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
value |
|---|---|---|---|---|---|---|
DEAT | DEAT-main/models/pnasnet.py | '''PNASNet in PyTorch.
Paper: Progressive Neural Architecture Search
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class SepConv(nn.Module):
'''Separable Convolution.'''
def __init__(self, in_planes, out_planes, kernel_size, stride):
super(SepConv, self).__init__()
se... | 4,258 | 32.801587 | 105 | py |
DEAT | DEAT-main/models/resnet.py | '''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansi... | 4,218 | 30.721805 | 83 | py |
DEAT | DEAT-main/models/mobilenetv2.py | '''MobileNetV2 in PyTorch.
See the paper "Inverted Residuals and Linear Bottlenecks:
Mobile Networks for Classification, Detection and Segmentation" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
'''expand + depthwise + pointwise'''
def __init... | 3,092 | 34.551724 | 114 | py |
DEAT | DEAT-main/models/vgg.py | '''VGG11/13/16/19 in Pytorch.'''
import torch
import torch.nn as nn
cfg = {
'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512... | 1,442 | 29.0625 | 117 | py |
DEAT | DEAT-main/models/densenet.py | '''DenseNet in PyTorch.'''
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
def __init__(self, in_planes, growth_rate):
super(Bottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, 4*gr... | 3,542 | 31.805556 | 96 | py |
DEAT | DEAT-main/models/googlenet.py | '''GoogLeNet with PyTorch.'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Inception(nn.Module):
def __init__(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
super(Inception, self).__init__()
# 1x1 conv branch
self.b1 = nn.Sequential(
... | 3,221 | 28.833333 | 83 | py |
DEAT | DEAT-main/models/resnext.py | '''ResNeXt in PyTorch.
See the paper "Aggregated Residual Transformations for Deep Neural Networks" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
'''Grouped convolution block.'''
expansion = 2
def __init__(self, in_planes, cardinality=32... | 3,478 | 35.239583 | 129 | py |
DEAT | DEAT-main/models/senet.py | '''SENet in PyTorch.
SENet is the winner of ImageNet-2017. The paper is not released yet.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(... | 4,027 | 32.016393 | 102 | py |
DEAT | DEAT-main/models/shufflenet.py | '''ShuffleNet in PyTorch.
See the paper "ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices" for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class ShuffleBlock(nn.Module):
def __init__(self, groups):
super(ShuffleBlock, self).__init... | 3,542 | 31.209091 | 126 | py |
DEAT | DEAT-main/models/lenet.py | '''LeNet in PyTorch.'''
import torch.nn as nn
import torch.nn.functional as F
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16*5*5, 120)
self.fc2 = nn.Linear... | 699 | 28.166667 | 43 | py |
DEAT | DEAT-main/models/__init__.py | from .vgg import *
from .dpn import *
from .lenet import *
from .senet import *
from .pnasnet import *
from .densenet import *
from .googlenet import *
from .shufflenet import *
from .shufflenetv2 import *
from .resnet import *
from .resnext import *
from .mobilenet import *
from .mobilenetv2 import *
from .efficientne... | 353 | 21.125 | 27 | py |
DEAT | DEAT-main/models/mobilenet.py | '''MobileNet in PyTorch.
See the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
for more details.
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Block(nn.Module):
'''Depthwise conv + Pointwise conv'''
def __init__(self, in_planes, out_... | 2,025 | 31.677419 | 123 | py |
DEAT | DEAT-main/models/dpn.py | '''Dual Path Networks in PyTorch.'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer):
super(Bottleneck, self).__init__()
self.out_planes = out_planes
sel... | 3,562 | 34.989899 | 116 | py |
DEAT | DEAT-main/Positive_Negative_Momentum/pnm_optim/pnm.py |
import math
import torch
from torch.optim.optimizer import Optimizer, required
class PNM(Optimizer):
r"""Implements Positive-Negative Momentum (PNM).
It has be proposed in
`Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve
Generalization`__.
Args:
params (iter... | 3,616 | 39.188889 | 121 | py |
DEAT | DEAT-main/Positive_Negative_Momentum/pnm_optim/__init__.py |
from .pnm import *
from .adapnm import *
del pnm
del adapnm
| 62 | 8 | 21 | py |
DEAT | DEAT-main/Positive_Negative_Momentum/pnm_optim/adapnm.py |
import math
import torch
from torch.optim.optimizer import Optimizer, required
class AdaPNM(Optimizer):
r"""Implements Adaptive Positive-Negative Momentum.
It has be proposed in
`Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve
Generalization`__.
Arguments:
... | 5,725 | 45.177419 | 106 | py |
DEAT | DEAT-main/Positive_Negative_Momentum/model/resnet.py | '''ResNet in PyTorch.
The source code is adopted from:
https://github.com/kuangliu/pytorch-cifar
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBloc... | 4,138 | 34.076271 | 102 | py |
DEAT | DEAT-main/Positive_Negative_Momentum/model/vgg.py | '''VGG11/13/16/19 in Pytorch.
The source code is adopted from:
https://github.com/kuangliu/pytorch-cifar
'''
import torch
import torch.nn as nn
cfg = {
'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M']... | 1,432 | 31.568182 | 117 | py |
DEAT | DEAT-main/Positive_Negative_Momentum/model/densenet.py | '''DenseNet in PyTorch.
The source code is adopted from:
https://github.com/kuangliu/pytorch-cifar
'''
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Bottleneck(nn.Module):
def __init__(self, in_planes, growth_rate):
super(Bottleneck, self).__init__()
self.... | 3,707 | 33.981132 | 96 | py |
DEAT | DEAT-main/Positive_Negative_Momentum/model/googlenet.py | """google net in pytorch
The source code is adopted from:
https://github.com/weiaicunzai/pytorch-cifar100/
[1] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed,
Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich.
Going Deeper with Convolutions
https://arxiv.org/a... | 4,443 | 32.164179 | 94 | py |
DEAT | DEAT-main/Positive_Negative_Momentum/model/__init__.py |
from .resnet import *
from .vgg import *
from .densenet import *
from .googlenet import *
del resnet
del vgg
del densenet
del googlenet
| 139 | 10.666667 | 24 | py |
beta-tcvae | beta-tcvae-master/disentanglement_metrics.py | import math
import os
import torch
from tqdm import tqdm
from torch.utils.data import DataLoader
from torch.autograd import Variable
import lib.utils as utils
from metric_helpers.loader import load_model_and_dataset
from metric_helpers.mi_metric import compute_metric_shapes, compute_metric_faces
def estimate_entropi... | 8,678 | 34.863636 | 112 | py |
beta-tcvae | beta-tcvae-master/vae_quant.py | import os
import time
import math
from numbers import Number
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
import visdom
from torch.autograd import Variable
from torch.utils.data import DataLoader
import lib.dist as dist
import lib.utils as utils
import lib.datasets as dset
from lib.fl... | 18,265 | 36.975052 | 120 | py |
beta-tcvae | beta-tcvae-master/plot_latent_vs_true.py | import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import torch
from torch.autograd import Variable
from torch.utils.data import DataLoader
import brewer2mpl
bmap = brewer2mpl.get_map('Set1', 'qualitative', 3)
colors = bmap.mpl_colors
plt.style.use('ggplot')
... | 8,973 | 36.864979 | 109 | py |
beta-tcvae | beta-tcvae-master/elbo_decomposition.py | import os
import math
from numbers import Number
from tqdm import tqdm
import torch
from torch.autograd import Variable
import lib.dist as dist
import lib.flows as flows
def estimate_entropies(qz_samples, qz_params, q_dist):
"""Computes the term:
E_{p(x)} E_{q(z|x)} [-log q(z)]
and
E_{p(x)} E... | 8,303 | 34.33617 | 109 | py |
beta-tcvae | beta-tcvae-master/metric_helpers/mi_metric.py | import torch
metric_name = 'MIG'
def MIG(mi_normed):
return torch.mean(mi_normed[:, 0] - mi_normed[:, 1])
def compute_metric_shapes(marginal_entropies, cond_entropies):
factor_entropies = [6, 40, 32, 32]
mutual_infos = marginal_entropies[None] - cond_entropies
mutual_infos = torch.sort(mutual_infos... | 882 | 31.703704 | 83 | py |
beta-tcvae | beta-tcvae-master/metric_helpers/loader.py | import torch
import lib.dist as dist
import lib.flows as flows
import vae_quant
def load_model_and_dataset(checkpt_filename):
print('Loading model and dataset.')
checkpt = torch.load(checkpt_filename, map_location=lambda storage, loc: storage)
args = checkpt['args']
state_dict = checkpt['state_dict']
... | 1,550 | 33.466667 | 115 | py |
beta-tcvae | beta-tcvae-master/lib/functions.py | import torch
from torch.autograd import Function
class STHeaviside(Function):
@staticmethod
def forward(ctx, x):
y = torch.zeros(x.size()).type_as(x)
y[x >= 0] = 1
return y
@staticmethod
def backward(ctx, grad_output):
return grad_output
| 290 | 17.1875 | 44 | py |
beta-tcvae | beta-tcvae-master/lib/utils.py | from numbers import Number
import math
import torch
import os
def save_checkpoint(state, save, epoch):
if not os.path.exists(save):
os.makedirs(save)
filename = os.path.join(save, 'checkpt-%04d.pth' % epoch)
torch.save(state, filename)
class AverageMeter(object):
"""Computes and stores the a... | 1,828 | 23.716216 | 75 | py |
beta-tcvae | beta-tcvae-master/lib/datasets.py | import numpy as np
import torch
import torchvision.datasets as datasets
import torchvision.transforms as transforms
class Shapes(object):
def __init__(self, dataset_zip=None):
loc = 'data/dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz'
if dataset_zip is None:
self.dataset_zip = np.loa... | 1,102 | 22.978261 | 75 | py |
beta-tcvae | beta-tcvae-master/lib/dist.py | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from lib.functions import STHeaviside
eps = 1e-8
class Normal(nn.Module):
"""Samples from a Normal distribution using the reparameterization trick.
"""
def __init__(self,... | 8,542 | 32.501961 | 84 | py |
beta-tcvae | beta-tcvae-master/lib/flows.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from lib.dist import Normal
eps = 1e-8
class FactorialNormalizingFlow(nn.Module):
def __init__(self, dim, nsteps):
super(FactorialNormalizingFlow, self).__init__()
self.dim = dim
self.... | 1,405 | 30.244444 | 94 | py |
ewN2HDECAY | ewN2HDECAY-master/CommonFunctions.py | #!/usr/bin/env python
#Filename: CommonFunctions.py
###############################################################################################################
# #
# ... | 3,333 | 55.508475 | 113 | py |
ewN2HDECAY | ewN2HDECAY-master/Config.py | #!/usr/bin/env python
#Filename: Config.py
##################################################################################################################
# #
# ... | 4,403 | 114.894737 | 384 | py |
ewN2HDECAY | ewN2HDECAY-master/setup.py | #!/usr/bin/env python
#Filename: setup.py
###############################################################################################################
# #
# ... | 134,033 | 83.404282 | 397 | py |
ewN2HDECAY | ewN2HDECAY-master/ewN2HDECAY.py | #!/usr/bin/env python
#Filename: ewN2HDECAY.py
#################################################################################################################################
# #
# ... | 16,960 | 53.362179 | 143 | py |
applesoss | applesoss-main/setup.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from setuptools import setup
setup(name='applesoss',
version='2.0.0',
license='MIT',
author='Michael Radica',
author_email='michael.radica@umontreal.ca',
packages=['applesoss'],
include_package_data=True,
url='https://github.com/r... | 882 | 31.703704 | 71 | py |
applesoss | applesoss-main/applesoss/applesoss_utils.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thurs Mar 11 14:35 2020
@author: MCR
Miscellaneous utility functions for applesoss.
"""
from astropy.io import fits
from datetime import datetime
import numpy as np
import warnings
try:
import webbpsf
except ModuleNotFoundError:
print('WebbPSF not... | 14,004 | 33.925187 | 107 | py |
applesoss | applesoss-main/applesoss/plotting.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 22 12:03 2021
@author: MCR
File containing all diagnostic plotting functions for the applesoss.
"""
import matplotlib.pyplot as plt
import numpy as np
def plot_badpix(clear, mask):
"""Plot the difference between the originanl dataframe, and ... | 1,920 | 29.015625 | 78 | py |
applesoss | applesoss-main/applesoss/edgetrigger_centroids.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 04 15:35:32 2020
@author: albert
Functions necessary to locate the centroids of the NIRISS SOSS trace using the
edgetrigger algorithm.
"""
from astropy.io import fits
from matplotlib import colors
import matplotlib.pyplot as plt
import numpy as np... | 42,334 | 32.49288 | 100 | py |
applesoss | applesoss-main/applesoss/__init__.py | 0 | 0 | 0 | py | |
applesoss | applesoss-main/applesoss/edgetrigger_utils.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 04 15:36:22 2020
@author: albert, MCR
Utility functions for the edgetrigger algorithm
"""
import numpy as np
from scipy.optimize import least_squares
def zero_roll(a, shift):
"""Like np.roll but the wrapped around part is set to zero. Only w... | 6,141 | 29.557214 | 79 | py |
applesoss | applesoss-main/applesoss/applesoss.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 19 11:46 2021
@author: MCR
Definitions of the main functions for the applesoss (A Producer of ProfiLEs
for SOSS) module. This class will be initialized and called by the user to
create models of the spatial profiles for the first, second, and third... | 31,493 | 39.222222 | 122 | py |
gunpowder | gunpowder-master/gunpowder/roi.py | from funlib.geometry import Roi # noqa
| 40 | 19.5 | 39 | py |
gunpowder | gunpowder-master/gunpowder/provider_spec.py | import math
from gunpowder.coordinate import Coordinate
from gunpowder.array import ArrayKey
from gunpowder.array_spec import ArraySpec
from gunpowder.graph import GraphKey
from gunpowder.graph_spec import GraphSpec
from gunpowder.roi import Roi
from .freezable import Freezable
import time
import logging
import copy
l... | 7,634 | 29.662651 | 88 | py |
gunpowder | gunpowder-master/gunpowder/array.py | from .freezable import Freezable
from copy import deepcopy
from gunpowder.coordinate import Coordinate
from gunpowder.roi import Roi
import logging
import numpy as np
import copy
logger = logging.getLogger(__name__)
class Array(Freezable):
"""A numpy array with a specification describing the data.
Args:
... | 6,344 | 29.07109 | 86 | py |
gunpowder | gunpowder-master/gunpowder/batch.py | from copy import copy as shallow_copy
import logging
import multiprocessing
import warnings
from .freezable import Freezable
from .profiling import ProfilingStats
from .array import Array, ArrayKey
from .graph import Graph, GraphKey
logger = logging.getLogger(__name__)
class Batch(Freezable):
"""Contains the re... | 6,565 | 29.398148 | 85 | py |
gunpowder | gunpowder-master/gunpowder/profiling.py | import copy
import numpy as np
import time
from .freezable import Freezable
class Timing(Freezable):
def __init__(self, node, method_name=None):
self.__name = type(node).__name__
self.__method_name = method_name
self.__start = 0
self.__first_start = 0
self.__last_stop = 0
... | 3,969 | 26.762238 | 89 | py |
gunpowder | gunpowder-master/gunpowder/graph.py | from .graph_spec import GraphSpec
from .roi import Roi
from .freezable import Freezable
import numpy as np
import networkx as nx
from copy import deepcopy
from typing import Dict, Optional, Set, Iterator, Any
import logging
import itertools
import warnings
logger = logging.getLogger(__name__)
class Node(Freezable... | 20,695 | 30.028486 | 102 | py |
gunpowder | gunpowder-master/gunpowder/array_spec.py | import copy
from .coordinate import Coordinate
from .freezable import Freezable
class ArraySpec(Freezable):
"""Contains meta-information about an array. This is used by
:class:`BatchProviders<BatchProvider>` to communicate the arrays they
offer, as well as by :class:`Arrays<Array>` to describe the data th... | 3,293 | 30.075472 | 88 | py |
gunpowder | gunpowder-master/gunpowder/ndarray.py | import numpy as np
def replace(array, old_values, new_values):
"""Replace all occurences of ``old_values[i]`` with ``new_values[i]`` in the
given array."""
old_values = np.array(old_values)
new_values = np.array(new_values)
values_map = np.arange(int(array.max() + 1), dtype=new_values.dtype)
... | 387 | 24.866667 | 80 | py |
gunpowder | gunpowder-master/gunpowder/freezable.py | class Freezable(object):
__isfrozen = False
def __setattr__(self, key, value):
if self.__isfrozen and not hasattr(self, key):
raise TypeError("%r is frozen, you can't add attributes to it" % self)
object.__setattr__(self, key, value)
def freeze(self):
self.__isfrozen = ... | 378 | 26.071429 | 82 | py |
gunpowder | gunpowder-master/gunpowder/batch_request.py | import copy
from .provider_spec import ProviderSpec
from .roi import Roi
from .array import ArrayKey
from .array_spec import ArraySpec
from .graph import GraphKey
from .graph_spec import GraphSpec
from warnings import warn
import time
class BatchRequest(ProviderSpec):
"""A collection of (possibly partial) :class... | 4,468 | 28.596026 | 81 | py |
gunpowder | gunpowder-master/gunpowder/__init__.py | from __future__ import absolute_import
from .nodes import *
from .array import Array, ArrayKey, ArrayKeys
from .array_spec import ArraySpec
from .batch import Batch
from .batch_request import BatchRequest
from .build import build
from .coordinate import Coordinate
from .graph import Graph, Node, Edge, GraphKey, Graph... | 648 | 27.217391 | 57 | py |
gunpowder | gunpowder-master/gunpowder/morphology.py | import numpy as np
from scipy.ndimage.morphology import distance_transform_edt
def enlarge_binary_map(
binary_map, radius, voxel_size, ring_fraction=None, in_place=False
):
"""Enlarge existing regions in a binary map.
Args:
binary_map (numpy array):
A matrix with zeros, in which reg... | 3,244 | 28.5 | 79 | py |
gunpowder | gunpowder-master/gunpowder/compat.py | import sys
PY2 = sys.version_info[0] == 2
if PY2:
binary_type = str
else:
binary_type = bytes
def ensure_str(s):
if PY2:
if isinstance(s, buffer):
s = str(s)
else:
if isinstance(s, memoryview):
s = s.tobytes()
if isinstance(s, binary_type):
... | 356 | 16 | 38 | py |
gunpowder | gunpowder-master/gunpowder/version_info.py | __major__ = 1
__minor__ = 3
__patch__ = 0
__tag__ = ""
__version__ = "{}.{}.{}{}".format(__major__, __minor__, __patch__, __tag__).strip(".")
class _Version(object):
def major(self):
return __major__
def minor(self):
return __minor__
def patch(self):
return __patch__
def tag... | 528 | 15.53125 | 86 | py |
gunpowder | gunpowder-master/gunpowder/coordinate.py | from funlib.geometry import Coordinate # noqa
| 47 | 23 | 46 | py |
gunpowder | gunpowder-master/gunpowder/pipeline.py | import logging
from gunpowder.nodes import BatchProvider
logger = logging.getLogger(__name__)
class PipelineSetupError(Exception):
def __init__(self, provider):
self.provider = provider
def __str__(self):
return f"Exception in {self.provider.name()} while calling setup()"
class PipelineTea... | 5,738 | 27.270936 | 86 | py |
gunpowder | gunpowder-master/gunpowder/build.py | import logging
logger = logging.getLogger(__name__)
class build(object):
def __init__(self, pipeline):
self.pipeline = pipeline
def __enter__(self):
try:
self.pipeline.setup()
except:
logger.error(
"something went wrong during the setup of the ... | 702 | 26.038462 | 90 | py |
gunpowder | gunpowder-master/gunpowder/graph_spec.py | import numpy as np
import copy
from .freezable import Freezable
class GraphSpec(Freezable):
"""Contains meta-information about a graph. This is used by
:class:`BatchProviders<BatchProvider>` to communicate the graphs they
offer, as well as by :class:`Graph` to describe the data they contain.
Attrib... | 2,002 | 26.438356 | 85 | py |
gunpowder | gunpowder-master/gunpowder/producer_pool.py | try:
import Queue
except:
import queue as Queue
import logging
import multiprocessing
import os
import sys
import time
import traceback
import numpy as np
logger = logging.getLogger(__name__)
class NoResult(Exception):
pass
class ParentDied(Exception):
pass
class WorkersDied(Exception):
pass... | 4,825 | 27.388235 | 83 | py |
gunpowder | gunpowder-master/gunpowder/torch/__init__.py | from __future__ import absolute_import
from .nodes import *
| 61 | 14.5 | 38 | py |
gunpowder | gunpowder-master/gunpowder/torch/nodes/__init__.py | from __future__ import absolute_import
from .train import Train
from .predict import Predict
__all__ = ["Train", "Predict"]
| 126 | 17.142857 | 38 | py |
gunpowder | gunpowder-master/gunpowder/torch/nodes/predict.py | from gunpowder.array import ArrayKey, Array
from gunpowder.array_spec import ArraySpec
from gunpowder.ext import torch
from gunpowder.nodes.generic_predict import GenericPredict
import logging
from typing import Dict, Union
logger = logging.getLogger(__name__)
class Predict(GenericPredict):
"""Torch implementat... | 5,622 | 34.815287 | 83 | py |
gunpowder | gunpowder-master/gunpowder/torch/nodes/train.py | import logging
import numpy as np
from gunpowder.array import ArrayKey, Array
from gunpowder.array_spec import ArraySpec
from gunpowder.ext import torch, tensorboardX, NoSuchModule
from gunpowder.nodes.generic_train import GenericTrain
from typing import Dict, Union, Optional
logger = logging.getLogger(__name__)
c... | 13,024 | 36.002841 | 96 | py |
gunpowder | gunpowder-master/gunpowder/zoo/__init__.py | 0 | 0 | 0 | py | |
gunpowder | gunpowder-master/gunpowder/zoo/tensorflow/unet.py | import tensorflow as tf
def conv_pass(
fmaps_in,
kernel_size,
num_fmaps,
num_repetitions,
activation="relu",
name="conv_pass",
):
"""Create a convolution pass::
f_in --> f_1 --> ... --> f_n
where each ``-->`` is a convolution followed by a (non-linear) activation
function... | 6,758 | 24.996154 | 88 | py |
gunpowder | gunpowder-master/gunpowder/zoo/tensorflow/__init__.py | from .unet import unet, conv_pass
| 34 | 16.5 | 33 | py |
gunpowder | gunpowder-master/gunpowder/jax/generic_jax_model.py | class GenericJaxModel:
"""An interface for models to follow in order to train or predict. A model
implementing this interface will need to contain not only the forward
model but also loss and update fn. Some examples can be found in
https://github.com/funkelab/funlib.learn.jax
Args:
is_tra... | 2,633 | 25.34 | 78 | py |
gunpowder | gunpowder-master/gunpowder/jax/__init__.py | from .generic_jax_model import GenericJaxModel
from .nodes import *
| 68 | 22 | 46 | py |
gunpowder | gunpowder-master/gunpowder/jax/nodes/__init__.py | from .train import Train
from .predict import Predict
__all__ = ["Train", "Predict"]
| 86 | 16.4 | 30 | py |
gunpowder | gunpowder-master/gunpowder/jax/nodes/predict.py | from gunpowder.array import ArrayKey, Array
from gunpowder.array_spec import ArraySpec
from gunpowder.ext import jax
from gunpowder.nodes.generic_predict import GenericPredict
from gunpowder.jax import GenericJaxModel
import pickle
import logging
from typing import Dict, Union
logger = logging.getLogger(__name__)
c... | 3,564 | 32.317757 | 88 | py |
gunpowder | gunpowder-master/gunpowder/jax/nodes/train.py | import logging
import numpy as np
from gunpowder.ext import jax
from gunpowder.ext import jnp
import pickle
import os
from gunpowder.array import ArrayKey, Array
from gunpowder.array_spec import ArraySpec
from gunpowder.ext import tensorboardX, NoSuchModule
from gunpowder.nodes.generic_train import GenericTrain
from g... | 11,796 | 36.690096 | 99 | py |
gunpowder | gunpowder-master/gunpowder/nodes/unsqueeze.py | import copy
from typing import List
import logging
import numpy as np
from gunpowder.array import ArrayKey
from gunpowder.batch import Batch
from gunpowder.batch_request import BatchRequest
from .batch_filter import BatchFilter
logger = logging.getLogger(__name__)
class Unsqueeze(BatchFilter):
"""Unsqueeze a ... | 1,757 | 29.310345 | 82 | py |
gunpowder | gunpowder-master/gunpowder/nodes/squeeze.py | import copy
from typing import List
import logging
import numpy as np
from gunpowder.array import ArrayKey
from gunpowder.batch_request import BatchRequest
from gunpowder.batch import Batch
from .batch_filter import BatchFilter
logger = logging.getLogger(__name__)
class Squeeze(BatchFilter):
"""Squeeze a batch... | 1,832 | 30.067797 | 79 | py |
gunpowder | gunpowder-master/gunpowder/nodes/intensity_augment.py | import numpy as np
from gunpowder.batch_request import BatchRequest
from .batch_filter import BatchFilter
class IntensityAugment(BatchFilter):
"""Randomly scale and shift the values of an intensity array.
Args:
array (:class:`ArrayKey`):
The intensity array to modify.
scale_m... | 3,352 | 30.632075 | 101 | py |
gunpowder | gunpowder-master/gunpowder/nodes/elastic_augment.py | import logging
import math
import numpy as np
import random
from scipy import ndimage
from .batch_filter import BatchFilter
from gunpowder.batch_request import BatchRequest
from gunpowder.coordinate import Coordinate
from gunpowder.ext import augment
from gunpowder.roi import Roi
from gunpowder.array import ArrayKey
... | 24,005 | 36.924171 | 96 | py |
gunpowder | gunpowder-master/gunpowder/nodes/reject.py | import logging
import random
from .batch_filter import BatchFilter
from gunpowder.profiling import Timing
logger = logging.getLogger(__name__)
class Reject(BatchFilter):
"""Reject batches based on the masked-in vs. masked-out ratio.
If a pipeline also contains a :class:`RandomLocation` node,
:class:`Re... | 4,419 | 31.740741 | 88 | py |
gunpowder | gunpowder-master/gunpowder/nodes/astype.py | from .batch_filter import BatchFilter
from gunpowder.array import ArrayKey, Array
from gunpowder.batch import Batch
import logging
logger = logging.getLogger(__name__)
class AsType(BatchFilter):
"""Cast arrays to a different datatype (ex: np.float32 --> np.uint8).
Args:
source (:class:`ArrayKey`):
... | 1,628 | 24.857143 | 73 | py |
gunpowder | gunpowder-master/gunpowder/nodes/csv_points_source.py | import numpy as np
import logging
from gunpowder.batch import Batch
from gunpowder.coordinate import Coordinate
from gunpowder.nodes.batch_provider import BatchProvider
from gunpowder.graph import Node, Graph
from gunpowder.graph_spec import GraphSpec
from gunpowder.profiling import Timing
from gunpowder.roi import Roi... | 4,231 | 31.553846 | 86 | py |
gunpowder | gunpowder-master/gunpowder/nodes/klb_source.py | import copy
import logging
import numpy as np
import glob
from gunpowder.batch import Batch
from gunpowder.coordinate import Coordinate
from gunpowder.ext import pyklb
from gunpowder.profiling import Timing
from gunpowder.roi import Roi
from gunpowder.array import Array
from gunpowder.array_spec import ArraySpec
from ... | 6,603 | 31.372549 | 82 | py |
gunpowder | gunpowder-master/gunpowder/nodes/resample.py | from .batch_filter import BatchFilter
from gunpowder.array import ArrayKey, Array
from gunpowder.batch_request import BatchRequest
from gunpowder.batch import Batch
from gunpowder.coordinate import Coordinate
from gunpowder.roi import Roi
from skimage.transform import rescale
import numpy as np
import logging
logger =... | 5,299 | 36.323944 | 232 | py |
gunpowder | gunpowder-master/gunpowder/nodes/noise_augment.py | import numpy as np
import skimage
from gunpowder.batch_request import BatchRequest
from .batch_filter import BatchFilter
class NoiseAugment(BatchFilter):
"""Add random noise to an array. Uses the scikit-image function skimage.util.random_noise.
See scikit-image documentation for more information on argument... | 2,041 | 30.90625 | 109 | py |
gunpowder | gunpowder-master/gunpowder/nodes/renumber_connected_components.py | from .batch_filter import BatchFilter
from gunpowder.ext import malis
class RenumberConnectedComponents(BatchFilter):
"""Find connected components of the same value, and replace each component
with a new label.
Args:
labels (:class:`ArrayKey`):
The label array to modify.
"""
... | 863 | 27.8 | 78 | py |
gunpowder | gunpowder-master/gunpowder/nodes/batch_provider.py | import numpy as np
import copy
import logging
import random
from gunpowder.coordinate import Coordinate
from gunpowder.provider_spec import ProviderSpec
from gunpowder.array import ArrayKey
from gunpowder.array_spec import ArraySpec
from gunpowder.graph import GraphKey
from gunpowder.graph_spec import GraphSpec
logg... | 13,607 | 33.105263 | 106 | py |
gunpowder | gunpowder-master/gunpowder/nodes/iterate_locations.py | import logging
import multiprocessing as mp
from random import randrange
from .batch_filter import BatchFilter
from gunpowder.batch_request import BatchRequest
from gunpowder.coordinate import Coordinate
from gunpowder.array import Array
from gunpowder.array_spec import ArraySpec
logger = logging.getLogger(__name__)
... | 7,416 | 39.091892 | 88 | py |
gunpowder | gunpowder-master/gunpowder/nodes/crop.py | import copy
import logging
from .batch_filter import BatchFilter
from gunpowder.coordinate import Coordinate
logger = logging.getLogger(__name__)
class Crop(BatchFilter):
"""Limits provided ROIs by either giving a new :class:`Roi` or crop
fractions from either face of the provided ROI.
Args:
k... | 2,655 | 30.619048 | 86 | py |
gunpowder | gunpowder-master/gunpowder/nodes/scan.py | import logging
import multiprocessing
import numpy as np
import tqdm
from gunpowder.array import Array
from gunpowder.batch import Batch
from gunpowder.coordinate import Coordinate
from gunpowder.graph import Graph
from gunpowder.producer_pool import ProducerPool
from gunpowder.roi import Roi
from .batch_filter import ... | 13,722 | 33.65404 | 88 | py |
gunpowder | gunpowder-master/gunpowder/nodes/exclude_labels.py | import logging
import numpy as np
from scipy.ndimage.morphology import distance_transform_edt
from .batch_filter import BatchFilter
from gunpowder.array import Array
logger = logging.getLogger(__name__)
class ExcludeLabels(BatchFilter):
"""Excludes several labels from the ground-truth.
The labels will be r... | 3,596 | 33.257143 | 88 | py |
gunpowder | gunpowder-master/gunpowder/nodes/dvid_source.py | import logging
import numpy as np
from gunpowder.batch import Batch
from gunpowder.coordinate import Coordinate
from gunpowder.ext import dvision
from gunpowder.profiling import Timing
from gunpowder.roi import Roi
from gunpowder.array import Array
from gunpowder.array_spec import ArraySpec
from .batch_provider import... | 8,505 | 30.157509 | 85 | py |
gunpowder | gunpowder-master/gunpowder/nodes/merge_provider.py | from gunpowder.provider_spec import ProviderSpec
from gunpowder.batch import Batch
from gunpowder.batch_request import BatchRequest
from .batch_provider import BatchProvider
import random
class MergeProvider(BatchProvider):
"""Merges different providers::
(a, b, c) + MergeProvider()
will create a ... | 2,498 | 36.298507 | 106 | py |
gunpowder | gunpowder-master/gunpowder/nodes/add_affinities.py | import logging
import numpy as np
from .batch_filter import BatchFilter
from gunpowder.array import Array
from gunpowder.batch_request import BatchRequest
from gunpowder.batch import Batch
from gunpowder.coordinate import Coordinate
logger = logging.getLogger(__name__)
def seg_to_affgraph(seg, nhood):
nhood = n... | 10,113 | 34.738516 | 88 | py |
gunpowder | gunpowder-master/gunpowder/nodes/daisy_request_blocks.py | from gunpowder.batch import Batch
from gunpowder.ext import daisy
from gunpowder.nodes.batch_filter import BatchFilter
from gunpowder.roi import Roi
import logging
import multiprocessing
import time
logger = logging.getLogger(__name__)
class DaisyRequestBlocks(BatchFilter):
"""Iteratively requests batches simila... | 4,453 | 34.349206 | 86 | py |
gunpowder | gunpowder-master/gunpowder/nodes/generic_predict.py | import logging
import multiprocessing
import time
from gunpowder.nodes.batch_filter import BatchFilter
from gunpowder.producer_pool import ProducerPool, WorkersDied, NoResult
from gunpowder.array import ArrayKey
from gunpowder.array_spec import ArraySpec
from gunpowder.batch_request import BatchRequest
from queue impo... | 7,420 | 33.840376 | 88 | py |
gunpowder | gunpowder-master/gunpowder/nodes/rasterize_graph.py | import copy
import logging
import numpy as np
from scipy.ndimage.filters import gaussian_filter
from skimage import draw
from .batch_filter import BatchFilter
from gunpowder.array import Array
from gunpowder.array_spec import ArraySpec
from gunpowder.batch_request import BatchRequest
from gunpowder.coordinate import C... | 15,235 | 36.07056 | 94 | py |
gunpowder | gunpowder-master/gunpowder/nodes/deform_augment.py | from .batch_filter import BatchFilter
from gunpowder.batch import Batch
from gunpowder.batch_request import BatchRequest
from gunpowder.coordinate import Coordinate
from gunpowder.roi import Roi
from gunpowder.array import ArrayKey, Array
from gunpowder.array_spec import ArraySpec
from augment.transform import (
c... | 24,947 | 37.205207 | 138 | py |
gunpowder | gunpowder-master/gunpowder/nodes/snapshot.py | import logging
import numpy as np
import os
from .batch_filter import BatchFilter
from gunpowder.batch_request import BatchRequest
from gunpowder.ext import h5py
from gunpowder.ext import ZarrFile
logger = logging.getLogger(__name__)
class Snapshot(BatchFilter):
"""Save a passing batch in an HDF file.
The ... | 8,947 | 34.367589 | 85 | py |
gunpowder | gunpowder-master/gunpowder/nodes/simple_augment.py | import logging
import random
import itertools
import numpy as np
from .batch_filter import BatchFilter
from gunpowder.coordinate import Coordinate
logger = logging.getLogger(__name__)
class SimpleAugment(BatchFilter):
"""Randomly mirror and transpose all :class:`Arrays<Array>` and
:class:`Graph` in a batch... | 11,423 | 37.857143 | 88 | py |
gunpowder | gunpowder-master/gunpowder/nodes/grow_boundary.py | import numpy as np
from scipy import ndimage
from .batch_filter import BatchFilter
from gunpowder.array import Array
class GrowBoundary(BatchFilter):
"""Grow a boundary between regions in a label array. Does not grow at the
border of the batch or an optionally provided mask.
Args:
labels (:clas... | 3,510 | 32.438095 | 80 | py |
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