlexNet, self).__init__()
self.features = n... | 1,359 | 29.222222 | 69 | py |
NIPS2019DeepGamblers | NIPS2019DeepGamblers-master/models/cifar/wrn.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
__all__ = ['wrn']
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(inplac... | 3,902 | 40.521277 | 116 | py |
NIPS2019DeepGamblers | NIPS2019DeepGamblers-master/utils/misc.py | '''Some helper functions for PyTorch, including:
- get_mean_and_std: calculate the mean and std value of dataset.
- msr_init: net parameter initialization.
- progress_bar: progress bar mimic xlua.progress.
'''
import errno
import os
import sys
import time
import math
import torch.nn as nn
import torch.nn.i... | 2,206 | 28.039474 | 110 | py |
NIPS2019DeepGamblers | NIPS2019DeepGamblers-master/utils/logger.py | # A simple torch style logger
# (C) Wei YANG 2017
from __future__ import absolute_import
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import os
import sys
import numpy as np
__all__ = ['Logger', 'LoggerMonitor', 'savefig', 'closefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == ... | 4,482 | 32.207407 | 100 | py |
NIPS2019DeepGamblers | NIPS2019DeepGamblers-master/utils/visualize.py | import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import numpy as np
from .misc import *
__all__ = ['make_image', 'show_batch', 'show_mask', 'show_mask_single']
# functions to show an image
def make_image(img, mean=(0,0,0), std=(1,1,1)... | 3,795 | 33.509091 | 95 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/nn/mlp.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import torch.nn as nn
from code.nn.utils import ReactionDiffusionParametersScaler
act_dict = {"ReLU": nn.ReLU(),
"Softplus": nn.Softplus(),
"SELU": nn.SELU(),
"ReactionDiffusionPar... | 1,409 | 30.333333 | 85 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/nn/utils.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import torch
import torch.nn as nn
class Permute(nn.Module):
def __init__(self, order):
super().__init__()
self.order = order
def forward(self, x):
return x.permute(self.order)
cla... | 788 | 25.3 | 65 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/nn/unet.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import Dataset, DataLoader
import torchvision
class Block(nn.M... | 7,275 | 35.38 | 112 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/simulators/RLCCircuit.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import torch
import torch.nn as nn
from torchdyn.core import NeuralODE
from torchdyn import *
from code.simulators.GenericSimulator import PhysicalModel
import math
class RLCODE(PhysicalModel):
def __init__(self... | 4,665 | 46.612245 | 126 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/simulators/DampedPendulum.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import torch
import torch.nn as nn
from torchdyn.core import NeuralODE
from torchdyn import *
from code.simulators.GenericSimulator import PhysicalModel
import math
class DampedPendulumODE(PhysicalModel):
def __... | 5,636 | 43.039063 | 135 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/simulators/DoublePendulum.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import torch
import torch.nn as nn
from torchdyn.core import NeuralODE
from torchdyn import *
from code.simulators.GenericSimulator import PhysicalModel
import math
class DoublePendulumODE(PhysicalModel):
def __... | 4,266 | 40.833333 | 119 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/simulators/GenericSimulator.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import torch
import torch.nn as nn
class PhysicalModel(nn.Module):
def __init__(self, param_values, trainable_param):
super(PhysicalModel, self).__init__()
self._nb_parameters = len(trainable_par... | 1,876 | 29.770492 | 115 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/simulators/NoSimulator.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import torch
import torch.nn as nn
from torchdyn.core import NeuralODE
from torchdyn import *
from code.simulators.GenericSimulator import PhysicalModel
import math
class NoSimulator(PhysicalModel):
def __init__... | 643 | 23.769231 | 64 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/simulators/ReactionDiffusion.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import torch
import torch.nn as nn
from torchdyn.core import NeuralODE
from torchdyn import *
from code.simulators.GenericSimulator import PhysicalModel
import math
import torch.nn.functional as F
# This code is stro... | 5,026 | 39.869919 | 115 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/scripts/run_experiments_robustness.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import shutil
import torch
from matplotlib import pyplot as plt
from torch import nn
from code.hybrid_models.APHYNITY import APHYNITYAutoencoderDoublePendulum
from code.simulators import PhysicalModel
from code.simu... | 15,605 | 52.262799 | 132 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/scripts/run_experiments.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import shutil
import torch
from matplotlib import pyplot as plt
from torch import nn
from code.hybrid_models.APHYNITY import APHYNITYAutoencoderDoublePendulum
from code.simulators import PhysicalModel
from code.simu... | 15,872 | 53.546392 | 125 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/hybrid_models/APHYNITY.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import math
import pickle
import torch.nn as nn
from code.simulators import PhysicalModel, NoSimulator
import torch
from code.nn import MLP, ConditionalUNet, Permute, act_dict
from torchdiffeq import odeint
from code... | 26,155 | 44.887719 | 146 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/hybrid_models/HVAE.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import math
import torch.nn as nn
from matplotlib import pyplot as plt
from code.simulators import PhysicalModel, NoSimulator
import torch
from code.nn import MLP, act_dict, ConditionalUNet, ConditionalUNetReactionD... | 39,125 | 47.007362 | 146 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/hybrid_models/HybridAutoencoder.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
from abc import abstractmethod
import torch.nn as nn
import torch
class HybridAutoencoder(nn.Module):
def __init__(self):
super(HybridAutoencoder, self).__init__()
@abstractmethod
def forward(se... | 884 | 26.65625 | 96 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/utils/double_pendulum.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import math
import cv2
import numpy as np
import pandas as pd
import torch
import torch.utils.data as data_utils
def xy_to_theta(dx, dy):
theta = np.arctan2(dy, dx) + math.pi / 2
# cond_1 = ((dy > 0) & (the... | 2,883 | 33.333333 | 116 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/utils/loaders.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import torch.utils.data as data_utils
import pickle
def load_data(data_path, device):
with open(r"%s/train.pkl" % data_path, "rb") as output_file:
t_train, x_train, true_param_train = pickle.load(output_... | 1,102 | 51.52381 | 154 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/utils/utils.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import torch
from matplotlib import pyplot as plt
from torch import nn
from code.hybrid_models import HybridAutoencoder
from code.hybrid_models.APHYNITY import APHYNITYAutoencoderDoublePendulum
from code.hybrid_model... | 5,484 | 45.483051 | 116 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/utils/plotter.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import matplotlib.pyplot as plt
from textwrap import wrap
import torch
def plot_curves(t, x_pred, x_obs, title, labels, save_name):
fig = plt.figure(figsize=(24, 12))
fig.suptitle("\n".join(wrap(title, 150)... | 7,184 | 45.655844 | 120 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/data/RLC/GenerateDataset.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
from code.simulators import RLCCircuit
import torch
from tqdm import tqdm
import os
import pickle
def gen_data(n=500, shifted="False"):
if shifted == "small_all":
distributions = {
"R": lambd... | 6,038 | 47.312 | 79 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/data/ReactionDiffusion/GenerateDataset.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
from code.simulators import ReactionDiffusion
import torch
from tqdm import tqdm
import os
import pickle
def gen_data(n=500, shifted=""):
if shifted == "small_all":
distributions = {
"a": lam... | 3,946 | 40.114583 | 79 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/data/DampedPendulum/GenerateDataset.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
from code.simulators import DampedPendulum
import torch
from tqdm import tqdm
import os
import pickle
#import turibolt as bolt
import argparse
def gen_data(n=500, shifted=""):
if shifted == "small_all":
... | 4,462 | 39.572727 | 113 | py |
ml-robust-expert-augmentations | ml-robust-expert-augmentations-main/code/data/DoublePendulum/GenerateDataset.py | # For licensing see accompanying LICENSE file.
# Copyright (C) 2023 Apple Inc. All Rights Reserved.
import math
import pandas as pd
import code.simulators.DoublePendulum as DP
import torch
from torchdiffeq import odeint
from tqdm import tqdm
import os
import pickle
import argparse
from code.utils.double_pendulum ... | 6,221 | 47.609375 | 116 | py |
moment-neural-network | moment-neural-network-main/examples/mnist/mnist.py | import torch
from mnn import snn, utils
class MnistTrainFuncs(utils.training_tools.TrainProcessCollections):
"""
The general pipeline to construct MNN for training.
"""
def set_random_seed(self, seed):
"""
This function can fix all random seed (numpy, pytorch) for better reproducibility... | 12,025 | 49.529412 | 229 | py |
moment-neural-network | moment-neural-network-main/mnn/models/mlp.py | # -*- coding: utf-8 -*-
from typing import Tuple, Optional
import torch
from torch import Tensor
from .. import mnn_core
def _general_forward(inputs, feature_extractor: torch.nn.ModuleList, decoder: Optional[torch.nn.Module] = None):
u, cov = inputs
for module in feature_extractor:
u, cov = module(u,... | 5,606 | 41.157895 | 156 | py |
moment-neural-network | moment-neural-network-main/mnn/mnn_core/mnn_utils.py | # -*- coding: utf-8 -*-
import numpy as np
from . import fast_dawson
import torch
from torch import Tensor
from typing import Tuple
class Param_Container:
"""
args:
_vol_rest: the rest voltage of a neuron
_vol_th: the fire threshold of a neuron
_t_ref: the refractory time of a neuoron ... | 16,921 | 37.546697 | 119 | py |
moment-neural-network | moment-neural-network-main/mnn/mnn_core/__init__.py | # -*- coding: utf-8 -*-
from . import nn
from .mnn_pytorch import mnn_activate_trio, mnn_activate_no_rho, get_core_attr, set_core_attr, reset_core_attr
from .mnn_utils import Mnn_Core_Func | 188 | 46.25 | 110 | py |
moment-neural-network | moment-neural-network-main/mnn/mnn_core/mnn_pytorch.py | # -*- coding: utf-8 -*-
"""
Created on Sat Oct 3 14:49:43 2020
Copyright 2020 Zhichao Zhu, ISTBI, Fudan University China
"""
from typing import Any
import torch
from .mnn_utils import *
mnn_core_func = Mnn_Core_Func()
def _batch_detach(*args):
temp = list()
for i in args:
temp.append(i.cpu().detach... | 4,871 | 38.934426 | 160 | py |
moment-neural-network | moment-neural-network-main/mnn/mnn_core/nn/batch_norm.py | # -*- coding: utf-8 -*-
from typing import Tuple
import torch
from torch import Tensor
from torch.nn import init
from torch.nn.parameter import Parameter
from . import functional
class BatchNorm1dDuo(torch.nn.Module):
__constant__ = ['num_features']
num_features: int
def __init__(self, num_features: int,... | 3,256 | 36.436782 | 113 | py |
moment-neural-network | moment-neural-network-main/mnn/mnn_core/nn/activation.py | # -*- coding: utf-8 -*-
import torch
from torch import Tensor
from typing import Tuple
from . import functional
from ..mnn_pytorch import mnn_activate_trio, mnn_activate_no_rho
class OriginMnnActivation(torch.nn.Module):
def forward(self, *args) -> Tuple[Tensor, Tensor]:
u, cov = functional.parse_input(ar... | 686 | 31.714286 | 64 | py |
moment-neural-network | moment-neural-network-main/mnn/mnn_core/nn/functional.py | import math
import torch
from torch import Tensor
from typing import Tuple, Any, Optional
import torch.nn.functional as F
class ModifyDiagToOne(torch.autograd.Function):
@staticmethod
def forward(ctx: Any, rho: Tensor) -> Tensor:
torch.diagonal(rho, dim1=-1, dim2=-2).data.fill_(1.0)
return rho... | 6,083 | 36.097561 | 131 | py |
moment-neural-network | moment-neural-network-main/mnn/mnn_core/nn/custom_batch_norm.py | # -*- coding: utf-8 -*-
import torch
from torch.nn.parameter import Parameter
from torch import Tensor
from typing import Optional, Tuple
from . import functional
def _compute_weight(gamma: Optional[Tensor], var, eps=1e-5):
if gamma is None:
return 1 / torch.sqrt(var + eps)
else:
return gamma ... | 4,938 | 41.213675 | 136 | py |
moment-neural-network | moment-neural-network-main/mnn/mnn_core/nn/linear.py | # -*- coding: utf-8 -*-
import torch
import math
from typing import Tuple, Optional
from torch.nn.parameter import Parameter
from torch.nn import init
import numpy as np
import torch.nn.functional as F
from torch import Tensor
from . import functional
class LinearDuo(torch.nn.Module):
__constant__ = ['in_features... | 5,152 | 37.17037 | 134 | py |
moment-neural-network | moment-neural-network-main/mnn/mnn_core/nn/mconv2d.py | import torch
class MomentConv2d(torch.nn.Module):
__constant__ = ['in_channels', 'out_channels','kernel_size', 'stride']
in_channels: int
out_channels: int
kernel_size: int
stride: int
def __init__(self, in_channels: int, out_channels: int, kernel_size: int, stride: int) -> None:
super... | 3,520 | 38.561798 | 120 | py |
moment-neural-network | moment-neural-network-main/mnn/mnn_core/nn/criterion.py | import torch
from torch import Tensor
from torch.nn import functional as F
from typing import Tuple
from . import functional
class LabelSmoothing(torch.nn.Module):
def __init__(self, num_class: int = 10, alpha: float = 0.1) -> None:
super(LabelSmoothing, self).__init__()
assert 0. <= alpha < 1.
... | 8,229 | 42.544974 | 150 | py |
moment-neural-network | moment-neural-network-main/mnn/mnn_core/nn/ensemble.py | # -*- coding: utf-8 -*-
from typing import Tuple, Optional
import torch
from torch import Tensor
from .activation import OriginMnnActivation
from . import linear, batch_norm, custom_batch_norm
class EnsembleLinearDuo(torch.nn.Module):
def __init__(self, in_features: int, out_features: int, ln_bias_mean: bool = ... | 2,733 | 45.338983 | 130 | py |
moment-neural-network | moment-neural-network-main/mnn/snn/snn2loihi.py | import os
import torch
import nxsdk.api.n2a as nx
from nxsdk.api.enums.api_enums import ProbeParameter
from nxsdk.graph.monitor.probes import PerformanceProbeCondition
from n2_apps.modules.slayer import src as nxSlayer
from n2_apps.modules.slayer.src.slayer2loihi import Slayer2Loihi
import seaborn as sns
from scipy imp... | 35,284 | 45.305774 | 177 | py |
moment-neural-network | moment-neural-network-main/mnn/snn/functional.py | # -*- coding: utf-8 -*-
import torch
import numpy as np
from .. import mnn_core
from torch import Tensor
from .. import utils
from . import base, mnn2snn
def modify_value_by_condition(data, condition):
u, cov = data
if 'mask_mean' in condition:
u = torch.zeros_like(u)
elif 'shuffle_cov' in conditio... | 10,165 | 40.663934 | 141 | py |
moment-neural-network | moment-neural-network-main/mnn/snn/mnn2snn.py | # -*- coding: utf-8 -*-
import types
import torch
from torch import Tensor
from torch.nn import functional as F
from collections import defaultdict
from .. import mnn_core
from .. import models
from . import base
from .base import SpikeMonitor, GeneralCurrentGenerator, LIFNeurons
@torch.no_grad()
def ln_params_transf... | 13,757 | 38.648415 | 124 | py |
moment-neural-network | moment-neural-network-main/mnn/snn/base/base_type.py | # -*- coding: utf-8 -*-
import torch
class BaseCurrentGenerator(torch.nn.Module):
def __init__(self) -> None:
super().__init__()
def forward(self, *args, **kwargs):
raise NotImplementedError
def reset(self, *args, **kwargs):
raise NotImplementedError
class BaseMonitor(to... | 1,216 | 23.34 | 44 | py |
moment-neural-network | moment-neural-network-main/mnn/snn/base/neurons.py | # -*- coding: utf-8 -*-
import torch
from torch import Tensor
from .base_type import BaseNeuronType
class LIFNeurons(BaseNeuronType):
__constants__ = ['L', 'V_th', 'V_res', 'V_spk', 'dt', 'T_ref']
def __init__(self, num_neurons, L=1/20, V_th=20, V_res=0., V_spk=50., dt=1e-1, T_ref=5., init_vol=None,spike_dt... | 2,666 | 39.409091 | 156 | py |
moment-neural-network | moment-neural-network-main/mnn/snn/base/functional.py | # -*- coding: utf-8 -*-
import torch
from torch import Tensor
import numpy as np
from typing import Optional
def sample_size(num_neurons, num_steps=None):
if num_steps is None:
num = [1, num_neurons]
else:
if isinstance(num_neurons, int):
num = (num_steps, num_neurons)
else... | 1,187 | 36.125 | 135 | py |
moment-neural-network | moment-neural-network-main/mnn/snn/base/probes.py | # -*- coding: utf-8 -*-
import torch
from collections import defaultdict
from typing import Union, List
import math
from .base_type import BaseProbe
class NeuronProbe(BaseProbe):
def __init__(self,
attr: Union[str, List],
dt: float = 1e-2,
probe_interval: int = 1,... | 1,877 | 35.115385 | 91 | py |
moment-neural-network | moment-neural-network-main/mnn/snn/base/currents.py | # -*- coding: utf-8 -*-
import numpy as np
import torch
from torch import Tensor
from typing import Optional
from .base_type import BaseCurrentGenerator
from .functional import sample_size, pregenerate_gaussian_current
class PregeneratedCurrent(BaseCurrentGenerator):
def __init__(self, pregenerated_current: Tens... | 5,926 | 37.993421 | 138 | py |
moment-neural-network | moment-neural-network-main/mnn/snn/base/monitors.py | # -*- coding: utf-8 -*-
import torch
from torch import Tensor
from .base_type import BaseMonitor
class SpikeMonitor(BaseMonitor):
def __init__(self, num_neurons, dt=1e-1, **kwargs):
super(SpikeMonitor, self).__init__()
self.register_buffer('monitor', torch.zeros(num_neurons).unsqueeze(0).to(torch.... | 1,296 | 34.054054 | 113 | py |
moment-neural-network | moment-neural-network-main/mnn/utils/training_tools/general_train.py | import os
import shutil
import tempfile
import time
from enum import Enum
import numpy as np
import torch
import torch.backends.cudnn as cudnn
from . import general_prepare
from . import functional as func
class Summary(Enum):
NONE = 0
AVERAGE = 1
SUM = 2
COUNT = 3
class AverageMeter(object):
"... | 21,713 | 39.435754 | 144 | py |
moment-neural-network | moment-neural-network-main/mnn/utils/training_tools/general_prepare.py | import argparse
import os
import random
import numpy as np
import torch.backends.cudnn as cudnn
import torch.nn
import torchvision
import yaml
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
import warnings
try:
from torch.distributed.optim import ZeroRedundancy... | 12,207 | 39.423841 | 128 | py |
moment-neural-network | moment-neural-network-main/mnn/utils/training_tools/functional.py | # -*- coding: utf-8 -*-
import yaml
import torch
import os
import warnings
from torch import Tensor, distributed as dist, multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DistributedSampler, DataLoader
class RecordMethods:
@staticmethod
def make_d... | 6,629 | 29.552995 | 119 | py |
moment-neural-network | moment-neural-network-main/mnn/utils/dataloaders/mnist_loader.py | # -*- coding: utf-8 -*-
from typing import Callable
import torchvision
from torch.utils.data import DataLoader
from torchvision import transforms
def classic_mnist_loader(data_dir: str, train_batch: int = 128, test_batch: int = 100,
transform_train: Callable = transforms.Compose([transforms.... | 1,239 | 52.913043 | 118 | py |
csn | csn-main/src/models/dist_prob_layer.py | import tensorflow as tf
from tensorflow import keras
class DistProbLayer(keras.layers.Layer):
def __init__(self, num_classes, duplication_factor): # Note that we assume the same number of classes and prototypes.
super(DistProbLayer, self).__init__()
self.num_classes = num_classes
self.dup... | 1,395 | 44.032258 | 122 | py |
csn | csn-main/src/models/proto_model.py | import networkx as nx
import numpy as np
import tensorflow as tf
from classification_models.tfkeras import Classifiers
from tensorflow import keras
from tensorflow.keras import layers
import pandas as pd
import seaborn as sn
import cv2
import pickle as pkl
import random
from scipy import stats
from src.data_parsing.mn... | 76,608 | 51.328552 | 152 | py |
csn | csn-main/src/models/proto_layer.py | import tensorflow as tf
from tensorflow import keras
import numpy as np
class ProtoLayer(tf.keras.layers.Layer):
def __init__(self, num_prototypes, dim, fixed_protos=None, in_plane=True, unit_cube_init=False):
super(ProtoLayer, self).__init__()
self.num_prototypes = num_prototypes
self.lat... | 5,494 | 51.333333 | 127 | py |
csn | csn-main/src/scripts/train_parity_after.py | import numpy as np
import tensorflow as tf
from src.data_parsing.mnist_data import get_digit_data, make_noisy
from src.models.proto_model import ProtoModel
from src.utils.gpu import set_gpu_config
set_gpu_config()
print(tf.test.is_gpu_available())
tf.compat.v1.disable_eager_execution()
latent_dim = 32
noise_level =... | 4,160 | 36.486486 | 114 | py |
csn | csn-main/src/scripts/train_adult.py | import numpy as np
from tensorflow import keras
import tensorflow as tf
from src.data_parsing.adult_data import get_adult_data
from src.models.proto_model import ProtoModel
from src.utils.gpu import set_gpu_config
set_gpu_config()
print(tf.test.is_gpu_available())
tf.compat.v1.disable_eager_execution()
wass_setup = ... | 4,326 | 44.072917 | 178 | py |
csn | csn-main/src/scripts/baseline_fashion_tree_edits.py | import numpy as np
import tensorflow as tf
from src.data_parsing.mnist_data import get_fashion_data, make_noisy, get_fashion_tree
from src.models.proto_model import ProtoModel
from src.utils.eval import trees_match, graph_edit_dist
from src.utils.gpu import set_gpu_config
from src.utils.to_files import write_to_file
... | 4,279 | 40.960784 | 122 | py |
csn | csn-main/src/scripts/train_fashion.py | import numpy as np
import tensorflow as tf
from src.data_parsing.mnist_data import get_fashion_data, make_noisy, get_fashion_tree
from src.models.proto_model import ProtoModel
from src.utils.gpu import set_gpu_config
from src.utils.eval import trees_match, graph_edit_dist
from src.utils.plotting import plot_mst
from s... | 5,032 | 45.601852 | 203 | py |
csn | csn-main/src/scripts/train_correlated.py | import numpy as np
import tensorflow as tf
from src.models.proto_model import ProtoModel
import tensorflow.keras as keras
from src.utils.gpu import set_gpu_config
set_gpu_config()
print(tf.test.is_gpu_available())
tf.compat.v1.disable_eager_execution()
factor = 1
def get_rainy_data(num_examples=10000):
true_tem... | 3,163 | 38.55 | 189 | py |
csn | csn-main/src/scripts/train_bolts.py | import networkx as nx
import numpy as np
import tensorflow as tf
from src.data_parsing.bolts.data_parser import DataParser
from src.models.proto_model import ProtoModel
from src.utils.eval import trees_match, graph_edit_dist
from src.utils.gpu import set_gpu_config
from src.utils.to_files import write_to_file
set_gpu... | 8,443 | 47.251429 | 255 | py |
csn | csn-main/src/scripts/train_german.py | import numpy as np
import tensorflow as tf
import tensorflow.keras as keras
from src.data_parsing.german_data import get_german_data
from src.models.proto_model import ProtoModel
from src.utils.gpu import set_gpu_config
set_gpu_config()
print(tf.test.is_gpu_available())
tf.compat.v1.disable_eager_execution()
wass_se... | 4,186 | 40.455446 | 189 | py |
csn | csn-main/src/scripts/train_hierarchical.py | # Mycal's recreation of the hierarchical prototype network training idea.
import numpy as np
import tensorflow as tf
import tensorflow.keras as keras
from src.data_parsing.mnist_data import get_digit_data, make_noisy, get_parity_tree
from src.models.proto_model import ProtoModel
from src.utils.to_files import write_t... | 4,534 | 46.239583 | 169 | py |
csn | csn-main/src/scripts/train_mnist_unsupervised.py | import numpy as np
import tensorflow as tf
from src.data_parsing.mnist_data import get_digit_data, make_noisy, get_guided_tree
from src.models.proto_model import ProtoModel
from src.utils.eval import trees_match
from src.utils.gpu import set_gpu_config
set_gpu_config()
print(tf.test.is_gpu_available())
tf.compat.v1.... | 2,955 | 47.459016 | 144 | py |
csn | csn-main/src/scripts/train_cifar100_deep.py | import numpy as np
import tensorflow as tf
from src.data_parsing.cifar_data import get_deep_data, get_deep_tree
from src.models.proto_model import ProtoModel
from src.utils.eval import trees_match, graph_edit_dist
from src.utils.gpu import set_gpu_config
from src.utils.to_files import write_to_file
set_gpu_config()
... | 3,332 | 48.746269 | 203 | py |
csn | csn-main/src/scripts/train_hierarchical_fashion.py | # Mycal's recreation of the hierarchical prototype network training idea.
import numpy as np
import tensorflow as tf
import tensorflow.keras as keras
from src.data_parsing.mnist_data import get_fashion_data, make_noisy, get_fashion_tree
from src.models.proto_model import ProtoModel
from src.utils.to_files import writ... | 5,242 | 44.991228 | 217 | py |
csn | csn-main/src/scripts/train_cifar10.py | import tensorflow as tf
from src.data_parsing.cifar_data import get_cifar10_data
from src.models.proto_model import ProtoModel
from src.utils.gpu import set_gpu_config
set_gpu_config()
print(tf.test.is_gpu_available())
tf.compat.v1.disable_eager_execution()
latent_dim = 40
x_train, y_train, y_train_one_hot, x_test,... | 1,822 | 41.395349 | 138 | py |
csn | csn-main/src/scripts/baseline_digit_tree_edits.py | import numpy as np
import tensorflow as tf
from src.data_parsing.mnist_data import get_digit_data, get_parity_tree
from src.models.proto_model import ProtoModel
from src.utils.eval import trees_match, graph_edit_dist
from src.utils.gpu import set_gpu_config
from src.utils.to_files import write_to_file
set_gpu_config(... | 3,181 | 43.194444 | 114 | py |
csn | csn-main/src/scripts/train_mnist_elastic.py | import tensorflow as tf
from src.data_parsing.mnist_data import get_digit_data
from src.models.proto_model import ProtoModel
from src.utils.gpu import set_gpu_config
set_gpu_config()
print(tf.test.is_gpu_available())
tf.compat.v1.disable_eager_execution()
latent_dim = 40
x_train, y_train, y_train_one_hot, x_test, y... | 1,528 | 38.205128 | 123 | py |
csn | csn-main/src/scripts/train_hierarchical_cifar100.py | import numpy as np
import tensorflow as tf
import tensorflow.keras as keras
from src.data_parsing.cifar_data import get_cifar100_data, get_cifar100_tree, get_cifar100_mapping
from src.models.proto_model import ProtoModel
from src.utils.eval import trees_match, graph_edit_dist
from src.utils.gpu import set_gpu_config
f... | 4,087 | 47.094118 | 140 | py |
csn | csn-main/src/scripts/train_mnist.py | import numpy as np
import tensorflow as tf
from src.data_parsing.mnist_data import get_digit_data, make_noisy, get_parity_tree
from src.models.proto_model import ProtoModel
from src.utils.eval import trees_match, graph_edit_dist
from src.utils.gpu import set_gpu_config
from src.utils.to_files import write_to_file
set... | 4,387 | 47.755556 | 203 | py |
csn | csn-main/src/scripts/train_cifar100.py | import numpy as np
import tensorflow as tf
from src.data_parsing.cifar_data import get_cifar100_data, get_cifar100_tree
from src.models.proto_model import ProtoModel
from src.utils.eval import trees_match, graph_edit_dist
from src.utils.gpu import set_gpu_config
from src.utils.to_files import write_to_file
set_gpu_co... | 2,857 | 47.440678 | 199 | py |
csn | csn-main/src/data_parsing/german_data.py | import numpy as np
import pandas as pd
from pandas.api.types import is_string_dtype
def normalize(df):
result = df.copy()
max_value = df.max()
min_value = df.min()
result = (df - min_value) / (max_value - min_value)
return result
def rebalance(x, p, y, dist):
keep_x = []
keep_p = []
... | 3,492 | 38.247191 | 116 | py |
csn | csn-main/src/data_parsing/mnist_data.py | from keras.datasets import fashion_mnist
from keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
import networkx as nx
def get_digit_data():
# Load the data.
(x_train, y_train), (x_test, y_test) = mnist.load_data()
image_size = x_train.shape[1]
original_dim = image_size * im... | 6,820 | 34.526042 | 105 | py |
csn | csn-main/src/data_parsing/cifar_data.py | import networkx as nx
import numpy as np
from keras.datasets import cifar10, cifar100
from tensorflow.keras.utils import to_categorical
cifar_fine_labels = [
'apple', # id 0
'aquarium_fish',
'baby',
'bear',
'beaver',
'bed',
'bee',
'beetle',
'bicycle',
'bottle',
'bowl',
... | 23,121 | 34.35474 | 201 | py |
csn | csn-main/src/data_parsing/inaturalist/inaturalist_dataflow.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: iNaturalist.py
from tensorflow.keras.utils import to_categorical
import json
import os
import os.path as osp
import pickle
import cv2
import numpy as np
__all__ = ['iNaturalistMeta', 'iNaturalist', 'iNaturalistFiles']
ground_truth_dir = '/mnt/mount_sda2/src/inatura... | 8,967 | 35.307692 | 110 | py |
csn | csn-main/tst/test_proto_layer.py | import unittest
from src.models.proto_layer import ProtoLayer
from tensorflow import keras
import numpy as np
from src.models.proto_model import ProtoModel
class MyTestCase(unittest.TestCase):
def setUp(self) -> None:
keras.backend.clear_session()
def test_projecting1(self):
num_prototypes = 2... | 2,764 | 38.5 | 108 | py |
Generalization-and-Memorization-in-Sparse-Training | Generalization-and-Memorization-in-Sparse-Training-main/run.py | """
[Title] run.py
[Usage] The file to train a model and save model information.
[Functions to be added]
[ ] iterative training
[ ] sparsity-aware initialization
[ ] random pruning (random mask?)
[ ] SNIP pruning
"""
from loader import load_dataset
from optim import Model
from helper import pruner, plo... | 19,948 | 44.965438 | 120 | py |
Generalization-and-Memorization-in-Sparse-Training | Generalization-and-Memorization-in-Sparse-Training-main/exp-spectrum.py | """
[Title] experiment-spectrum.py
[Usage] This is a file to calculate the hessian spectrum.
"""
from helper import utils, pruner, hessian
from pathlib import Path
from torch import nn
from PIL import Image
from functools import reduce
from abc import ABC, abstractmethod
from torch.utils.data import DataLoader
from to... | 6,223 | 33.577778 | 86 | py |
Generalization-and-Memorization-in-Sparse-Training | Generalization-and-Memorization-in-Sparse-Training-main/exp-trace.py | """
[Title] experiment-trace.py
[Usage] This is a file to calculate the hessian traces.
"""
# from pyhessian import hessian
import pyhessian
from helper import utils, pruner
from pathlib import Path
from torch import nn
from PIL import Image
from functools import reduce
from abc import ABC, abstractmethod
from torch.u... | 5,001 | 30.459119 | 85 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.