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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SpinalNet | SpinalNet-master/MNIST_VGG/MNIST_VGG_and_SpinalVGG.py | # -*- coding: utf-8 -*-
"""
This Script contains the default and Spinal VGG code for MNIST.
This code trains both NNs as two different models.
This code randomly changes the learning rate to get a good result.
@author: Dipu
"""
import torch
import torchvision
import torch.nn as nn
import math
import torch.nn.funct... | 11,616 | 32.191429 | 116 | py |
SpinalNet | SpinalNet-master/MNIST_VGG/FashionMNIST_VGG_and _SpinalVGG.py | # -*- coding: utf-8 -*-
"""
This Script contains the default and Spinal VGG code for Fashion-MNIST.
This code trains both NNs as two different models.
This code randomly changes the learning rate to get a good result.
@author: Dipu
"""
import torch
import torchvision
import torch.nn as nn
import math
import torch.... | 11,734 | 32.528571 | 116 | py |
SpinalNet | SpinalNet-master/MNIST_VGG/EMNIST_letters_VGG_and _SpinalVGG.py | # -*- coding: utf-8 -*-
"""
This Script contains the default and Spinal VGG code for EMNIST(Letters).
This code trains both NNs as two different models.
This code randomly changes the learning rate to get a good result.
@author: Dipu
"""
import torch
import torchvision
import torch.nn as nn
import math
import torch.n... | 11,677 | 32.751445 | 116 | py |
SpinalNet | SpinalNet-master/MNIST_VGG/QMNIST_VGG_and _SpinalVGG.py | # -*- coding: utf-8 -*-
"""
This Script contains the default and Spinal VGG code for QMNIST.
This code trains both NNs as two different models.
This code randomly changes the learning rate to get a good result.
@author: Dipu
"""
import torch
import torchvision
import torch.nn as nn
import math
import torch.nn.func... | 11,636 | 32.34384 | 116 | py |
SpinalNet | SpinalNet-master/MNIST/Arch2_Fashion_MNIST.py | # -*- coding: utf-8 -*-
"""
This Script contains the SpinalNet Arch2 Fashion MNIST code.
@author: Dipu
"""
import torch
import torchvision
import numpy as np
n_epochs = 200
batch_size_train = 64
batch_size_test = 1000
learning_rate = 0.005
momentum = 0.5
log_interval = 500
first_HL =300
max_accuracy= 0.0
random... | 10,213 | 34.099656 | 105 | py |
SpinalNet | SpinalNet-master/MNIST/Arch2_KMNIST.py | # -*- coding: utf-8 -*-
"""
This Script contains the SpinalNet Arch2 KMNIST code.
@author: Dipu
"""
import torch
import torchvision
n_epochs = 200
batch_size_train = 64
batch_size_test = 1000
momentum = 0.5
log_interval = 5000
first_HL = 50
random_seed = 1
torch.backends.cudnn.enabled = False
torch.manu... | 9,711 | 32.839721 | 117 | py |
SpinalNet | SpinalNet-master/MNIST/SpinalNet_MNIST.py | # -*- coding: utf-8 -*-
"""
This Script contains the SpinalNet MNIST code.
It ususlly provides better performance for the same number of epoch.
The same code can also be used for KMNIST, QMNIST and FashionMNIST.
torchvision.datasets.MNIST needs to be changed to
torchvision.datasets.FashionMNIST for FashionMNIST si... | 5,560 | 29.387978 | 78 | py |
SpinalNet | SpinalNet-master/MNIST/Arch2_QMNIST.py | # -*- coding: utf-8 -*-
"""
This Script contains the SpinalNet Arch2 QMNIST code.
@author: Dipu
"""
import torch
import torchvision
n_epochs = 200
batch_size_train = 64
batch_size_test = 1000
momentum = 0.5
log_interval = 5000
first_HL = 50
prob = 0.5
random_seed = 1
torch.backends.cudnn.enabled = False... | 9,751 | 32.512027 | 117 | py |
SpinalNet | SpinalNet-master/MNIST/default_pytorch_EMNIST.py | # -*- coding: utf-8 -*-
"""
This Script contains the default EMNIST code for comparison.
The code is collected from:
nextjournal.com/gkoehler/pytorch-mnist
As the EMNIST needs split='digits', we make a different file for EMNIST
@author: Dipu
"""
import torch
import torchvision
n_epochs = 8
batch_size_train = 6... | 4,274 | 28.081633 | 84 | py |
SpinalNet | SpinalNet-master/MNIST/Arch2_MNIST.py | # -*- coding: utf-8 -*-
"""
This Script contains the SpinalNet Arch2 MNIST code.
@author: Dipu
"""
import torch
import torchvision
import numpy as np
n_epochs = 200
batch_size_train = 64
batch_size_test = 1000
learning_rate = 0.01
momentum = 0.5
log_interval = 500
first_HL =30
max_accuracy= 0.0
torch.backends.cud... | 10,686 | 33.253205 | 105 | py |
SpinalNet | SpinalNet-master/MNIST/default_pytorch_MNIST.py | # -*- coding: utf-8 -*-
"""
This Script contains the default MNIST code for comparison.
The code is collected from:
nextjournal.com/gkoehler/pytorch-mnist
The same code can also be used for KMNIST, QMNIST and FashionMNIST.
torchvision.datasets.MNIST needs to be changed to
torchvision.datasets.FashionMNIST for ... | 4,349 | 28.391892 | 76 | py |
SpinalNet | SpinalNet-master/MNIST/Arch2_EMNIST_Digits.py | # -*- coding: utf-8 -*-
"""
This Script contains the SpinalNet Arch2 for EMNIST digits.
@author: Dipu
"""
import torch
import torchvision
n_epochs = 200
batch_size_train = 64
batch_size_test = 1000
momentum = 0.5
log_interval = 5000
first_HL = 50
prob = 0.5
torch.backends.cudnn.enabled = False
train_l... | 9,736 | 32.926829 | 117 | py |
SpinalNet | SpinalNet-master/MNIST/SpinalNet_EMNIST_Digits.py | # -*- coding: utf-8 -*-
"""
This Script contains the SpinalNet EMNIST digits code.
It provides better performance for the same number of epoch.
@author: Dipu
"""
import torch
import torchvision
n_epochs = 8
batch_size_train = 64
batch_size_test = 1000
learning_rate = 0.01
momentum = 0.5
log_interval = 100
first_HL ... | 5,407 | 30.08046 | 84 | py |
SpinalNet | SpinalNet-master/Customizable Model/spinalnettorch.py | # Customizable SpinalNet. Supports up to 30 layers.
import torch
import torch.nn as nn
import numpy as np
class SpinalNet(nn.Module):
def __init__(self, Input_Size, Number_of_Split, HL_width, number_HL, Output_Size, Activation_Function):
super(SpinalNet, self).__init__()
Splitted_Input_Si... | 12,469 | 46.414449 | 107 | py |
SpinalNet | SpinalNet-master/CIFAR-100/CNN_dropout_Default_and_SpinalFC_CIFAR100.py | # -*- coding: utf-8 -*-
"""
This Script contains the default and Spinal CNN dropout code for CIFAR-100.
This code trains both NNs as two different models.
The code is collected and changed from:
https://zhenye-na.github.io/2018/09/28/pytorch-cnn-cifar10.html
This code gradually decreases the learning rate to get... | 9,248 | 29.22549 | 99 | py |
SpinalNet | SpinalNet-master/CIFAR-100/ResNet_Default_and_SpinalFC_CIFAR100.py | # -*- coding: utf-8 -*-
"""
This Script contains the default and Spinal ResNet code for CIFAR-100.
This code trains both NNs as two different models.
There is option of choosing ResNet18(), ResNet34(), SpinalResNet18(), or
SpinalResNet34().
This code randomly changes the learning rate to get a good result.
@author:... | 13,588 | 30.025114 | 101 | py |
SpinalNet | SpinalNet-master/CIFAR-100/VGG_Default_and_SpinalFC_CIFAR_100.py | # -*- coding: utf-8 -*-
"""
This Script contains the default and Spinal VGG code for CIFAR-100.
This code trains both NNs as two different models.
There is option of choosing NN among:
vgg11_bn(), vgg13_bn(), vgg16_bn(), vgg19_bn() and
Spinalvgg11_bn(), Spinalvgg13_bn(), Spinalvgg16_bn(), Spinalvgg19_bn()
Th... | 9,281 | 28.845659 | 116 | py |
SubOmiEmbed | SubOmiEmbed-main/train_test.py | """
Training and testing for OmiEmbed
"""
import time
import warnings
import numpy as np
import torch
from util import util
from params.train_test_params import TrainTestParams
from datasets import create_separate_dataloader
from models import create_model
from util.visualizer import Visualizer
if __name__ == "__mai... | 7,049 | 54.952381 | 146 | py |
SubOmiEmbed | SubOmiEmbed-main/models/vae_survival_model.py | import torch
from .vae_basic_model import VaeBasicModel
from . import networks
from . import losses
class VaeSurvivalModel(VaeBasicModel):
"""
This class implements the VAE survival model, using the VAE framework with the survival prediction downstream task.
"""
@staticmethod
def modify_commandli... | 5,390 | 38.933333 | 151 | py |
SubOmiEmbed | SubOmiEmbed-main/models/losses.py | import torch
import torch.nn as nn
def get_loss_func(loss_name, reduction='mean'):
"""
Return the loss function.
Parameters:
loss_name (str) -- the name of the loss function: BCE | MSE | L1 | CE
reduction (str) -- the reduction method applied to the loss function: sum | mean
"""
... | 2,558 | 32.671053 | 176 | py |
SubOmiEmbed | SubOmiEmbed-main/models/vae_alltask_gn_model.py | import torch
import torch.nn as nn
from .basic_model import BasicModel
from . import networks
from . import losses
from torch.nn import functional as F
from sklearn import metrics
class VaeAlltaskGNModel(BasicModel):
"""
This class implements the VAE multitasking model with GradNorm (all tasks), using the VAE... | 17,700 | 47.231608 | 382 | py |
SubOmiEmbed | SubOmiEmbed-main/models/vae_regression_model.py | import torch
from sklearn import metrics
from .vae_basic_model import VaeBasicModel
from . import networks
from . import losses
class VaeRegressionModel(VaeBasicModel):
"""
This class implements the VAE regression model, using the VAE framework with the regression downstream task.
"""
@staticmethod
... | 3,793 | 37.323232 | 152 | py |
SubOmiEmbed | SubOmiEmbed-main/models/vae_alltask_model.py | import torch
from .vae_basic_model import VaeBasicModel
from . import networks
from . import losses
from torch.nn import functional as F
from sklearn import metrics
class VaeAlltaskModel(VaeBasicModel):
"""
This class implements the VAE multitasking model with all downstream tasks (5 classifiers + 1 regressor... | 10,265 | 49.078049 | 371 | py |
SubOmiEmbed | SubOmiEmbed-main/models/networks.py | import torch
import torch.nn as nn
import functools
from torch.nn import init
from torch.optim import lr_scheduler
# Class components
class DownSample(nn.Module):
"""
SingleConv1D module + MaxPool
The output dimension = input dimension // down_ratio
"""
def __init__(self, input_chan_num, output_c... | 107,411 | 46.131198 | 202 | py |
SubOmiEmbed | SubOmiEmbed-main/models/basic_model.py | import os
import torch
import numpy as np
from abc import ABC, abstractmethod
from . import networks
from collections import OrderedDict
class BasicModel(ABC):
"""
This class is an abstract base class for models.
To create a subclass, you need to implement the following five functions:
-- <__init_... | 15,137 | 39.475936 | 166 | py |
SubOmiEmbed | SubOmiEmbed-main/models/vae_classifier_model.py | import torch
from .vae_basic_model import VaeBasicModel
from . import networks
from . import losses
from torch.nn import functional as F
import random
class VaeClassifierModel(VaeBasicModel):
"""
This class implements the VAE classifier model, using the VAE framework with the classification downstream task.
... | 9,850 | 44.396313 | 151 | py |
SubOmiEmbed | SubOmiEmbed-main/models/vae_multitask_model.py | import torch
from .vae_basic_model import VaeBasicModel
from . import networks
from . import losses
from torch.nn import functional as F
from sklearn import metrics
class VaeMultitaskModel(VaeBasicModel):
"""
This class implements the VAE multitasking model, using the VAE framework with the multiple downstrea... | 8,142 | 44.238889 | 269 | py |
SubOmiEmbed | SubOmiEmbed-main/models/vae_basic_model.py | import torch
from .basic_model import BasicModel
from . import networks
from . import losses
class VaeBasicModel(BasicModel):
"""
This is the basic VAE model class, called by all other VAE son classes.
"""
def __init__(self, param):
"""
Initialize the VAE basic class.
"""
... | 12,659 | 48.84252 | 153 | py |
SubOmiEmbed | SubOmiEmbed-main/models/vae_multitask_gn_model.py | import torch
import torch.nn as nn
from .basic_model import BasicModel
from . import networks
from . import losses
from torch.nn import functional as F
from sklearn import metrics
class VaeMultitaskGNModel(BasicModel):
"""
This class implements the VAE multitasking model with GradNorm, using the VAE framework... | 15,071 | 45.091743 | 269 | py |
SubOmiEmbed | SubOmiEmbed-main/util/visualizer.py | import os
import time
import numpy as np
import pandas as pd
import sklearn as sk
from sklearn.preprocessing import label_binarize
from util import util
from util import metrics
from torch.utils.tensorboard import SummaryWriter
class Visualizer:
"""
This class print/save logging information
"""
def _... | 27,478 | 49.981447 | 370 | py |
SubOmiEmbed | SubOmiEmbed-main/util/util.py | """
Contain some simple helper functions
"""
import os
import shutil
import torch
import random
import numpy as np
def mkdir(path):
"""
Create a empty directory in the disk if it didn't exist
Parameters:
path(str) -- a directory path we would like to create
"""
if not os.path.exists(path)... | 1,204 | 20.517857 | 82 | py |
SubOmiEmbed | SubOmiEmbed-main/params/basic_params.py | import time
import argparse
import torch
import os
import models
from util import util
class BasicParams:
"""
This class define the console parameters
"""
def __init__(self):
"""
Reset the class. Indicates the class hasn't been initialized
"""
self.initialized = False
... | 12,834 | 54.323276 | 224 | py |
SubOmiEmbed | SubOmiEmbed-main/datasets/a_dataset.py | import os.path
from datasets import load_file
from datasets import get_survival_y_true
from datasets.basic_dataset import BasicDataset
import numpy as np
import pandas as pd
import torch
class ADataset(BasicDataset):
"""
A dataset class for gene expression dataset.
File should be prepared as '/path/to/dat... | 10,137 | 50.461929 | 184 | py |
SubOmiEmbed | SubOmiEmbed-main/datasets/abc_dataset.py | import os.path
from datasets import load_file
from datasets import get_survival_y_true
from datasets.basic_dataset import BasicDataset
from util import preprocess
import numpy as np
import pandas as pd
import torch
class ABCDataset(BasicDataset):
"""
A dataset class for multi-omics dataset.
For gene expre... | 13,033 | 48.748092 | 152 | py |
SubOmiEmbed | SubOmiEmbed-main/datasets/basic_dataset.py | """
This module implements an abstract base class for datasets. Other datasets can be created from this base class.
"""
import torch.utils.data as data
from abc import ABC, abstractmethod
class BasicDataset(data.Dataset, ABC):
"""
This class is an abstract base class for datasets.
To create a subclass, yo... | 1,272 | 31.641026 | 116 | py |
SubOmiEmbed | SubOmiEmbed-main/datasets/ab_dataset.py | import os.path
from datasets import load_file
from datasets import get_survival_y_true
from datasets.basic_dataset import BasicDataset
from util import preprocess
import numpy as np
import pandas as pd
import torch
class ABDataset(BasicDataset):
"""
A dataset class for multi-omics dataset.
For gene expres... | 12,076 | 49.112033 | 152 | py |
SubOmiEmbed | SubOmiEmbed-main/datasets/c_dataset.py | import os.path
from datasets import load_file
from datasets import get_survival_y_true
from datasets.basic_dataset import BasicDataset
import numpy as np
import pandas as pd
import torch
class CDataset(BasicDataset):
"""
A dataset class for miRNA expression dataset.
File should be prepared as '/path/to/da... | 10,372 | 50.098522 | 152 | py |
SubOmiEmbed | SubOmiEmbed-main/datasets/__init__.py | """
This package about data loading and data preprocessing
"""
import os
import torch
import importlib
import numpy as np
import pandas as pd
from util import util
from datasets.basic_dataset import BasicDataset
from datasets.dataloader_prefetch import DataLoaderPrefetch
from torch.utils.data import Subset
from sklearn... | 8,346 | 34.219409 | 177 | py |
SubOmiEmbed | SubOmiEmbed-main/datasets/dataloader_prefetch.py | from torch.utils.data import DataLoader
from prefetch_generator import BackgroundGenerator
class DataLoaderPrefetch(DataLoader):
def __iter__(self):
return BackgroundGenerator(super().__iter__())
| 210 | 25.375 | 54 | py |
SubOmiEmbed | SubOmiEmbed-main/datasets/b_dataset.py | import os.path
from datasets import load_file
from datasets import get_survival_y_true
from datasets.basic_dataset import BasicDataset
from util import preprocess
import numpy as np
import pandas as pd
import torch
class BDataset(BasicDataset):
"""
A dataset class for methylation dataset.
DNA methylation ... | 11,172 | 49.556561 | 152 | py |
mixstyle-release | mixstyle-release-master/reid/main.py | import sys
import time
import os.path as osp
import argparse
import torch
import torch.nn as nn
import torchreid
from torchreid.utils import (
Logger, check_isfile, set_random_seed, collect_env_info,
resume_from_checkpoint, load_pretrained_weights, compute_model_complexity
)
from default_config import (
i... | 7,293 | 32.925581 | 159 | py |
mixstyle-release | mixstyle-release-master/reid/models/osnet_db.py | from __future__ import division, absolute_import
import warnings
import torch
from torch import nn
from torch.nn import functional as F
from .dropblock import DropBlock2D, LinearScheduler
__all__ = [
'osnet_x1_0', 'osnet_x0_75', 'osnet_x0_5', 'osnet_x0_25', 'osnet_ibn_x1_0'
]
pretrained_urls = {
'osnet_x1_0'... | 18,266 | 27.676609 | 108 | py |
mixstyle-release | mixstyle-release-master/reid/models/resnet_db.py | """
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import torch.utils.model_zoo as model_zoo
from torch import nn
from .dropblock import DropBlock2D, LinearScheduler
__all__ = [
'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
'resnext50_32x4d'... | 16,436 | 27.685864 | 106 | py |
mixstyle-release | mixstyle-release-master/reid/models/osnet_ms.py | from __future__ import division, absolute_import
import warnings
import torch
from torch import nn
from torch.nn import functional as F
from .mixstyle import MixStyle
__all__ = [
'osnet_x1_0', 'osnet_x0_75', 'osnet_x0_5', 'osnet_x0_25', 'osnet_ibn_x1_0'
]
pretrained_urls = {
'osnet_x1_0':
'https://drive.... | 19,075 | 27.5142 | 108 | py |
mixstyle-release | mixstyle-release-master/reid/models/resnet_ms.py | """
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import torch.utils.model_zoo as model_zoo
from torch import nn
from .mixstyle import MixStyle
__all__ = [
'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
'resnext50_32x4d', 'resnext101_32x8d',... | 19,818 | 27.516547 | 106 | py |
mixstyle-release | mixstyle-release-master/reid/models/mixstyle.py | import random
from contextlib import contextmanager
import torch
import torch.nn as nn
def deactivate_mixstyle(m):
if type(m) == MixStyle:
m.set_activation_status(False)
def activate_mixstyle(m):
if type(m) == MixStyle:
m.set_activation_status(True)
def random_mixstyle(m):
if type(m) =... | 3,127 | 24.430894 | 90 | py |
mixstyle-release | mixstyle-release-master/reid/models/osnet_ms2.py | from __future__ import division, absolute_import
import warnings
import torch
from torch import nn
from torch.nn import functional as F
from .mixstyle import MixStyle
__all__ = [
'osnet_x1_0', 'osnet_x0_75', 'osnet_x0_5', 'osnet_x0_25', 'osnet_ibn_x1_0'
]
pretrained_urls = {
'osnet_x1_0':
'https://drive.... | 18,135 | 27.56063 | 108 | py |
mixstyle-release | mixstyle-release-master/reid/models/resnet_ms2.py | """
Code source: https://github.com/pytorch/vision
"""
from __future__ import division, absolute_import
import torch.utils.model_zoo as model_zoo
from torch import nn
from .mixstyle import MixStyle
__all__ = [
'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
'resnext50_32x4d', 'resnext101_32x8d',... | 16,298 | 27.898936 | 106 | py |
mixstyle-release | mixstyle-release-master/reid/models/dropblock/dropblock.py | import torch
import torch.nn.functional as F
from torch import nn
class DropBlock2D(nn.Module):
r"""Randomly zeroes 2D spatial blocks of the input tensor.
As described in the paper
`DropBlock: A regularization method for convolutional networks`_ ,
dropping whole blocks of feature map allows to remove... | 4,440 | 29.210884 | 98 | py |
mixstyle-release | mixstyle-release-master/reid/models/dropblock/scheduler.py | import numpy as np
from torch import nn
class LinearScheduler(nn.Module):
def __init__(self, dropblock, start_value, stop_value, nr_steps):
super(LinearScheduler, self).__init__()
self.dropblock = dropblock
self.i = 0
self.drop_values = np.linspace(start=start_value, stop=stop_valu... | 546 | 26.35 | 88 | py |
mixstyle-release | mixstyle-release-master/imcls/vis.py | import argparse
import torch
import os.path as osp
import numpy as np
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
from matplotlib import pyplot as plt
def normalize(feature):
norm = np.sqrt((feature**2).sum(1, keepdims=True))
return feature / (norm + 1e-12)
def main():
parser... | 3,338 | 26.368852 | 87 | py |
mixstyle-release | mixstyle-release-master/imcls/train.py | import argparse
import copy
import torch
from dassl.utils import setup_logger, set_random_seed, collect_env_info
from dassl.config import get_cfg_default
from dassl.engine import build_trainer
# custom
from yacs.config import CfgNode as CN
import datasets.ssdg_pacs
import datasets.ssdg_officehome
import datasets.msda... | 5,312 | 26.386598 | 80 | py |
mixstyle-release | mixstyle-release-master/imcls/trainers/semimixstyle.py | import torch
from torch.nn import functional as F
from dassl.data import DataManager
from dassl.engine import TRAINER_REGISTRY, TrainerXU
from dassl.metrics import compute_accuracy
from dassl.data.transforms import build_transform
from dassl.modeling.ops import deactivate_mixstyle, run_with_mixstyle
@TRAINER_REGISTR... | 4,835 | 35.360902 | 79 | py |
mixstyle-release | mixstyle-release-master/imcls/trainers/vanilla2.py | import torch
from torch.nn import functional as F
from dassl.engine import TRAINER_REGISTRY, TrainerX
from dassl.metrics import compute_accuracy
from dassl.modeling.ops import random_mixstyle, crossdomain_mixstyle
@TRAINER_REGISTRY.register()
class Vanilla2(TrainerX):
"""Vanilla baseline.
Slightly modified ... | 3,011 | 29.424242 | 77 | py |
scipy | scipy-main/dev.py | #! /usr/bin/env python3
'''
Developer CLI: building (meson), tests, benchmark, etc.
This file contains tasks definitions for doit (https://pydoit.org).
And also a CLI interface using click (https://click.palletsprojects.com).
The CLI is ideal for project contributors while,
doit interface is better suited for author... | 50,071 | 33.085773 | 87 | py |
scipy | scipy-main/scipy/conftest.py | # Pytest customization
import json
import os
import warnings
import numpy as np
import numpy.array_api
import numpy.testing as npt
import pytest
from scipy._lib._fpumode import get_fpu_mode
from scipy._lib._testutils import FPUModeChangeWarning
from scipy._lib import _pep440
from scipy._lib._array_api import SCIPY_AR... | 5,991 | 33.436782 | 91 | py |
scipy | scipy-main/scipy/linalg/_basic.py | #
# Author: Pearu Peterson, March 2002
#
# w/ additions by Travis Oliphant, March 2002
# and Jake Vanderplas, August 2012
from warnings import warn
from itertools import product
import numpy as np
from numpy import atleast_1d, atleast_2d
from .lapack import get_lapack_funcs, _compute_lwork
from ._misc imp... | 69,470 | 34.939472 | 79 | py |
scipy | scipy-main/scipy/_lib/setup.py | import os
def check_boost_submodule():
from scipy._lib._boost_utils import _boost_dir
if not os.path.exists(_boost_dir(ret_path=True).parent / 'README.md'):
raise RuntimeError("Missing the `boost` submodule! Run `git submodule "
"update --init` to fix this.")
def check_hi... | 3,817 | 35.711538 | 82 | py |
scipy | scipy-main/scipy/_lib/tests/test_array_api.py | import numpy as np
from numpy.testing import assert_equal
import pytest
from scipy.conftest import array_api_compatible
from scipy._lib._array_api import (
_GLOBAL_CONFIG, array_namespace, as_xparray,
)
if not _GLOBAL_CONFIG["SCIPY_ARRAY_API"]:
pytest.skip(
"Array API test; set environment variable S... | 2,051 | 26.72973 | 79 | py |
scipy | scipy-main/doc/source/conf.py | import math
import os
from os.path import relpath, dirname
import re
import sys
import warnings
from datetime import date
from docutils import nodes
from docutils.parsers.rst import Directive
import matplotlib
import matplotlib.pyplot as plt
from numpydoc.docscrape_sphinx import SphinxDocString
from sphinx.util import... | 16,583 | 31.839604 | 87 | py |
MENET | MENET-master/light/utils/transforms.py | # -*- coding: utf-8 -*-
# @File : derain_wgan_tf/transforms.py
# @Info : @ TSMC-SIGGRAPH, 2019/5/29
# @Desc : @ sumihui : refer to pytorch
# -.-.. - ... -- -.-. .-.. .- -... .---. -.-- ..- .-.. --- -. --. ..-. .- -.
import numpy as np
from PIL import Image
class Compose(object):
"""Composes several... | 5,204 | 33.932886 | 106 | py |
MENET | MENET-master/heavy/utils/transforms.py | # -*- coding: utf-8 -*-
# @File : derain_wgan_tf/transforms.py
# @Info : @ TSMC-SIGGRAPH, 2019/5/29
# @Desc : @ sumihui : refer to pytorch
# -.-.. - ... -- -.-. .-.. .- -... .---. -.-- ..- .-.. --- -. --. ..-. .- -.
import numpy as np
from PIL import Image
class Compose(object):
"""Composes several... | 5,204 | 33.932886 | 106 | py |
ARFlow | ARFlow-master/inference.py | import imageio
import argparse
import numpy as np
import matplotlib.pyplot as plt
import torch
from easydict import EasyDict
from torchvision import transforms
from transforms import sep_transforms
from utils.flow_utils import flow_to_image, resize_flow
from utils.torch_utils import restore_model
from models.pwclite ... | 2,310 | 29.813333 | 90 | py |
ARFlow | ARFlow-master/basic_train.py | import torch
from utils.torch_utils import init_seed
from datasets.get_dataset import get_dataset
from models.get_model import get_model
from losses.get_loss import get_loss
from trainer.get_trainer import get_trainer
def main(cfg, _log):
init_seed(cfg.seed)
_log.info("=> fetching img pairs.")
train_set... | 1,854 | 34.673077 | 80 | py |
ARFlow | ARFlow-master/trainer/base_trainer.py | import torch
import numpy as np
from abc import abstractmethod
from tensorboardX import SummaryWriter
from utils.torch_utils import bias_parameters, weight_parameters, \
load_checkpoint, save_checkpoint, AdamW
class BaseTrainer:
"""
Base class for all trainers
"""
def __init__(self, train_loader,... | 4,244 | 34.672269 | 83 | py |
ARFlow | ARFlow-master/trainer/kitti_trainer_ar.py | import time
import torch
import numpy as np
from copy import deepcopy
from .base_trainer import BaseTrainer
from utils.flow_utils import load_flow, evaluate_flow
from utils.misc_utils import AverageMeter
from transforms.ar_transforms.sp_transfroms import RandomAffineFlow
from transforms.ar_transforms.oc_transforms impo... | 8,755 | 40.49763 | 91 | py |
ARFlow | ARFlow-master/trainer/sintel_trainer.py | import time
import torch
from .base_trainer import BaseTrainer
from utils.flow_utils import evaluate_flow
from utils.misc_utils import AverageMeter
class TrainFramework(BaseTrainer):
def __init__(self, train_loader, valid_loader, model, loss_func,
_log, save_root, config):
super(TrainFram... | 5,445 | 37.9 | 89 | py |
ARFlow | ARFlow-master/trainer/kitti_trainer.py | import time
import torch
import numpy as np
from .base_trainer import BaseTrainer
from utils.flow_utils import load_flow, evaluate_flow
from utils.misc_utils import AverageMeter
class TrainFramework(BaseTrainer):
def __init__(self, train_loader, valid_loader, model, loss_func,
_log, save_root, co... | 5,884 | 38.496644 | 89 | py |
ARFlow | ARFlow-master/trainer/sintel_trainer_ar.py | import time
import torch
from copy import deepcopy
from .base_trainer import BaseTrainer
from utils.flow_utils import evaluate_flow
from utils.misc_utils import AverageMeter
from transforms.ar_transforms.sp_transfroms import RandomAffineFlow
from transforms.ar_transforms.oc_transforms import run_slic_pt, random_crop
... | 8,316 | 40.173267 | 91 | py |
ARFlow | ARFlow-master/models/pwclite.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from utils.warp_utils import flow_warp
from .correlation_package.correlation import Correlation
# from .correlation_native import Correlation
def conv(in_planes, out_planes, kernel_size=3, stride=1, dilation=1, isReLU=True):
if isReLU:
ret... | 10,680 | 36.742049 | 90 | py |
ARFlow | ARFlow-master/models/correlation_native.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class Correlation(nn.Module):
def __init__(self, max_displacement=4, *args, **kwargs):
super(Correlation, self).__init__()
self.max_displacement = max_displacement
self.output_dim = 2 * self.max_displacement + 1
sel... | 2,336 | 28.961538 | 90 | py |
ARFlow | ARFlow-master/models/correlation_package/correlation.py | import torch
from torch.nn.modules.module import Module
from torch.autograd import Function
import correlation_cuda
class CorrelationFunction(Function):
def __init__(self, pad_size=3, kernel_size=3, max_displacement=20, stride1=1, stride2=2, corr_multiply=1):
super(CorrelationFunction, self).__init__()
... | 2,265 | 34.968254 | 156 | py |
ARFlow | ARFlow-master/models/correlation_package/setup.py | #!/usr/bin/env python3
import os
import torch
from setuptools import setup, find_packages
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
cxx_args = ['-std=c++11']
nvcc_args = [
'-gencode', 'arch=compute_50,code=sm_50',
'-gencode', 'arch=compute_52,code=sm_52',
'-gencode', 'arch=compu... | 813 | 26.133333 | 105 | py |
ARFlow | ARFlow-master/datasets/get_dataset.py | import copy
from torchvision import transforms
from torch.utils.data import ConcatDataset
from transforms.co_transforms import get_co_transforms
from transforms.ar_transforms.ap_transforms import get_ap_transforms
from transforms import sep_transforms
from datasets.flow_datasets import SintelRaw, Sintel
from datasets.... | 5,969 | 47.536585 | 89 | py |
ARFlow | ARFlow-master/datasets/flow_datasets.py | import imageio
import numpy as np
import random
from path import Path
from abc import abstractmethod, ABCMeta
from torch.utils.data import Dataset
from utils.flow_utils import load_flow
class ImgSeqDataset(Dataset, metaclass=ABCMeta):
def __init__(self, root, n_frames, input_transform=None, co_transform=None,
... | 10,692 | 38.3125 | 90 | py |
ARFlow | ARFlow-master/utils/warp_utils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import inspect
def mesh_grid(B, H, W):
# mesh grid
x_base = torch.arange(0, W).repeat(B, H, 1) # BHW
y_base = torch.arange(0, H).repeat(B, W, 1).transpose(1, 2) # BHW
base_grid = torch.stack([x_base, y_base], 1) # B2HW
return b... | 3,850 | 33.079646 | 106 | py |
ARFlow | ARFlow-master/utils/flow_utils.py | import torch
import cv2
import numpy as np
from matplotlib.colors import hsv_to_rgb
def load_flow(path):
if path.endswith('.png'):
# for KITTI which uses 16bit PNG images
# see 'https://github.com/ClementPinard/FlowNetPytorch/blob/master/datasets/KITTI.py'
# The -1 is here to specify not t... | 4,870 | 38.601626 | 93 | py |
ARFlow | ARFlow-master/utils/torch_utils.py | import torch
import shutil
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import numbers
import random
import math
from torch.optim import Optimizer
def init_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
def weig... | 5,676 | 34.04321 | 102 | py |
ARFlow | ARFlow-master/transforms/sep_transforms.py | import numpy as np
import torch
# from scipy.misc import imresize
from skimage.transform import resize as imresize
class ArrayToTensor(object):
"""Converts a numpy.ndarray (H x W x C) to a torch.FloatTensor of shape (C x H x W)."""
def __call__(self, array):
assert (isinstance(array, np.ndarray))
... | 832 | 26.766667 | 91 | py |
ARFlow | ARFlow-master/transforms/ar_transforms/interpolation.py | ## Portions of Code from, copyright 2018 Jochen Gast
from __future__ import absolute_import, division, print_function
import torch
from torch import nn
import torch.nn.functional as tf
def _bchw2bhwc(tensor):
return tensor.transpose(1,2).transpose(2,3)
def _bhwc2bchw(tensor):
return tensor.transpose(2,3).... | 6,104 | 37.15625 | 101 | py |
ARFlow | ARFlow-master/transforms/ar_transforms/sp_transfroms.py | # Part of the code from https://github.com/visinf/irr/blob/master/augmentations.py
import torch
import torch.nn as nn
from transforms.ar_transforms.interpolation import Interp2
from transforms.ar_transforms.interpolation import Meshgrid
import numpy as np
def denormalize_coords(xx, yy, width, height):
""" scale ... | 12,154 | 34.437318 | 89 | py |
ARFlow | ARFlow-master/transforms/ar_transforms/ap_transforms.py | import numpy as np
import torch
from torchvision import transforms as tf
from PIL import ImageFilter
def get_ap_transforms(cfg):
transforms = [ToPILImage()]
if cfg.cj:
transforms.append(ColorJitter(brightness=cfg.cj_bri,
contrast=cfg.cj_con,
... | 2,275 | 30.611111 | 85 | py |
ARFlow | ARFlow-master/transforms/ar_transforms/oc_transforms.py | import numpy as np
import torch
# from skimage.color import rgb2yuv
import cv2
from fast_slic.avx2 import SlicAvx2 as Slic
from skimage.segmentation import slic as sk_slic
def run_slic_pt(img_batch, n_seg=200, compact=10, rd_select=(8, 16), fast=True): # Nx1xHxW
"""
:param img: Nx3xHxW 0~1 float32
:para... | 1,952 | 29.046154 | 91 | py |
ARFlow | ARFlow-master/losses/flow_loss.py | import torch.nn as nn
import torch.nn.functional as F
from .loss_blocks import SSIM, smooth_grad_1st, smooth_grad_2nd, TernaryLoss
from utils.warp_utils import flow_warp
from utils.warp_utils import get_occu_mask_bidirection, get_occu_mask_backward
class unFlowLoss(nn.modules.Module):
def __init__(self, cfg):
... | 4,395 | 37.226087 | 88 | py |
ARFlow | ARFlow-master/losses/loss_blocks.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# Crecit: https://github.com/simonmeister/UnFlow/blob/master/src/e2eflow/core/losses.py
def TernaryLoss(im, im_warp, max_distance=1):
patch_size = 2 * max_distance + 1
def _rgb_to_grayscale(image):
grayscale = image[:, 0, :, :] * 0.29... | 3,197 | 30.98 | 87 | py |
myriad | myriad-main/run.py | # (c) 2021 Nikolaus Howe
import numpy as np
import random
from jax.config import config
from myriad.experiments.e2e_sysid import run_endtoend
from myriad.experiments.mle_sysid import run_mle_sysid
from myriad.experiments.node_e2e_sysid import run_node_endtoend
from myriad.experiments.node_mle_sysid import run_node_ml... | 2,407 | 26.363636 | 83 | py |
myriad | myriad-main/tests/tests.py | # (c) Nikolaus Howe 2021
from scipy.integrate import odeint
import jax.numpy as jnp
import numpy as np
import sys
import unittest
from run import run_trajectory_opt
from myriad.config import IntegrationMethod, NLPSolverType, OptimizerType, QuadratureRule, SystemType
from myriad.custom_types import State, Control, Tim... | 7,521 | 26.654412 | 104 | py |
myriad | myriad-main/tests/test_smoke.py | import random
import unittest
import jax
import numpy as np
from myriad.config import Config, SystemType, HParams, OptimizerType
from myriad.trajectory_optimizers import get_optimizer
from myriad.systems import IndirectFHCS
from myriad.plotting import plot_result
# import os
# os.environ['KMP_DUPLICATE_LIB_OK'] = 'T... | 2,495 | 36.253731 | 117 | py |
myriad | myriad-main/myriad/custom_types.py | # (c) Nikolaus Howe 2021
import jax.numpy as jnp
from typing import Callable, Mapping, Optional, Union
Batch = jnp.ndarray
Control = Union[float, jnp.ndarray]
Controls = jnp.ndarray
Cost = float
Dataset = jnp.ndarray
Defect = jnp.ndarray
DParams = Mapping[str, Union[float, jnp.ndarray]]
DState = Union[float, jnp.nda... | 663 | 25.56 | 68 | py |
myriad | myriad-main/myriad/plotting.py | # (c) 2021 Nikolaus Howe
import jax.numpy as jnp
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.offsetbox import AnchoredText
from typing import Dict, Optional, Tuple
from myriad.config import SystemType, IntegrationMethod, OptimizerType, HParams
from myriad.systems import state_... | 8,641 | 32.496124 | 125 | py |
myriad | myriad-main/myriad/probing_numerical_instability.py | # (c) 2021 Nikolaus Howe
import numpy as np
import jax
import jax.numpy as jnp
import matplotlib.pyplot as plt
import pickle as pkl
from jax import lax
from typing import Callable, Tuple
from myriad.custom_types import State, States, Control, Controls, DState
from myriad.utils import integrate, integrate_time_indepen... | 7,453 | 30.451477 | 116 | py |
myriad | myriad-main/myriad/utils.py | # (c) 2021 Nikolaus Howe
from __future__ import annotations
import jax
import jax.numpy as jnp
import numpy as np
import time
import typing
if typing.TYPE_CHECKING:
from myriad.neural_ode.create_node import NeuralODE
from myriad.config import HParams, Config
from jax import jit, lax, vmap
from typing import Call... | 18,600 | 39.088362 | 133 | py |
myriad | myriad-main/myriad/useful_scripts.py | # (c) 2021 Nikolaus Howe
from __future__ import annotations
import jax.numpy as jnp
import numpy as np
import pickle as pkl
import simple_parsing
from jax.flatten_util import ravel_pytree
from jax.config import config
from pathlib import Path
from typing import Tuple
from myriad.config import HParams, Config
from my... | 11,496 | 36.087097 | 118 | py |
myriad | myriad-main/myriad/config.py | # (c) 2021 Nikolaus Howe
from typing import Tuple
import jax
from dataclasses import dataclass
from enum import Enum
from myriad.systems import SystemType
class OptimizerType(Enum):
"""Parser argument. Optimizing strategy used to solve the OCP"""
# _settings_ = NoAlias
COLLOCATION = "COLLOCATION"
SHOOTING ... | 4,564 | 34.115385 | 150 | py |
myriad | myriad-main/myriad/study_scripts.py | # (c) 2021 Nikolaus Howe
import jax.numpy as jnp
import matplotlib
import matplotlib.pyplot as plt
import pickle as pkl
from jax.config import config
from pathlib import Path
from myriad.defaults import param_guesses
from myriad.neural_ode.create_node import NeuralODE
from myriad.experiments.mle_sysid import run_mle_... | 7,920 | 32.706383 | 113 | py |
myriad | myriad-main/myriad/__init__.py | """
This library implements in [JAX](https://github.com/google/jax) various real-world environments,
neural ODEs for system identification, and trajectory optimizers for solving the optimal control problem.
"""
# from .config import *
# from .nlp_solvers import *
# from .trajectory_optimizers import *
# from .plotting ... | 1,043 | 40.76 | 105 | py |
myriad | myriad-main/myriad/neural_ode/data_generators.py | # # (c) 2021 Nikolaus Howe
# from __future__ import annotations # for nicer typing
#
# import typing
#
# if typing.TYPE_CHECKING:
# pass
# import jax
# import jax.numpy as jnp
# import numpy as np
# import time
#
# from typing import Optional
#
# from myriad.config import Config, HParams, SamplingApproach
# from myr... | 8,950 | 39.502262 | 124 | py |
myriad | myriad-main/myriad/neural_ode/node_training.py | # (c) Nikolaus Howe 2021
from __future__ import annotations
import haiku as hk
import jax
import jax.numpy as jnp
import optax
import typing
if typing.TYPE_CHECKING:
from myriad.neural_ode.create_node import NeuralODE
from jax.flatten_util import ravel_pytree
from tqdm import trange
from typing import Callable, Op... | 8,523 | 41.40796 | 116 | py |
myriad | myriad-main/myriad/neural_ode/create_node.py | # (c) 2021 Nikolaus Howe
from pathlib import Path
import haiku as hk
import jax
import jax.numpy as jnp
import optax
import pickle as pkl
from dataclasses import dataclass
from jax import config
from typing import Optional
from myriad.config import HParams, Config, SamplingApproach
from myriad.trajectory_optimizers ... | 7,428 | 40.044199 | 115 | py |
myriad | myriad-main/myriad/nlp_solvers/__init__.py | # (c) 2021 Nikolaus Howe
import jax
import jax.numpy as jnp
import time
from cyipopt import minimize_ipopt
from scipy.optimize import minimize
from typing import Dict
from myriad.config import Config, HParams, NLPSolverType
from myriad.defaults import learning_rates
from myriad.utils import get_state_trajectory_and_c... | 3,500 | 34.363636 | 110 | py |
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