repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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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/test.py | """
Separated testing for OmiEmbed
"""
import time
from util import util
from params.test_params import TestParams
from datasets import create_single_dataloader
from models import create_model
from util.visualizer import Visualizer
if __name__ == '__main__':
# Get testing parameter
param = TestParams().parse()... | 3,489 | 46.808219 | 120 | py |
SubOmiEmbed | SubOmiEmbed-main/train.py | """
Separated training for OmiEmbed
"""
import time
import warnings
from util import util
from params.train_params import TrainParams
from datasets import create_single_dataloader
from models import create_model
from util.visualizer import Visualizer
if __name__ == "__main__":
warnings.filterwarnings('ignore')
... | 5,827 | 55.038462 | 146 | 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/__init__.py | """
This package contains modules related to objective functions, optimizations, and network architectures.
"""
import importlib
from models.basic_model import BasicModel
def find_model_using_name(model_name):
"""
Import the module with certain name
"""
model_filename = "models." + model_name + "_mod... | 1,425 | 30 | 170 | 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/util/metrics.py | """
Contain some metrics
"""
import numpy as np
# from lifelines.utils import concordance_index
# from pysurvival.utils._metrics import _concordance_index
from sksurv.metrics import concordance_index_censored
from sksurv.metrics import integrated_brier_score
def c_index(true_T, true_E, pred_risk, include_ties=True):
... | 1,617 | 31.36 | 129 | py |
SubOmiEmbed | SubOmiEmbed-main/util/__init__.py | 0 | 0 | 0 | py | |
SubOmiEmbed | SubOmiEmbed-main/util/preprocess.py | """
Contain some omics data preprocess functions
"""
import pandas as pd
def separate_B(B_df_single):
"""
Separate the DNA methylation dataframe into subsets according to their targeting chromosomes
Parameters:
B_df_single(DataFrame) -- a dataframe that contains the single DNA methylation matrix
... | 967 | 30.225806 | 96 | py |
SubOmiEmbed | SubOmiEmbed-main/params/train_test_params.py | from .basic_params import BasicParams
class TrainTestParams(BasicParams):
"""
This class is a son class of BasicParams.
This class includes parameters for training & testing and parameters inherited from the father class.
"""
def initialize(self, parser):
parser = BasicParams.initialize(se... | 2,954 | 52.727273 | 130 | 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/params/train_params.py | from .basic_params import BasicParams
class TrainParams(BasicParams):
"""
This class is a son class of BasicParams.
This class includes parameters for training and parameters inherited from the father class.
"""
def initialize(self, parser):
parser = BasicParams.initialize(self, parser)
... | 2,769 | 53.313725 | 130 | py |
SubOmiEmbed | SubOmiEmbed-main/params/test_params.py | from .basic_params import BasicParams
class TestParams(BasicParams):
"""
This class is a son class of BasicParams.
This class includes parameters for testing and parameters inherited from the father class.
"""
def initialize(self, parser):
parser = BasicParams.initialize(self, parser)
... | 727 | 32.090909 | 123 | py |
SubOmiEmbed | SubOmiEmbed-main/params/__init__.py | 0 | 0 | 0 | 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/default_config.py | from yacs.config import CfgNode as CN
def get_default_config():
cfg = CN()
# model
cfg.model = CN()
cfg.model.name = 'resnet50'
cfg.model.pretrained = True # automatically load pretrained model weights if available
cfg.model.load_weights = '' # path to model weights
cfg.model.resume = '' ... | 8,128 | 37.709524 | 104 | 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/__init__.py | 0 | 0 | 0 | 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/reid/models/dropblock/__init__.py | from .dropblock import DropBlock2D, DropBlock3D
from .scheduler import LinearScheduler
__all__ = ['DropBlock2D', 'DropBlock3D', 'LinearScheduler']
| 148 | 28.8 | 59 | 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/parse_test_res.py | """
Goal
---
1. Read test results from log.txt files
2. Compute mean and std across different folders (seeds)
Usage
---
Assume the output files are saved under output/my_experiment,
which contains results of different seeds, e.g.,
my_experiment/
seed1/
log.txt
seed2/
log.txt
seed3/
... | 4,752 | 24.148148 | 77 | 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/datasets/ssdg_officehome.py | import os.path as osp
import glob
import random
from dassl.utils import listdir_nohidden
from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase
from dassl.utils import mkdir_if_missing
from .ssdg_pacs import SSDGPACS
@DATASET_REGISTRY.register()
class SSDGOfficeHome(DatasetBase):
"""Office-Home.
... | 4,583 | 36.884298 | 106 | py |
mixstyle-release | mixstyle-release-master/imcls/datasets/ssdg_pacs.py | import os.path as osp
import random
from collections import defaultdict
from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase
from dassl.utils import mkdir_if_missing, read_json, write_json
@DATASET_REGISTRY.register()
class SSDGPACS(DatasetBase):
"""PACS.
Statistics:
- 4 domains: Pho... | 7,401 | 36.01 | 106 | py |
mixstyle-release | mixstyle-release-master/imcls/datasets/__init__.py | 0 | 0 | 0 | py | |
mixstyle-release | mixstyle-release-master/imcls/datasets/msda_pacs.py | import os.path as osp
from dassl.data.datasets import DATASET_REGISTRY, Datum, DatasetBase
@DATASET_REGISTRY.register()
class MSDAPACS(DatasetBase):
"""PACS.
Modified for multi-source domain adaptation.
Statistics:
- 4 domains: Photo (1,670), Art (2,048), Cartoon
(2,344), Sketch (3,929)... | 3,145 | 33.955556 | 81 | 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 |
mixstyle-release | mixstyle-release-master/imcls/trainers/__init__.py | 0 | 0 | 0 | py | |
mixstyle-release | mixstyle-release-master/rl/setup.py | from setuptools import setup, find_packages
setup(
name='coinrun',
packages=find_packages(),
version='0.0.1',
)
| 125 | 14.75 | 43 | py |
mixstyle-release | mixstyle-release-master/rl/plots.py | import tensorflow as tf
import os
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
sns.set()
sns.set_style("ticks")
params = {'legend.fontsize': 10, 'legend.handlelength': 2,
'font.size': 10}
plt.rcParams.update(params)
def movingaverage (values, window):
weights = np.repeat(1.0... | 8,656 | 39.265116 | 124 | py |
mixstyle-release | mixstyle-release-master/rl/create_gif.py | import imageio
import os
images = []
# for filename in os.listdir(modified_path):
filenames = []
for filename in os.listdir('./images'):
if filename.startswith('img_'):
filenames.append(filename)
sorted_files = [None] * len(filenames)
for filename in filenames:
nr = int(filename[4:-4])
sorted_fi... | 495 | 20.565217 | 46 | py |
mixstyle-release | mixstyle-release-master/rl/create_saliency.py | """
Load an agent trained with train_agent.py and
"""
import time
import tensorflow as tf
import numpy as np
from coinrun import setup_utils
import coinrun.main_utils as utils
from coinrun.config import Config
from coinrun import config
from coinrun import policies, wrappers
import imageio
import sys
# Import for s... | 6,228 | 30.301508 | 145 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/tb_utils.py | import tensorflow as tf
from mpi4py import MPI
from coinrun.config import Config
import numpy as np
def clean_tb_dir():
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
if rank == 0:
if tf.gfile.Exists(Config.TB_DIR):
tf.gfile.DeleteRecursively(Config.TB_DIR)
tf.gfile.MakeDirs(Con... | 2,740 | 30.147727 | 108 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/train_agent.py | """
Train an agent using a PPO2 based on OpenAI Baselines.
"""
import time
from mpi4py import MPI
import tensorflow as tf
from baselines.common import set_global_seeds
import coinrun.main_utils as utils
from coinrun import setup_utils, policies, wrappers, ppo2
from coinrun.config import Config
def main():
args = ... | 1,651 | 27 | 66 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/test_coinrun.py | from coinrun import random_agent
def test_coinrun():
random_agent.random_agent(num_envs=16, max_steps=100)
if __name__ == '__main__':
test_coinrun() | 159 | 19 | 57 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/coinrunenv.py | """
Python interface to the CoinRun shared library using ctypes.
On import, this will attempt to build the shared library.
"""
import os
import atexit
import random
import sys
from ctypes import c_int, c_char_p, c_float, c_bool
import gym
import gym.spaces
import numpy as np
import numpy.ctypeslib as npct
from basel... | 6,996 | 31.09633 | 205 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/ppo2.py | """
This is a copy of PPO from openai/baselines (https://github.com/openai/baselines/blob/52255beda5f5c8760b0ae1f676aa656bb1a61f80/baselines/ppo2/ppo2.py) with some minor changes.
"""
import time
import datetime
import joblib
import numpy as np
import tensorflow as tf
from collections import deque
from mpi4py import ... | 17,289 | 37.59375 | 175 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/setup_utils.py | from coinrun.config import Config
import os
import joblib
def load_for_setup_if_necessary():
print("Restoring from ID: {}".format(Config.RESTORE_ID))
restore_file(Config.RESTORE_ID)
def restore_file(restore_id, load_key='default'):
if restore_id is not None:
load_file = Config.get_load_filename(r... | 1,466 | 30.212766 | 101 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/utils.py | import os
import sys
import time
import os.path as osp
import errno
def mkdir_if_missing(dirname):
"""Create dirname if it is missing."""
if not osp.exists(dirname):
try:
os.makedirs(dirname)
except OSError as e:
if e.errno != errno.EEXIST:
raise
class... | 2,424 | 22.095238 | 90 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/main_utils.py | import tensorflow as tf
import os
import joblib
import numpy as np
from mpi4py import MPI
from baselines.common.vec_env.vec_frame_stack import VecFrameStack
from coinrun.config import Config
from coinrun import setup_utils, wrappers
import platform
def make_general_env(num_env, seed=0, use_sub_proc=True):
from ... | 5,094 | 25.957672 | 89 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/random_agent.py | import numpy as np
from coinrun import setup_utils, make
def random_agent(num_envs=1, max_steps=100000):
setup_utils.setup_and_load(use_cmd_line_args=False)
env = make('standard', num_envs=num_envs)
for step in range(max_steps):
acts = np.array([env.action_space.sample() for _ in range(env.num_env... | 482 | 29.1875 | 81 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/wrappers.py | import gym
import numpy as np
class EpsilonGreedyWrapper(gym.Wrapper):
"""
Wrapper to perform a random action each step instead of the requested action,
with the provided probability.
"""
def __init__(self, env, prob=0.05):
gym.Wrapper.__init__(self, env)
self.prob = prob
s... | 3,145 | 30.148515 | 94 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/config.py | from mpi4py import MPI
import argparse
import os
class ConfigSingle(object):
"""
A global config object that can be initialized from command line arguments or
keyword arguments.
"""
def __init__(self):
self.WORKDIR = './saved_models'
self.TB_DIR = './tb_log'
if not os.path.e... | 12,414 | 35.514706 | 129 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/policies.py | import sys
import numpy as np
import tensorflow as tf
from baselines.a2c.utils import conv, fc, conv_to_fc, batch_to_seq, seq_to_batch, lstm
from baselines.common.distributions import make_pdtype, _matching_fc
from baselines.common.input import observation_input
ds = tf.contrib.distributions
from coinrun.config import... | 10,335 | 39.375 | 150 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/interactive.py | """
Run a CoinRun environment in a window where you can interact with it using the keyboard
"""
from coinrun.coinrunenv import lib
from coinrun import setup_utils
def main():
setup_utils.setup_and_load(paint_vel_info=0)
print("""Control with arrow keys,
F1, F2 -- switch resolution,
F5, F6, F7, F8 -- zoom,
F9... | 457 | 20.809524 | 87 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/__init__.py | from .coinrunenv import init_args_and_threads
from .coinrunenv import make
__all__ = [
'init_args_and_threads',
'make'
]
| 134 | 15.875 | 45 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/enjoy.py | """
Load an agent trained with train_agent.py and
"""
import time
import tensorflow as tf
import numpy as np
import os
from coinrun import setup_utils
import coinrun.main_utils as utils
from coinrun.config import Config
from coinrun import policies, wrappers
mpi_print = utils.mpi_print
def create_act_model(sess, e... | 3,856 | 24.543046 | 99 | py |
mixstyle-release | mixstyle-release-master/rl/coinrun/OldPlots.py | # plotname = "VIB_repeats_{}".format(ending)
# experiments = {
# '0424_vibnn12e4_l2w_uda_{}': "L2W + VIB-SNI (1e-4) + UDA",
# '0424_vibnn12e4_{}': "VIB-SNI (1e-4)",
# '0501_0_vibnn12e4_uda_{}': "VIB-SNI (1e-4) + UDA",
# '0501_1_vibnn12e4_uda_{}': "VIB-SNI (1e-4) + UDA",
# '0501_0_vibnn12e4_l2w_uda_{... | 2,420 | 31.28 | 66 | py |
scipy | scipy-main/setup.py | #!/usr/bin/env python
"""SciPy: Scientific Library for Python
SciPy (pronounced "Sigh Pie") is open-source software for mathematics,
science, and engineering. The SciPy library
depends on NumPy, which provides convenient and fast N-dimensional
array manipulation. The SciPy library is built to work with NumPy
arrays, a... | 20,342 | 37.166979 | 109 | 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/tools/openblas_support.py | import glob
import os
import platform
import sysconfig
import sys
import shutil
import tarfile
import textwrap
import time
import zipfile
from tempfile import mkstemp, gettempdir
from urllib.request import urlopen, Request
from urllib.error import HTTPError
OPENBLAS_V = '0.3.21.dev'
OPENBLAS_LONG = 'v0.3.20-571-g3dec... | 12,716 | 32.465789 | 81 | py |
scipy | scipy-main/tools/check_test_name.py | #!/usr/bin/env python
"""
MIT License
Copyright (c) 2020 Marco Gorelli
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, ... | 5,952 | 34.017647 | 78 | py |
scipy | scipy-main/tools/gh_lists.py | #!/usr/bin/env python3
# -*- encoding:utf-8 -*-
"""
gh_lists.py MILESTONE
Functions for Github API requests.
"""
import os
import re
import sys
import json
import collections
import argparse
import datetime
import time
from urllib.request import urlopen, Request, HTTPError
Issue = collections.namedtuple('Issue', ('... | 7,508 | 30.953191 | 113 | py |
scipy | scipy-main/tools/unicode-check.py | #!/usr/bin/env python
import re
from itertools import chain
from glob import iglob
import sys
import argparse
# The set of Unicode code points greater than 127 that we
# allow in the source code.
latin1_letters = set(chr(cp) for cp in range(192, 256))
box_drawing_chars = set(chr(cp) for cp in range(0x2500, 0x2580))
... | 3,136 | 36.795181 | 79 | py |
scipy | scipy-main/tools/refguide_summaries.py | #!/usr/bin/env python
"""Generate function summaries for the refguide. For example, if the
__init__ file of a submodule contains:
.. autosummary::
:toctree: generated/
foo
foobar
Then it will modify the __init__ file to contain (*)
.. autosummary::
:toctree: generated/
foo -- First line of the do... | 3,456 | 30.144144 | 74 | py |
scipy | scipy-main/tools/check_installation.py | """
Script for checking if all the test files are installed after building.
Examples::
$ python check_installation.py install_directory_name
install_directory_name:
the relative path to the directory where SciPy is installed after
building and running `meson install`.
Notes
=====... | 3,388 | 29.809091 | 80 | py |
scipy | scipy-main/tools/ninjatracing.py | # Copyright 2018 Nico Weber
#
# 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 to in writing, s... | 7,126 | 36.314136 | 81 | py |
scipy | scipy-main/tools/cythonize.py | """cythonize
Cythonize pyx files into C files as needed.
Usage: cythonize [root_dir]
Default [root_dir] is 'scipy'.
The number of parallel Cython processes is controlled by the
environment variable SCIPY_NUM_CYTHONIZE_JOBS. If not set, determined
from the number of CPUs.
Checks pyx files to see if they have been c... | 11,648 | 31.90678 | 79 | py |
scipy | scipy-main/tools/authors.py | #!/usr/bin/env python
# -*- encoding:utf-8 -*-
"""
List the authors who contributed within a given revision interval::
python tools/authors.py REV1..REV2
`REVx` being a commit hash.
To change the name mapping, edit .mailmap on the top-level of the
repository.
"""
# Author: Pauli Virtanen <pav@iki.fi>. This scri... | 7,474 | 30.540084 | 215 | py |
scipy | scipy-main/tools/refguide_check.py | #!/usr/bin/env python3
"""
refguide_check.py [OPTIONS] [-- ARGS]
Check for a Scipy submodule whether the objects in its __all__ dict
correspond to the objects included in the reference guide.
Example of usage::
$ python3 refguide_check.py optimize
Note that this is a helper script to be able to check if things ... | 35,211 | 32.187559 | 101 | py |
scipy | scipy-main/tools/download-wheels.py | #!/usr/bin/env python
"""
Download SciPy wheels from Anaconda staging area.
"""
import os
import re
import shutil
import argparse
import urllib
import urllib.request
import urllib3
from bs4 import BeautifulSoup
__version__ = '0.1'
# Edit these for other projects.
STAGING_URL = 'https://anaconda.org/multibuild-wheel... | 2,978 | 27.92233 | 77 | py |
scipy | scipy-main/tools/generate_f2pymod.py | """
Process f2py template files (`filename.pyf.src` -> `filename.pyf`)
Usage: python generate_pyf.py filename.pyf.src -o filename.pyf
"""
import os
import sys
import re
import subprocess
import argparse
# START OF CODE VENDORED FROM `numpy.distutils.from_template`
###################################################... | 9,372 | 30.989761 | 94 | py |
scipy | scipy-main/tools/write_release_and_log.py | """
Standalone script for writing release doc and logs::
python tools/write_release_and_log.py <LOG_START> <LOG_END>
Example::
python tools/write_release_and_log.py v1.7.0 v1.8.0
Needs to be run from the root of the repository.
"""
import os
import sys
import subprocess
from hashlib import md5
from hashli... | 4,045 | 25.103226 | 79 | py |
scipy | scipy-main/tools/lint.py | #!/usr/bin/env python
import os
import sys
import subprocess
from argparse import ArgumentParser
CONFIG = os.path.join(
os.path.abspath(os.path.dirname(__file__)),
'lint.toml',
)
def rev_list(branch, num_commits):
"""List commits in reverse chronological order.
Only the first `num_commits` are show... | 3,870 | 26.848921 | 87 | py |
scipy | scipy-main/tools/pre-commit-hook.py | #!/usr/bin/env python
#
# Pre-commit linting hook.
#
# Install from root of repository with:
#
# cp tools/pre-commit-hook.py .git/hooks/pre-commit
import subprocess
import sys
import os
# Run lint.py from the scipy source tree
linters = [
'../../tools/lint.py',
'tools/lint.py',
'lint.py' # in case pre... | 2,915 | 31.764045 | 82 | py |
scipy | scipy-main/tools/version_utils.py | import os
import subprocess
import argparse
MAJOR = 1
MINOR = 12
MICRO = 0
ISRELEASED = False
IS_RELEASE_BRANCH = False
VERSION = '%d.%d.%d' % (MAJOR, MINOR, MICRO)
def get_version_info(source_root):
# Adding the git rev number needs to be done inside
# write_version_py(), otherwise the import of scipy.vers... | 4,136 | 33.475 | 83 | py |
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