signify | OpenBSD tool to sign and verify signatures | Cryptography library
kandi X-RAY | signify Summary
kandi X-RAY | signify Summary
OpenBSD tool to sign and verify signatures on files. This is a portable version which uses libbsd (version 0.8 or newer is required). See for more information.
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signify Key Features
signify Examples and Code Snippets
def _connect_nodes(self, first, second):
"""Connects nodes to signify that control flows from first to second.
Args:
first: Union[Set[Node, ...], Node]
second: Node
"""
if isinstance(first, Node):
first.next.add(sec
Community Discussions
Trending Discussions on signify
QUESTION
import torch
import torch.nn as nn
import torch.nn.functional as F
class double_conv(nn.Module):
'''(conv => BN => ReLU) * 2'''
def __init__(self, in_ch, out_ch):
super(double_conv, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(in_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True),
nn.Conv2d(out_ch, out_ch, 3, padding=1),
nn.BatchNorm2d(out_ch),
nn.ReLU(inplace=True)
)
def forward(self, x):
x = self.conv(x)
return x
class inconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(inconv, self).__init__()
self.conv = double_conv(in_ch, out_ch)
def forward(self, x):
x = self.conv(x)
return x
class down(nn.Module):
def __init__(self, in_ch, out_ch):
super(down, self).__init__()
self.mpconv = nn.Sequential(
nn.MaxPool2d(2),
double_conv(in_ch, out_ch)
)
def forward(self, x):
x = self.mpconv(x)
return x
class up(nn.Module):
def __init__(self, in_ch, out_ch, bilinear=True):
super(up, self).__init__()
# would be a nice idea if the upsampling could be learned too,
# but my machine do not have enough memory to handle all those weights
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
else:
self.up = nn.ConvTranspose2d(in_ch//2, in_ch//2, 2, stride=2)
self.conv = double_conv(in_ch, out_ch)
def forward(self, x1, x2):
x1 = self.up(x1)
diffX = x1.size()[2] - x2.size()[2]
diffY = x1.size()[3] - x2.size()[3]
x2 = F.pad(x2, (diffX // 2, int(diffX / 2),
diffY // 2, int(diffY / 2)))
x = torch.cat([x2, x1], dim=1)
x = self.conv(x)
return x
class outconv(nn.Module):
def __init__(self, in_ch, out_ch):
super(outconv, self).__init__()
self.conv = nn.Conv2d(in_ch, out_ch, 1)
def forward(self, x):
x = self.conv(x)
return x
class UNet(nn.Module):
def __init__(self, n_channels, n_classes):
super(UNet, self).__init__()
self.inc = inconv(n_channels, 64)
self.down1 = down(64, 128)
self.down2 = down(128, 256)
self.down3 = down(256, 512)
self.down4 = down(512, 512)
self.up1 = up(1024, 256)
self.up2 = up(512, 128)
self.up3 = up(256, 64)
self.up4 = up(128, 64)
self.outc = outconv(64, n_classes)
def forward(self, x):
self.x1 = self.inc(x)
self.x2 = self.down1(self.x1)
self.x3 = self.down2(self.x2)
self.x4 = self.down3(self.x3)
self.x5 = self.down4(self.x4)
self.x6 = self.up1(self.x5, self.x4)
self.x7 = self.up2(self.x6, self.x3)
self.x8 = self.up3(self.x7, self.x2)
self.x9 = self.up4(self.x8, self.x1)
self.y = self.outc(self.x9)
return self.y
...ANSWER
Answered 2021-Jun-11 at 09:42Does n_classes signify multiclass segmentation?
Yes, if you specify n_classes=4
it will output a (batch, 4, width, height)
shaped tensor, where each pixel can be segmented as one of 4
classes. Also one should use torch.nn.CrossEntropyLoss
for training.
If so, what is the output of binary UNet segmentation?
If you want to use binary segmentation you'd specify n_classes=1
(either 0
for black or 1
for white) and use torch.nn.BCEWithLogitsLoss
I am trying to use this code for image denoising and I couldn't figure out what will should the n_classes parameter be
It should be equal to n_channels
, usually 3
for RGB or 1
for grayscale. If you want to teach this model to denoise an image you should:
- Add some noise to the image (e.g. using
torchvision.transforms
) - Use
sigmoid
activation at the end as the pixels will have value between0
and1
(unless normalized) - Use
torch.nn.MSELoss
for training
Because [0,255]
pixel range is represented as [0, 1]
pixel value (without normalization at least). sigmoid
does exactly that - squashes value into [0, 1]
range, hence linear
outputs (logits) can have a range from -inf
to +inf
.
Why not a linear output and a clamp?
In order for the Linear layer to be in [0, 1]
range after clamp possible output values from Linear would have to be greater than 0
(logits range to fit the target: [0, +inf]
)
Why not a linear output without a clamp?
Logits outputted would have to be within [0, 1]
range
Why not some other method?
You could do that, but the idea of sigmoid
is:
- help neural network (any logit value can be outputted)
- first derivative of
sigmoid
is gaussian standard normal, hence it models the probability of many real-life occurring phenomena (see also here for more)
QUESTION
I found the following page about columns.format on Kendo UI jQuery API:
https://docs.telerik.com/kendo-ui/api/javascript/ui/grid/configuration/columns.format
In the documentation it give a couple of examples of the format values:
...ANSWER
Answered 2021-Jun-09 at 18:16{0:c}
is the syntax for the kendo.format method. 0
means the first value which you want to format, c
means that it will be formatted as a currency value. :
is just a separator between value position and the format syntax. Here you can see an example with two values passed to kendo.format:
QUESTION
I have a flow where I consume the paths to the files in small batches from Kafka pah topics, read the files themselves(big JSON arrays) and write them back to Kafka data topics.
It looks like this:
...ANSWER
Answered 2021-May-29 at 21:01I'm struck by
...I can't take the entire file content, frame it into separate objects, store them all to Kafka and commit only after that
Since it seems (and you can comment if I'm getting this wrong) that the offset commit is effectively an acknowledgement that you've fully processed a file, there's no way around not committing the offset until after all the objects in the file in the message at that offset have been produced to Kafka.
The downside of Source.via(Flow.flatMapConcat.via(...)).map.via(...)
is that it's a single stream and everything between the first and second via
s, inclusive takes a while.
If you're OK with interleaving objects from files in the output topic and are OK with an unavoidable chance of an object from a given file being produced twice to the output topic (both of these may or may not impose meaningful constraints/difficulties on the implementation of downstream consumers of that topic), you can parallelize the processing of a file. The mapAsync
stream stage is especially useful for this:
QUESTION
I'm trying to come up with a neat/fast way to read files delimited by newline (\n
) characters into more than one column.
Essentially in a given input file, multiple rows in the input file should become a single row in the output, however most file reading functions sensibly interpret the newline character as signifying a new row, and so they end up as a data frame with a single column. Here's an example:
The input files look like this:
...ANSWER
Answered 2021-Apr-12 at 14:59You can get round some of the string manipulation with something along the lines of:
QUESTION
I am doing an exercise from a textbook and I have been stuck for 3 days finally I decided to get help here.
The question is: write a program that reads the contents of a text file. The program should create a dictionary in which the key-value pairs are described as follows:
- Key. The keys are the individual words found in the file.
- Values. Each value is a list that contains the line numbers in the file where the word (the key) is found.
For example: suppose the word “robot” is found in lines 7, 18, 94, and 138. The dictionary would contain an element in which the key was the string “robot”, and the value was a list containing the numbers 7, 18, 94, and 138.
Once the dictionary is built, the program should create another text file, known as a word index, listing the contents of the dictionary. The word index file should contain an alphabetical listing of the words that are stored as keys in the dictionary, along with the line numbers where the words appear in the original file.
Figure 9-1 shows an example of an original text file (Kennedy.txt) and its index file (index.txt).
Here are the code i tried so far and the functions is not completed, not sure what to do next:
...ANSWER
Answered 2021-Jan-02 at 04:13You are on the right track. This is how it can be done
QUESTION
I know two uses of WITH
in SQL:
- To signify a CTE (Common Table Expression) clause, creating a temporary table for use in the present query, and
- To dictate properties in a CTAS (CREATE TABLE AS) statement, e.g. Presto, AWS Athena, Cloudera, etc.
However, in reading long queries, I have on several occasions had diffculty immediately telling these two uses apart, and I always thought to myself if it would have made more sense to use another word for one of the two, to improve readability and avoid ambiguity.
So my question is: are these two uses related somehow? Do they stem from some common root?
...ANSWER
Answered 2021-May-22 at 23:00They are not related at all. WITH
is a syntactic construct similar to a subquery. The other is used for other purposes.
An analogy by might the BY
in GROUP BY
and ORDER BY
. Or the AND
used for BETWEEN
and as a stand-alone boolean operator. They just happen to have the same name.
QUESTION
I would like to split a string in word boundaries and hence for now I am considering that whitespace, a ',' and a '.' or '!' signify the boundaries of words.
In the following example:
ANSWER
Answered 2021-May-18 at 21:13"[\\s+,.!]"
doesn't do what you think it does. Inside of []
s, +
is treated as a literal character, not the regex special character meaning "one or more".
The empty strings in this first pattern are because substrings like ", "
have an empty string between ","
and " "
.
"[\\s+,.!]+"
works because the regex repetition character +
is in the correct location -- "one or more of any of the characters in the preceding group", i.e. the stuff inside []
before the last +
.
But the +
inside the group is probably not what you want. That would split "foo+bar"
into {"foo", "bar"}
, which appears to be a false positive. Use "[\\s,.!]+"
to mitigate this.
QUESTION
On a linux server when checking the Linux version I see the following "geeko@buildhost"
...ANSWER
Answered 2021-May-17 at 10:52It is simply an identifier showing the user and host names where the kernel is compiled. The former is the result of executing whoami
and the latter is the result of running uname -n
. You can see how it is put together in init/version.c
:
QUESTION
I'm following an online tutorial, but as usual I've gone off-piste and I'm trying to apply the lessons learned to my own project. All is going surprisingly well, however I've hit a problem and I haven't yet been able to find a solution.
There are two problems with this (I mean, I'm sure you can find many more than two...):
In any cells that have a hyperlink in them, the data is replaced with "None". Example, this:
...ANSWER
Answered 2021-May-17 at 02:13You can try doing the following:
QUESTION
I have a dataframe with column consisting of sequences of 0s and 1s. The 0s are not of interest but the 1s signify events occurring in a time series and the goal is to assign a unique value to each event. Simple integer values suffice. So in the code below 'x' is what I have and 'goal' is what I am after.
This seems so simple yet I don't quite know how to phrase the question on a help search...
What I have as a dataframe:
...ANSWER
Answered 2021-May-14 at 13:29This is effectively a run-length encoding, with a slight-twist (of zero-izing 0
s).
While data.table::rleid
does this well, if you are not already using that package, then we'll use
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install signify
Debian: apt install signify-openbsd
Arch Linux: pacman -S signify
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