x7 | Speedy self-documenting lisp in Rust | Learning library
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kandi X-RAY | x7 Summary
Speedy self-documenting lisp in Rust.
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QUESTION
In my iOS app "Progression" there is rarely a crash (1 crash in ~1000+ Sessions) I am currently not able to fix. The message is
Progression: protocol witness for TrainingSetSessionManager.update(object:weight:reps:) in conformance TrainingSetSessionDataManager + 40
This crash points me to the following method:
...ANSWER
Answered 2021-Jun-15 at 13:26While editing my initial question to add more context as Jay proposed I think it found the issue.
What probably happens? The view where the crash is, contains a table view. Each cell will be configured before being presented. I use a flag which holds the information, if the amount of weight for this cell (it is a strength workout app) has been initially set or is a change. When prepareForReuse is being called, this flag has not been reset. And that now means scrolling through the table view triggers a DB write for each reused cell, that leads to unnecessary writes to the db. Unnecessary, because the exact same number is already saved in the db.
My speculation: Scrolling fast could maybe lead to a race condition (I have read something about that issue with realm) and that maybe causes this weird crash, because there are multiple single writes initiated in a short time.
Solution: I now reset the flag on prepareForReuse to its initial value to prevent this misbehaviour.
The crash only happens when the cell is set up and the described behaviour happens. Therefor I'm quite confident I fixed the issue finally. Let's see. -- I was not able to reproduce the issue, but it also only happens pretty rare.
QUESTION
I am using the ECK operator, to create an Elasticsearch instance.
The instance uses a StorageClass that has Retain (instead of Delete) as its reclaim policy.
Here are my PVCs before deleting the Elasticsearch instance
ANSWER
Answered 2021-Jun-14 at 15:38with the hope that due to the Retain policy, the new pods (i.e. their PVCs would bind to the existing PVs (and data wouldn't get lost)
It is explicitly written in the documentation that this is not what happens. the PVs are not available for another PVC after delete of a PVC.
the PersistentVolume still exists and the volume is considered "released". But it is not yet available for another claim because the previous claimant's data remains on the volume.
QUESTION
I have two selects, I want to configure them so that only a certain amount of options show in the second select, depending on which first selection is chosen.
I found some code from this post and I've tried to edit it for my situation, as I need too keep the values as they are, because I'm using them in a calculator that needs them like that.
If any one could help me fix/finish this code so it works, it would be much appreciated!
What I'm trying to achieve:
- If the user selects combo-x1, bench-x1 option only shows
- If the user selects combo-x2, bench-x1 option + bench-x2 option only shows
- If the user selects combo-x3, bench-x1 option + bench-x2 + bench-x3 option only shows
- If the user selects combo-x4 up to combo-8, all options show
Here is the JSFiddle: https://jsfiddle.net/mbxz186q/
But here is the code so far as well:
...ANSWER
Answered 2021-Jun-14 at 02:05Don't need jquery or complex javascript for this, most of it can be done via css:
QUESTION
What would be the easiest way (if possible with tidyverse) to multiply each columns x1:x10 with their respective vector. For example: the first row of the new table would be: age = "one", x1 = x1 * 1, x2 = x2 * 2, x3 = x3 * 9, x4 = x4 * 4...etc
...ANSWER
Answered 2021-Jun-12 at 03:48Here is a {tidyverse} approach using c_across(). The result is a list column which can be extracted using unnest().
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
sigmoidactivation at the end as the pixels will have value between0and1(unless normalized) - Use
torch.nn.MSELossfor 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
sigmoidis gaussian standard normal, hence it models the probability of many real-life occurring phenomena (see also here for more)
QUESTION
I have a command that finds the oldest and newest users in a server. It checks through every user, until it find all the nessesary information. It works fine until I use it in a server with 2000 users takes like 10 seconds to process and I can't use any other command with the bot furring that time. Code of the command:
...ANSWER
Answered 2021-Jun-09 at 15:40I am not 100% sure if it is faster as I can't test it myself on a big server, but you could try to get every member along with their created_at date, append it to a list and then sort the list by the created_at date.
Very simplified example:
QUESTION
**I use Flutter Downloader Package After complete download some file , my app closes automatically and disconnecte to the android studio. Any one help me to find soltutions.
...ANSWER
Answered 2021-Jun-07 at 08:14Maybe it late but it may help others. Recently I faced this error and I solved it. Your UI is rendering in Main isolate and your download events come from background isolate. Because codes in callback are run in the background isolate, so you have to handle the communication between two isolates. Usually, communication needs to take place to show download progress in the main UI. Implement the below code to handle communication:
QUESTION
I have these two variables (itemname and name) which contain this data. I want to subset only those names from name variable where the name is equal to Itemname. The output should look like this because it only subsets those values from Itemname which are present in name.
...ANSWER
Answered 2021-Jun-06 at 19:55If you make your variable Itemname a vector e.g. Itemname <- c("X3", "X5", "X7", "X57", "X66", "X69"), you can do the following:
QUESTION
I have the following code from the example folder with the exception that I added @profile. I am just trying to make this example run because in my code which is more complex I have the same error and I would like to know how much memory is used on each line.
SYSTEM:
Python: 3.9
memory-profiler: 0.58
OS: Manjaro
CODE:
...ANSWER
Answered 2021-Jun-02 at 15:32The docs for memory_profiler : https://pypi.org/project/memory-profiler/ say the following if you use the decorator (@profile):
In this case the script can be run without specifying -m memory_profiler in the command line.
So I think you just need to run python MyScript.py
QUESTION
I'm currently trying to compare the column names of two data frames (ex. df1 and df2) and extract the values from one of them (df2), if there is a match, to create a new (third) data frame.
Example,
...ANSWER
Answered 2021-Jun-01 at 14:14this would work:
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