Fix containers remaining open by vanished player in certain conditions

Non-shulkerbox containers call ContainerOpenersCounter#recheckOpeners, which wasn't accounted for. This means that a Chest (any kind) or a Barrel that was at some point accessed by both visible and invisible players at the same time will remain open until every player, including the vanished ones, closes its GUI.
I'm not sure whether I should also test if the player is a ServerPlayer in the newly added mixin.

Thanks for the PR, didn't even look into implementing this, because spectator has the same bug lol
Checking for ServerPlayer is not neccessary. isVanished will just return false for any non server player


QR Code appears to be overly complex for data entered.

I'm generating a QR code using this package that is a MeCard (kind of like a VCard, but even more basic).
I would anticipate a fairly basic QR code to be generated, but instead, it's a very detailed QR code probably difficult to read in low light. I see 16 orientation squares.
The number of characters in the value string is under 100.
Any one have any clues?

I have the same problem

Updated Generation of QR Code in React Native to react-native-qrcode-svg


nixdoc: 2.7.0 -> 3.0.1

Automatic update generated by nixpkgs-update tools. This update was made based on information from https://github.com/nix-community/nixdoc/releases.
meta.description for nixdoc is: Generate documentation for Nix functions
meta.homepage for nixdoc is: https://github.com/nix-community/nixdoc
Updates performed

Rust version update

To inspect upstream changes

Release on GitHub

Compare changes on GitHub

Impact
Checks done

built on NixOS
The tests defined in passthru.tests, if any, passed
found 3.0.1 with grep in /nix/store/irb0h19d1yili1764yq7ppl14qhbl8ij-nixdoc-3.0.1
found 3.0.1 in filename of file in /nix/store/irb0h19d1yili1764yq7ppl14qhbl8ij-nixdoc-3.0.1

Rebuild report (if merged into master) (click to expand)

1 total rebuild path(s)

1 package rebuild(s)

First fifty rebuilds by attrpath
nixdoc

Instructions to test this update (click to expand)

Either download from Cachix:
nix-store -r /nix/store/irb0h19d1yili1764yq7ppl14qhbl8ij-nixdoc-3.0.1 \
  --option binary-caches 'https://cache.nixos.org/ https://nix-community.cachix.org/' \
  --option trusted-public-keys '
  nix-community.cachix.org-1:mB9FSh9qf2dCimDSUo8Zy7bkq5CX+/rkCWyvRCYg3Fs=
  cache.nixos.org-1:6NCHdD59X431o0gWypbMrAURkbJ16ZPMQFGspcDShjY=
  '

(The Cachix cache is only trusted for this store-path realization.)
For the Cachix download to work, your user must be in the trusted-users list or you can use sudo since root is effectively trusted.
Or, build yourself:
nix-build -A nixdoc https://github.com/r-ryantm/nixpkgs/archive/6010c3009c106db2676f63a420ac0c691e7c8a3d.tar.gz

Or:
nix build github:r-ryantm/nixpkgs/6010c3009c106db2676f63a420ac0c691e7c8a3d#nixdoc

After you've downloaded or built it, look at the files and if there are any, run the binaries:
ls -la /nix/store/irb0h19d1yili1764yq7ppl14qhbl8ij-nixdoc-3.0.1
ls -la /nix/store/irb0h19d1yili1764yq7ppl14qhbl8ij-nixdoc-3.0.1/bin

Pre-merge build results
We have automatically built all packages that will get rebuilt due to
this change.
This gives evidence on whether the upgrade will break dependent packages.
Note sometimes packages show up as failed to build independent of the
change, simply because they are already broken on the target branch.
Result of nixpkgs-review run on x86_64-linux 1

1 package built:

nixdoc

Maintainer pings
cc @infinisil @asymmetric for testing.

CC @hsjobeki :)


thank you for the new design i hope it will look like yours, you know? rounded corners, softer colors. they are quite different.

feel free to close this. just putting it here, hoping you'll implement some features yours has

It's easy to come true! You can refer to the custom CSS file from my blog.
https://github.com/g1eny0ung/blog/blob/master/static/css/custom.css

thank you


Improve error message when passing string instead of an iterable in Meta.fields

In my code I only have to filter by one field, and I mistakenly wrote:
class Meta:
    model = MyModel
    fields = ('my_field')

Notice the lack of an ending comma in fields. The error was TypeError: Meta.fields contains a field that isn't defined on this FilterSet. We could improve this error on the likes: TypeError: Meta.fields must be an iterable.

Yep. No problem. Happy to see a pull request for that.

Pending a PR, I'm going to close this.


Support for Play 2.5.x

Hi,
Is any current plan for supporting Play 2.5.x version.

I've just tried to add the play-yeoman module to a new Play 2.5 app (addSbtPlugin("com.typesafe.play" % "sbt-plugin" % "2.5.0")), after following all the steps suggested for play 2.4.x I got the following error when run in sbt...
ProvisionException: Unable to provision, see the following errors: 1) Error injecting constructor, java.lang.AbstractMethodError: com.tuplejump.playYeoman.Yeoman.play$api$mvc$Results$setter$TooManyRequests_$eq(Lplay/api/mvc/Results$Status;)V at com.tuplejump.playYeoman.Yeoman.(Yeoman.scala:47) while locating com.tuplejump.playYeoman.Yeoman for parameter 1 at router.Routes.(Routes.scala:43) while locating router.Routes while locating play.api.inject.RoutesProvider while locating play.api.routing.Router for parameter 0 at play.api.http.JavaCompatibleHttpRequestHandler.(HttpRequestHandler.scala:200) while locating play.api.http.JavaCompatibleHttpRequestHandler while locating play.api.http.HttpRequestHandler for parameter 4 at play.api.DefaultApplication.(Application.scala:220) at play.api.DefaultApplication.class(Application.scala:220) while locating play.api.DefaultApplication while locating play.api.Application
Any idea on how to solve it? It is a matter or Play versionning?
Thanks in advance
ReneX

@renexdev The issue you are facing is because the plugin only has support till 2.4.x. We are working on supporting 2.5.x

Great!! You are a genius guys! Cheers!

Are you foreseeing to support a new version of Angular 2.0 ?

published 0.9.0 for Play 2.5.x.
@carlosFattor Although We used Angular example for the demo, you can use any yeoman generator and it should work with the plugin since The plugin has no dependencies on Angular.

@Shiti  Thanks a lot :-)

Thanks a lot!


Update GraphEdit when GraphNode's slot is updated

Fixes #45857.
Added slot_updated signal. Emit when set_slot in GraphNode is successfully called.
And rename _graph_node_moved to _graph_node_updated for that the new signal slot_updated can call together a function.

@swarnimarun If you wanted to take a look, feel free.

Thanks!

Cherry-picked for 3.2.4.


Display response times in verbose mode

Feature Request
It would be nice to display the response time for each request.
Why?

It would help us determine which sites cause the program to slow down. If a site is very slow and non-critical, we could remove it to speed up Sherlock.
The more metrics the merrier! Has very little overhead, so why not :)

User Experience
We would only display this information in verbose mode (-v flag). The information can be displayed in each output line as:
[+] [78 ms] Quora: https://www.quora.com/profile/nareddyt

Furthermore, these response times can be exported in the CSV.
Implementation Details
We would just need to store the time reach request was created in the net_info or results_site dictionaries. Then when the request is done, we would note the end time.
Note there is some math involved in calculating times. It's not a simple end-start because the result for each response is extracted in the order of requests (not as soon as a request finishes). This is something I still need to think about...

Sounds good. Maybe then we could sorts the sites by their response time and check the fastest ones first.


I cant run w2022 image?

##[error]UnsupportedWindowsVersion: Unsupported windows image version. Supported versions are 'Windows Server 2016 - Before 2B, Windows Server 2019 - Before 2B, Windows Server 2016 - After 2B, Windows Server 2019 - After 2B, Windows Server, Version 1903 - After 2B, Windows Server, Version 2004'
I get this error deploying my container instance image why ?

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 894ff75f-fc5c-93a7-92d9-9d262d5bb904
Version Independent ID: 5464901a-bb5b-a9dd-0a51-3620383f4c23
Content: Frequently asked questions
Content Source: articles/container-instances/container-instances-faq.yml
GitHub Login: @tomvcassidy
Microsoft Alias: tomcassidy

Thanks for the feedback! We are currently investigating and will update you shortly.

@rifaterdemsahin Thank you for reaching out!  I've updated the Microsoft Q&A thread but Windows Server 2022 is not supported on Azure Container Instances (ACI) at this time however the product team is looking at adding this functionality in the future.  I've reached out to you on Microsoft Q&A with some additional questions to help the product team better understand your use case scenario.

@rifaterdemsahin Thank you again for reaching out to us!  I've made a recommendation to update the documentation and this conversation will be visible for anyone else who may have the same question.  We are going to close this thread as resolved but if there are any further questions regarding the documentation, please tag me in your reply and we will be happy to continue the conversation.

FAQ still does not mention clearly if 2022 version is supported or not.
Relevant parts of the FAQ were updated two years ago that is before this ticket was created and I could not other information about supported Windows server versions. Re-open?


debug report

I keep getting errors when attempting to merge a directory. Can you, please advice what to do?
Thanks in advance,
pdfsak --debug --input-dir /home/daifu/Documents/Acupuncture/Scanned\ chinese\ medicine/output --output tcm_book.pdf
ERROR: unrecognized file type 'unknown' for '/home/daifu/Documents/Acupuncture/Scanned chinese medicine/output/temp/latex_file.log'
report.zip

What error is it?

sudo apt-get install texlive-latex-extra fixed this issue.
Regards


Unable to use Linux App Service with Azure Functions because of what seems like issues with Kudu

I have unsuccessfully been trying for two days to deploy a Azure Functions .NET Worker app to Linux App Service. Most of my attempts result in all requests to Kudu returning 503 Service Unavailable, one time it actually managed to start but my changes to app settings in the Azure Portal was never reflected in Kudu despite many attempts of restarting the app, stopping the app, waiting till the next day and so on.
Repro steps.
Create a new Azure Function app targeting Linux App Service on an app service plan (not consumption), set app setting FUNCTIONS_WORKER_RUNTIME to dotnet-isolated, Kudu is now entirely unavailable.
The one time I managed to get Kudu to start I opened the SCM site before changing app settings, but then my changes to app settings in the portal were never reflected in Kudu so any deployment I tried to make would fail because FUNCTIONS_WORKER_RUNTIME was stuck at the default value dotnet.
The log/error given by the failure.
503 Service Unavailable.
Mention any other details that might be useful.
I am using a B1 app service plan, I tried multiple times to delete the app and recreate it but always get the same result. I even tried creating a brand new app service plan, same result regardless.

@purva-vasudeo, @sanchitmehta, @rabollin and @abathinimsft please help with Kudu on Linux issue.

@suwatch Has anyone looked at this? I just tried it again and it is still broken.

Still broken. :(

@purva-vasudeo Thank you, that did seem to make Kudu start working again. Is this documented anywhere? I have never seen this setting before, it is certainly not exposed in the UI and the magic string "DOTNET-ISOLATED|5.0" is hard to guess.

I'm closing this issue as I'm now successfully using functions in Linux, though it was rather rough getting it working but there are other issues covering those problems.


Homebridge wont connect iphone 7 plus ios10.2

I have the strangest issue and cant manage it myself after couple of hours trial and error. I had homebridge running perfectly fine with following plugins: hue, harmony, denon-volume. Today I made some wiring changes to my devices and some devices ip changed and I needed to do some changes in the config.json. After that the devices in homekit app added by homebridge plugins were very slow reacting so I started to reset my whole homebridge setup by deleting homebridge and with it all the plugins to do a fresh install. End of the story: when I try to add accessory in Home app iphone 7 plus ios10.2 it sees Homebridge but it wont connect, it keeps spinning and after a long time it fails connecting. The strange thing is when I open the Home app on my wifes iPad ios 10.2 it connects perfectly fine. The things I already tried: uninstall homebridge and plugins, reinstall homebridge and plugins, but  connecting still fails. Deleting persits folder and accessory folder, but connecting still fails. Changed bridge name, username in config.json, but connecting still fails. Signed out of iCloud from my iPhone and Atv4, but connecting still fails. The last thing I did, because it was connecting perfectly fine with iPad on the same ios 10.2 so thought it was a problem on the iPhone side, was restoring my iphone as new iPhone, but after it wont connect too. Now I am at the point that I dont no what to do next. Please help me out.

I am on iPhon 7 Plus without any issues.
Would recommend removing all entires from your config file except one. See if you can get that one work. Then add the rest in one at a time.  Most likely there is an error in your config.
This would be my plan of action anyway.  Good luck.


feat: add bindings and and example script to interact with the tips contract

This PR adds type definitions to interact with the tips contract with a script that shows how they can be used to interact with it.

This PR depends on https://github.com/desmos-labs/desmos-contracts/pull/58


[BUG] Warning: Classname 'font-mono' is not a Tailwind CSS class!

Describe the bug
24:17  Warning: Classname 'font-mono' is not a Tailwind CSS class!
Getting this warning, the font is being displayed fine and should be valid tailwindcss I believe?

👋  @tellaconz ,
I did try to reproduce the issue but I did not get an error...
I did add a test case:

And it is passing:

Please re-open the issue when you can provide a dummy project demonstrating the issue so that I can clone it and debunk the issue from there.

https://github.com/francoismassart/eslint-plugin-tailwindcss/commit/a3aa5ce953178ee8e688c87925a31b0d3cb6c915


Deprecation of "jwt" key from IResolvedUrl.

Relevant PRs: #4126 #4133
r11s driver now requires a token provider, so we don't need to keep the tokens.jwt in IResolvedUrl anymore, since it will create confusions. I have removed it from most places in the above PRs. But there are two places where we are still using the jwt fields. We should take a look into those.

https://github.com/microsoft/FluidFramework/blob/main/packages/tools/fetch-tool/src/fluidFetchInit.ts#L17
https://github.com/microsoft/FluidFramework/blob/main/packages/drivers/local-driver/src/localDocumentServiceFactory.ts#L97

For now I have made a DefaultTokenProvider in r11s driver to work around this issue. We should delete that once the two usages are removed.

open


Handle missing discordant read calls for Af1.x

Af1.x CNV releases will be considered complete without DRCs, only including HMM data and coverage calls.
I think the meaning of the pre-release pre arg, e.g. Af1(pre=True) will correspond to a known temporary incompleteness of data, rather than any embargo status, which will be handled separately via forthcoming terms-of-use info instead.
Possibly related: issue https://github.com/malariagen/malariagen-data-python/issues/89

Possibly connectable: issue https://github.com/malariagen/malariagen-data-python/issues/363 PR https://github.com/malariagen/malariagen-data-python/pull/409

Currently, attempting to run something like:
af1.cnv_discordant_read_calls(contig='2RL')

will result in FileNotFoundError: '.zmetadata'.
Compare to CNV HMM code, which currently handles missing data using None, i.e.
    def open_cnv_hmm(self, sample_set: base_params.sample_set) -> zarr.hierarchy.Group:
        try:
            return self._cache_cnv_hmm[sample_set]
        except KeyError:
            release = self.lookup_release(sample_set=sample_set)
            release_path = self._release_to_path(release)
            path = f"{self._base_path}/{release_path}/cnv/{sample_set}/hmm/zarr"

            # If CNV HMM data exists for this sample set then return the zarr,
            # Otherwise return None.
            marker = path + "/.zmetadata"
            if self._fs.exists(marker):
                store = init_zarr_store(fs=self._fs, path=path)
                root = zarr.open_consolidated(store=store)
            else:
                root = None

            self._cache_cnv_hmm[sample_set] = root
        return root

Compare to CNV coverage calls code, which currently handles missing data using ValueError, i.e.
    def open_cnv_coverage_calls(
        self,
        sample_set: base_params.sample_set,
        analysis: cnv_params.coverage_calls_analysis,
    ) -> zarr.hierarchy.Group:
        key = (sample_set, analysis)
        try:
            return self._cache_cnv_coverage_calls[key]
        except KeyError:
            release = self.lookup_release(sample_set=sample_set)
            release_path = self._release_to_path(release)
            path = f"{self._base_path}/{release_path}/cnv/{sample_set}/coverage_calls/{analysis}/zarr"
            # N.B., not all sample_set/analysis combinations exist, need to check
            marker = path + "/.zmetadata"
            if not self._fs.exists(marker):
                raise ValueError(
                    f"CNV coverage calls analysis f{analysis!r} not implemented for sample set {sample_set!r}"
                )
            store = init_zarr_store(fs=self._fs, path=path)
            root = zarr.open_consolidated(store=store)
            self._cache_cnv_coverage_calls[key] = root
        return root

I'm not sure what the action points are for this issue, besides raising a less cryptic error than FileNotFoundError: '.zmetadata' when a user attempts to retrieve DRCs for Af1.x sample sets.

[ ] Less cryptic error message for missing CNV DRCs PR #576

Are there cases where we need this to fail silently, or return some sort of empty dataset, e.g. for concatenation?


Create filte i cannot delete

Its werid.
When i drag a file to my phone ,sometime it sussecc but somethimes it create filte i cannot delete
Like this, it said permissions do not match in chinese

If you drag&drop a file, it just executes adb push yourfile /sdcard/Download.


Fix the examples in the documentation for the Dynamic color module

Fix the examples in the documentation for the Dynamic color module https://kivymd.readthedocs.io/en/latest/components/dynamic-color/#example-of-dynamic-color-from-the-list-of-standard-color-schemes

Does this also include the example code errors in dynamic_color_image.py?  I get this error:
File "E:...\Kivy2406\examples\dynamic_color_image.py", line 78, in generate_cards
for name_color in self.theme_cls.current_schemes_color_data:
AttributeError: 'ThemeManager' object has no attribute 'current_schemes_color_data'
...
def generate_cards(self, *args):
    self.root.ids.card_list.data = []
    for name_color in self.theme_cls.current_schemes_color_data:
            self.root.ids.card_list.data.append(
            {
                "bg_color": getattr(self.theme_cls, name_color),
                "text": name_color,
            }
        )

...
It seems that current_schemes_color_data is not an attribute of ThemeManager.  Is there supposed to be a way to get all of the colors in a Theme?


Issue 262 job usage bug

Fixes https://github.com/twosigma/Cook/issues/262

Awaiting https://github.com/twosigma/Cook/issues/259 to get merged

Ready for review.


docs: fix simple typo, viewier -> viewer

There is a small typo in mitmproxy/tools/console/consoleaddons.py.
Should read viewer rather than viewier.
Semi-automated pull request generated by
https://github.com/timgates42/meticulous/blob/master/docs/NOTE.md

Thanks! 🍰


Notes Within Weapons From GCS Not Showing Up in Character Sheet

When there are notes written inside say, a ranged weapon (The one below Usage), it does not appear inside the sheet.

I cannot recreate.   Any notes on the equipment (that appear on the ranged attack) also appear in Foundry.

Here's what happens when I import the sheet.
In GCS: I have additional notes within the field as shown here.

However, once imported into Foundry, it doesn't appear. I am not sure if it's just an oversight or a bug or just maybe my problem.

Are you editing equipment, or is that and Advantage?   Can you zip up the .gcs file and drop it into this issue?

That's an advantage. I haven't tested equipment but I think it might be fine with equipment.
I will see if I can do that. I am out of town at the moment.

That's an advantage. I haven't tested equipment but I think it might be fine with equipment.
I will see if I can do that. I am out of town at the moment.

When you get a chance, can you zip up a .gcs file that is showing this issue?

Skylos_GCS.zip
Here's the file. We messed around a lot in the advantages' inner section, maybe that's the reason why it's not appearing in GGA.

Found it.   They are called "usage notes"... and we were looking for "notes".


DRep - retirement - happens that after retire page become white

To reproduce:

Become a DRep
Retire - happens that after confirmation on transaction page is white:

OK up to now.


Implementation of Office App Launcher

Create the necessary angular directives and implementation to get the same Office App Launcher as the image.

Personally I'd love to see this, but it won't be part of our first milestone as we want full parity with Fabric first. At the moment this is on their roadmap... suggest you push on their issue https://github.com/OfficeDev/Office-UI-Fabric/issues/81 to get them to do it.
We are not trying to create new things just yet, rather we're trying to create Angular Directives for fabric UI components.


RSpec/ExcessiveDocstringSpacing incorrect behaviour with heredoc syntax

I think this rule can be applied only to one-line descriptions.
It should not correct heredocs, it breaks ruby syntax after auto-correct..
# delete/by_topic_spec.rb: C: [Correctable] RSpec/ExcessiveDocstringSpacing: Excessive whitespace.
#  it <<~DESC do
#      ^^^^^

  it <<~DESC do
    does not remove
      another comments reports,
      another comments votes,
      another comments image,
      another comments feeds,
      another comments notifications,
      another comments public feeds,
      another comments subs
    does not nullifies
      another comments notifiers resources
  DESC

@G-Rath would you like to tackle this and #1193? They seem to be somewhat similar.

@pirj sure :)

@pirj I might need some help with coming up with a good solution for this one - the fix for #1193 does mean this particular case isn't flagged anymore, but if the cop does flag a heredoc it'll break when fixing it.
The issue is that heredocs are not their own special node in the AST:
(block
  (send nil :it
    (dstr
      (str "  does not remove\n")
      (str "  another comments reports,\n")
      (str "  another comments   votes,\n")
      (str "  another comments image,\n")
      (str "  another   comments feeds,\n")
      (str "  another comments notifications,\n")
      (str "  another comments public feeds,\n")
      (str "  another comments subs\n")
      (str "does not nullifies\n")
      (str "  another comments notifiers resources\n")))
  (args) nil)

This also affects at least ExampleWording, as that was the cop I used as my starting point:
it 'supports heredocs' do
  expect_offense(<<-'RUBY')
    it <<~DESC do
        ^^^^^ Do not use should when describing your tests.
      should not start with this word
    DESC
    end
  RUBY

  expect_correction(<<-'RUBY')
    it <<~DESC do
      not start with this word
    DESC
    end
  RUBY
end

1) RuboCop::Cop::RSpec::ExampleWording supports heredocs
     Failure/Error:
       expect_correction(<<-'RUBY')
         it <<~DESC do
           not start with this word
         DESC
         end
       RUBY

       expected: "      it <<~DESC do\n        not start with this word\n      DESC\n      end\n"
            got: "      it <does not start with this word\nC do\n        should not start with this word\n      DESC\n      end\n"

       (compared using ==)

       Diff:
       @@ -1,5 +1,6 @@
       -      it <<~DESC do
       -        not start with this word
       +      it <does not start with this word
       +C do
       +        should not start with this word
              DESC
              end

     # ./spec/rubocop/cop/rspec/example_wording_spec.rb:190:in `block (2 levels) in <top (required)>'


Simplify the "ADE"  Table

Since we have a very static and generic table structure for storing CityGML ADE data, the ADE table can be simplified by dropping some columns e.g. db_prefix, xml_schemamapping_file etc.

I agree. If we make it to really map all objects to existing tables, then we are fine.
See also the parallel discussion on how to possibly (re)name the (current) FEATURE table which would contain also objects that are not features.


Markdown Linting Support

Problem
Many of the existing repo markdown documents are riddled with typos and formatting issues. This can cause confusion for users and is nonetheless poor hygiene. Adding markdown linting will help establish a consistent formatting style for Markdown files.
Problem Solution

Integrated markdown linting in CI
Make directive to run markdown linter
Repo passing markdown linting scan

Hi, I can take this.
I was thinking about markdownlint, should it be ok?

Hey @alessandromazza98, that'll be fine 🙏🏻


React Capstone

In this capstone project I completed all the requirements that is required. I fetch the data from the restcountries API and then display it as required.

Now I implemented the suggested changes as required by reviewer. I change the font family to gill sans and increase the timeout for the tests to be passed.


communication release

https://github.com/Azure/sdk-release-request/issues/3996

API change check
API changes are not detected in this pull request.


Snowflake Regex Collate error

It seems like this issue was discovered previously and also fixed  #https://github.com/sodadata/soda-sql/issues/201
but i still get this error:
001045 (22023): SQL compilation error:
| argument needs to be a string: 'RATES.CONVERSION_RATE'
It is created by Soda including COLLATE('column', '') into the SQL Statement. When i execute the statement manually in Snowflake without COLLATE, it works fine.
This is my checks.yml:
checks for RATES:

invalid_count(conversion_rate) = 0:
valid format: positive decimal

SAS-2696


Remove chopped notes from logs

Problem statement: Commitment/notes which are created and nullified within the same transaction are chopped so that no entry in the private data tree is ever created. However, the current state of the code still emits a log for such commitments (at least for value note). When the note processor is processing such a log it will not find a corresponding entry in the tree and will log a warning. In addition, there might be other cases where this could cause confusion or issues, for instance, when a chopped note that could be spent by more than one user is received by such a user and is not aware that note is actually already nullified.
Remark: Once this done, it is important to revert/adapt work done as part of issue #1603
References
Original Design on Discourse: https://discourse.aztec.network/t/proposal-forcing-the-sequencer-to-actually-submit-data-to-l1/426
Solution Design
Key Properties

Per function call execution, encrypted notes are added into the encrypted logs and after execution the function encrypted logs are hashed (in TS code at the moment) and this hashed values is added into publicInputs.
L1 contract checks consistency of published calldata with the log hash.
We must keep track of the encrypted logs in the code until after the private kernel circuit execution. Only then, we know which logs have to be removed and the correct hash can be computed. Since L1 contract verifies the hash value based on published data, we MUST removed any chopped note from the logs.
LogsHash computation need to happen in the private kernel ordering circuit

### HackMd Solution Design

@dbanks12 @suyash67 @jeanmon  Given how involved are the potential solutions and how much complexity they add to the code I think we should consider just leaving the code as is. I was just discussing this with @iAmMichaelConnor and leaving the code as is would have 2 downsides:

Waste of calldata,
confusing log readers.

I think point 2 is not that big of a deal because we can easily filter the logs out in AztecRPC.
Regarding point 1 this waste occurs whenever one function creates a commitment, and another function nullifies that commitment, all within the same tx. So it will happen if a recursive function is called, or if re-entrancy happens. In my discussion with Mike it's not clear how common this scenario would be.
I think it would be a good idea to first try to log these transient note occurrences. What I propose is submitting a log with the note spending info over http from NoteProcessor whenever this happens. It should be straightforward to implement and if we get enough sandbox users we might get an idea regarding whether the issue is worth dealing with.

also referenced in #4891


refactor: move frontend/ to frontend/console

Note that this does not rename any of the Just commands for now.

Will move the extensions in here in a followup.


ALOS OALSAR1 SICD Format Support

Thank you for the great SAR repository.
I would like to apply the SICD format for ALOS PALSAR1.
I have confirmed it with subaperture. I would be happy if i pull request.  I would love to contribute.

I don't have permission when pushing, so I get a 403 error.
Please grant me permission.

@syu-tan, thanks for the offer! SARPy uses the fork and pull request model for contributions, which means no additional permissions are necessary.
There's some information regarding SARPy contributions scattered in a few READMEs (apologies for the current scattered nature of the "Contributing" documentation...):

Pull Requests
About integration branches
tests README

Please reach out with any further questions and we look forward to your contribution!

Note that there is one irreconcilable difference between the current SICD spec and ALOS1, as I understand it.  What I have been told by others who know more than me about that sensor is that ALOS1 data is in a native Doppler geometry, which isn't really a grid currently supported by SICD (as opposed to a zero Doppler geometry that is used in SICD and many modern sensors).  That said, it may still effectively be able to get you most of the way there for much of the functionality you may be looking for (as you have shown in your results.)  It is possible that geolocation will be a bit off though.

Thank you for sharing this important information.
The distortion of iso doppler has not been corrected. I haven't done the calculations, but I think that if the difference in squint angle is large, the change in the azimuth direction cannot be reproduced in the SICD format.
Still, I thought the attempt to unify it to NGA was great.


Updated help file

Describe the bug
Help file is outdated. Updated text is attached. (Dont implement until all functionality is finalized.)

baytrendsmap Shiny App Help.docx

Let's push this edit to after we get internal comments

... overtaken by issue 29 .


Export to print-ready PDFs

Long story short: I would like Mechanic to export print-ready PDFs.
More specific:

PDF must include crop marks
There must be some way to tell the PPI of embedded images before export (PPI must be at least 300 for printing)

(⚠Warning: I'm a designer teaching myself more about code, not a developer. The last months I have fiddled around with mechanic.design.)
The promise of turning rules into tools is just what I am looking for in several of my projects. But when you're implementing across digital and print media, you need the output inteded for print to be production-ready.  I would really like to see mechanic.design solve that 😃
I looked into converting SVG and Canvas to PDF, but quickly gave up (not being a developer).

Hey @alexfjelldal! Thanks for reaching out. This is very much on our radar, along with a more generalized way to specify export formats, so your idea very much aligns with that.
We've been focusing on other projects the last few months, so Mechanic development has been slow lately. Once we finish some already started loose ends, this will probably be worked on.


CJM-33819 Rename eventtype Deliver to Send and add Dismiss

Please link to the issue:
Jira:https://jira.corp.adobe.com/browse/CJM-33819
Github Issue: https://github.com/adobe/xdm/issues/1519
Summary:
Rename eventtype decisioning.propositionDeliver to decisioning.propositionSend and adds decisioning.propositionDismiss

Was this a recently introduced event type ? I am wondering if there are any existing segments created based on this event type which will no longer evaluate if we change this. Could you please confirm before we can make this change ?

hi @mjasrotia yes, Deliver was recently introduced and is now being changed to Send. It is only being used in Stage environment right now, and will be changed once the xdm change is available. This feature is still pre-release, and the name change is needed so that we can progress to beta.


Update README.md

new look
issue #4

@Fenrir-04 you closed this repo ?

sorry it was a mistake

@Fenrir-04 now check made the changes


Release 0.0.11

Thanks for merging https://github.com/warmuuh/pactbroker-maven-plugin/pull/19
Please release these changes in next version of the plugin so we can start using them.

Released 0.0.11.  you can always use master-SNAPSHOT to get the latest version though.

Ah ok I didn't know that. Although some of our teams would not be comfortable automatically pulling in the latest version. Thanks for the release!


fix: implement base contract

Occasionally I see this error when I type my arguments:
Argument #1 ($builder) must be of type Illuminate\Contracts\Database\Eloquent\Builder, Tpetry\PostgresqlEnhanced\Query\Builder given

I think this change should not have any side effects.

This contract is only available for Laravel 9 but I am supporting a wider range of versions.

Do you have an example for the code producing such an error?

Oh, sorry, I didn't know that this contract was new. It happens for me for this case in the where method:
https://github.com/laravel/framework/blob/83b6064edd9ec38c5a2f300c4f197e7d825c98cf/src/Illuminate/Database/Query/Builder.php#L729-L734


[8] No importer registered for op: DCNv2_TRT

Thanks for your work here.
I have followed your steps, but changed the docker version to 20.03 which with cuda10.2 and Tensorrt7.0, I got this Error, do you have any suggestion? It's seems Plugins was loaded but trtexec still can't find it
[&&&& RUNNING TensorRT.trtexec # trtexec --onnx=y++.onnx --plugins=dcnv2_trt/build/dcn_plugin.so --workspace=2000 --saveEngine=y++.trt
[09/28/2020-09:35:51] [I] === Model Options ===
[09/28/2020-09:35:51] [I] Format: ONNX
[09/28/2020-09:35:51] [I] Model: y++.onnx
[09/28/2020-09:35:51] [I] Output:
[09/28/2020-09:35:51] [I] === Build Options ===
[09/28/2020-09:35:51] [I] Max batch: 1
[09/28/2020-09:35:51] [I] Workspace: 2000 MB
[09/28/2020-09:35:51] [I] minTiming: 1
[09/28/2020-09:35:51] [I] avgTiming: 8
[09/28/2020-09:35:51] [I] Precision: FP32
[09/28/2020-09:35:51] [I] Calibration:
[09/28/2020-09:35:51] [I] Safe mode: Disabled
[09/28/2020-09:35:51] [I] Save engine: y++.trt
[09/28/2020-09:35:51] [I] Load engine:
[09/28/2020-09:35:51] [I] Inputs format: fp32:CHW
[09/28/2020-09:35:51] [I] Outputs format: fp32:CHW
[09/28/2020-09:35:51] [I] Input build shapes: model
[09/28/2020-09:35:51] [I] === System Options ===
[09/28/2020-09:35:51] [I] Device: 0
[09/28/2020-09:35:51] [I] DLACore:
[09/28/2020-09:35:51] [I] Plugins: dcnv2_trt/build/dcn_plugin.so
[09/28/2020-09:35:51] [I] === Inference Options ===
[09/28/2020-09:35:51] [I] Batch: 1
[09/28/2020-09:35:51] [I] Iterations: 10
[09/28/2020-09:35:51] [I] Duration: 3s (+ 200ms warm up)
[09/28/2020-09:35:51] [I] Sleep time: 0ms
[09/28/2020-09:35:51] [I] Streams: 1
[09/28/2020-09:35:51] [I] ExposeDMA: Disabled
[09/28/2020-09:35:51] [I] Spin-wait: Disabled
[09/28/2020-09:35:51] [I] Multithreading: Disabled
[09/28/2020-09:35:51] [I] CUDA Graph: Disabled
[09/28/2020-09:35:51] [I] Skip inference: Disabled
[09/28/2020-09:35:51] [I] Input inference shapes: model
[09/28/2020-09:35:51] [I] Inputs:
[09/28/2020-09:35:51] [I] === Reporting Options ===
[09/28/2020-09:35:51] [I] Verbose: Disabled
[09/28/2020-09:35:51] [I] Averages: 10 inferences
[09/28/2020-09:35:51] [I] Percentile: 99
[09/28/2020-09:35:51] [I] Dump output: Disabled
[09/28/2020-09:35:51] [I] Profile: Disabled
[09/28/2020-09:35:51] [I] Export timing to JSON file:
[09/28/2020-09:35:51] [I] Export output to JSON file:
[09/28/2020-09:35:51] [I] Export profile to JSON file:
[09/28/2020-09:35:51] [I]
[09/28/2020-09:35:51] [I] Loading supplied plugin library: dcnv2_trt/build/dcn_plugin.so
----------------------------------------------------------------
Input filename:   y++.onnx
ONNX IR version:  0.0.6
Opset version:    11
Producer name:
Producer version:
Domain:
Model version:    0
Doc string:
While parsing node number 40 [DCNv2_TRT]:
ERROR: ModelImporter.cpp:134 In function parseGraph:
[8] No importer registered for op: DCNv2_TRT
[09/28/2020-09:35:51] [E] Failed to parse onnx file
[09/28/2020-09:35:51] [E] Parsing model failed
[09/28/2020-09:35:51] [E] Engine creation failed
[09/28/2020-09:35:51] [E] Engine set up failed]

@Kyuuki93 I haven't tested with TensorRT 7.0 (only 7.1), but theoretically it should work.  Can you use the --verbose flag with the trtexec command and see if that provides any other feedback into what the issue is?

@Kyuuki93 I haven't tested with TensorRT 7.0 (only 7.1), but theoretically it should work. Can you use the --verbose flag with the trtexec command and see if that provides any other feedback into what the issue is?

 Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 886
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: prediction_layers.0.conf_layer.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: prediction_layers.0.conf_layer.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 887
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 888
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: prediction_layers.0.mask_layer.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: prediction_layers.0.mask_layer.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 889
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 890
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 891
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 892
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 893
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 894
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 895
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 896
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 897
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 898
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 899
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 900
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 901
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 902
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 903
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 904
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 905
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 906
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 907
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 908
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 909
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 910
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 911
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 912
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 913
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:90: Importing initializer: 914
[09/29/2020-01:59:31] [W] [TRT] onnx2trt_utils.cpp:198: Your ONNX model has been generated with INT64 weights, while TensorRT does not natively support INT64. Attempting to cast down to INT32.
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Conv_0 [Conv]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: input.1
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.conv1.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Conv_0 [Conv] inputs: [input.1 -> (1, 3, 550, 550)], [backbone.conv1.weight -> (64, 3, 7, 7)],
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:442: Convolution input dimensions: (1, 3, 550, 550)
[09/29/2020-01:59:31] [V] [TRT] Bias weights are not set yet. Bias weights can be set using setInput(2, bias_tensor) API call.
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:524: Using kernel: (7, 7), strides: (2, 2), padding: (3, 3), dilations: (1, 1), numOutputs: 64
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:525: Convolution output dimensions: (1, 64, 275, 275)
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Conv_0 for ONNX node: Conv_0
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 406 for ONNX tensor: 406
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Conv_0 [Conv] outputs: [406 -> (1, 64, 275, 275)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: BatchNormalization_1 [BatchNormalization]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 406
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.bn1.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.bn1.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.bn1.running_mean
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.bn1.running_var
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: BatchNormalization_1 [BatchNormalization] inputs: [406 -> (1, 64, 275, 275)], [backbone.bn1.weight -> (64)], [backbone.bn1.bias -> (64)], [backbone.bn1.running_mean -> (64)], [backbone.bn1.running_var -> (64)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: BatchNormalization_1 for ONNX node: BatchNormalization_1
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 407 for ONNX tensor: 407
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: BatchNormalization_1 [BatchNormalization] outputs: [407 -> (1, 64, 275, 275)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Relu_2 [Relu]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 407
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Relu_2 [Relu] inputs: [407 -> (1, 64, 275, 275)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Relu_2 for ONNX node: Relu_2
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 408 for ONNX tensor: 408
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Relu_2 [Relu] outputs: [408 -> (1, 64, 275, 275)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: MaxPool_3 [MaxPool]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 408
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: MaxPool_3 [MaxPool] inputs: [408 -> (1, 64, 275, 275)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: MaxPool_3 for ONNX node: MaxPool_3
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 409 for ONNX tensor: 409
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: MaxPool_3 [MaxPool] outputs: [409 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Conv_4 [Conv]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 409
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.conv1.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Conv_4 [Conv] inputs: [409 -> (1, 64, 138, 138)], [backbone.layers.0.0.conv1.weight -> (64, 64, 1, 1)],
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:442: Convolution input dimensions: (1, 64, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] Bias weights are not set yet. Bias weights can be set using setInput(2, bias_tensor) API call.
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:524: Using kernel: (1, 1), strides: (1, 1), padding: (0, 0), dilations: (1, 1), numOutputs: 64
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:525: Convolution output dimensions: (1, 64, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Conv_4 for ONNX node: Conv_4
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 410 for ONNX tensor: 410
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Conv_4 [Conv] outputs: [410 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: BatchNormalization_5 [BatchNormalization]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 410
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.bn1.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.bn1.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.bn1.running_mean
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.bn1.running_var
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: BatchNormalization_5 [BatchNormalization] inputs: [410 -> (1, 64, 138, 138)], [backbone.layers.0.0.bn1.weight -> (64)], [backbone.layers.0.0.bn1.bias -> (64)], [backbone.layers.0.0.bn1.running_mean -> (64)], [backbone.layers.0.0.bn1.running_var -> (64)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: BatchNormalization_5 for ONNX node: BatchNormalization_5
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 411 for ONNX tensor: 411
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: BatchNormalization_5 [BatchNormalization] outputs: [411 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Relu_6 [Relu]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 411
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Relu_6 [Relu] inputs: [411 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Relu_6 for ONNX node: Relu_6
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 412 for ONNX tensor: 412
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Relu_6 [Relu] outputs: [412 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Conv_7 [Conv]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 412
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.conv2.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Conv_7 [Conv] inputs: [412 -> (1, 64, 138, 138)], [backbone.layers.0.0.conv2.weight -> (64, 64, 3, 3)],
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:442: Convolution input dimensions: (1, 64, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] Bias weights are not set yet. Bias weights can be set using setInput(2, bias_tensor) API call.
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:524: Using kernel: (3, 3), strides: (1, 1), padding: (1, 1), dilations: (1, 1), numOutputs: 64
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:525: Convolution output dimensions: (1, 64, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Conv_7 for ONNX node: Conv_7
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 413 for ONNX tensor: 413
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Conv_7 [Conv] outputs: [413 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: BatchNormalization_8 [BatchNormalization]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 413
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.bn2.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.bn2.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.bn2.running_mean
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.bn2.running_var
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: BatchNormalization_8 [BatchNormalization] inputs: [413 -> (1, 64, 138, 138)], [backbone.layers.0.0.bn2.weight -> (64)], [backbone.layers.0.0.bn2.bias -> (64)], [backbone.layers.0.0.bn2.running_mean -> (64)], [backbone.layers.0.0.bn2.running_var -> (64)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: BatchNormalization_8 for ONNX node: BatchNormalization_8
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 414 for ONNX tensor: 414
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: BatchNormalization_8 [BatchNormalization] outputs: [414 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Relu_9 [Relu]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 414
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Relu_9 [Relu] inputs: [414 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Relu_9 for ONNX node: Relu_9
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 415 for ONNX tensor: 415
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Relu_9 [Relu] outputs: [415 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Conv_10 [Conv]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 415
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.conv3.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Conv_10 [Conv] inputs: [415 -> (1, 64, 138, 138)], [backbone.layers.0.0.conv3.weight -> (256, 64, 1, 1)],
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:442: Convolution input dimensions: (1, 64, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] Bias weights are not set yet. Bias weights can be set using setInput(2, bias_tensor) API call.
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:524: Using kernel: (1, 1), strides: (1, 1), padding: (0, 0), dilations: (1, 1), numOutputs: 256
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:525: Convolution output dimensions: (1, 256, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Conv_10 for ONNX node: Conv_10
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 416 for ONNX tensor: 416
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Conv_10 [Conv] outputs: [416 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: BatchNormalization_11 [BatchNormalization]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 416
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.bn3.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.bn3.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.bn3.running_mean
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.bn3.running_var
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: BatchNormalization_11 [BatchNormalization] inputs: [416 -> (1, 256, 138, 138)], [backbone.layers.0.0.bn3.weight -> (256)], [backbone.layers.0.0.bn3.bias -> (256)], [backbone.layers.0.0.bn3.running_mean -> (256)], [backbone.layers.0.0.bn3.running_var -> (256)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: BatchNormalization_11 for ONNX node: BatchNormalization_11
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 417 for ONNX tensor: 417
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: BatchNormalization_11 [BatchNormalization] outputs: [417 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Conv_12 [Conv]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 409
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.downsample.0.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Conv_12 [Conv] inputs: [409 -> (1, 64, 138, 138)], [backbone.layers.0.0.downsample.0.weight -> (256, 64, 1, 1)],
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:442: Convolution input dimensions: (1, 64, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] Bias weights are not set yet. Bias weights can be set using setInput(2, bias_tensor) API call.
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:524: Using kernel: (1, 1), strides: (1, 1), padding: (0, 0), dilations: (1, 1), numOutputs: 256
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:525: Convolution output dimensions: (1, 256, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Conv_12 for ONNX node: Conv_12
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 418 for ONNX tensor: 418
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Conv_12 [Conv] outputs: [418 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: BatchNormalization_13 [BatchNormalization]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 418
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.downsample.1.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.downsample.1.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.downsample.1.running_mean
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.0.downsample.1.running_var
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: BatchNormalization_13 [BatchNormalization] inputs: [418 -> (1, 256, 138, 138)], [backbone.layers.0.0.downsample.1.weight -> (256)], [backbone.layers.0.0.downsample.1.bias -> (256)], [backbone.layers.0.0.downsample.1.running_mean -> (256)], [backbone.layers.0.0.downsample.1.running_var -> (256)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: BatchNormalization_13 for ONNX node: BatchNormalization_13
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 419 for ONNX tensor: 419
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: BatchNormalization_13 [BatchNormalization] outputs: [419 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Add_14 [Add]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 417
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 419
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Add_14 [Add] inputs: [417 -> (1, 256, 138, 138)], [419 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Add_14 for ONNX node: Add_14
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 420 for ONNX tensor: 420
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Add_14 [Add] outputs: [420 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Relu_15 [Relu]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 420
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Relu_15 [Relu] inputs: [420 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Relu_15 for ONNX node: Relu_15
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 421 for ONNX tensor: 421
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Relu_15 [Relu] outputs: [421 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Conv_16 [Conv]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 421
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.1.conv1.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Conv_16 [Conv] inputs: [421 -> (1, 256, 138, 138)], [backbone.layers.0.1.conv1.weight -> (64, 256, 1, 1)],
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:442: Convolution input dimensions: (1, 256, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] Bias weights are not set yet. Bias weights can be set using setInput(2, bias_tensor) API call.
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:524: Using kernel: (1, 1), strides: (1, 1), padding: (0, 0), dilations: (1, 1), numOutputs: 64
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:525: Convolution output dimensions: (1, 64, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Conv_16 for ONNX node: Conv_16
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 422 for ONNX tensor: 422
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Conv_16 [Conv] outputs: [422 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: BatchNormalization_17 [BatchNormalization]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 422
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.1.bn1.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.1.bn1.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.1.bn1.running_mean
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.1.bn1.running_var
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: BatchNormalization_17 [BatchNormalization] inputs: [422 -> (1, 64, 138, 138)], [backbone.layers.0.1.bn1.weight -> (64)], [backbone.layers.0.1.bn1.bias -> (64)], [backbone.layers.0.1.bn1.running_mean -> (64)], [backbone.layers.0.1.bn1.running_var -> (64)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: BatchNormalization_17 for ONNX node: BatchNormalization_17
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 423 for ONNX tensor: 423
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: BatchNormalization_17 [BatchNormalization] outputs: [423 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Relu_18 [Relu]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 423
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Relu_18 [Relu] inputs: [423 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Relu_18 for ONNX node: Relu_18
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 424 for ONNX tensor: 424
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Relu_18 [Relu] outputs: [424 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Conv_19 [Conv]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 424
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.1.conv2.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Conv_19 [Conv] inputs: [424 -> (1, 64, 138, 138)], [backbone.layers.0.1.conv2.weight -> (64, 64, 3, 3)],
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:442: Convolution input dimensions: (1, 64, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] Bias weights are not set yet. Bias weights can be set using setInput(2, bias_tensor) API call.
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:524: Using kernel: (3, 3), strides: (1, 1), padding: (1, 1), dilations: (1, 1), numOutputs: 64
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:525: Convolution output dimensions: (1, 64, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Conv_19 for ONNX node: Conv_19
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 425 for ONNX tensor: 425
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Conv_19 [Conv] outputs: [425 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: BatchNormalization_20 [BatchNormalization]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 425
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.1.bn2.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.1.bn2.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.1.bn2.running_mean
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.1.bn2.running_var
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: BatchNormalization_20 [BatchNormalization] inputs: [425 -> (1, 64, 138, 138)], [backbone.layers.0.1.bn2.weight -> (64)], [backbone.layers.0.1.bn2.bias -> (64)], [backbone.layers.0.1.bn2.running_mean -> (64)], [backbone.layers.0.1.bn2.running_var -> (64)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: BatchNormalization_20 for ONNX node: BatchNormalization_20
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 426 for ONNX tensor: 426
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: BatchNormalization_20 [BatchNormalization] outputs: [426 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Relu_21 [Relu]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 426
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Relu_21 [Relu] inputs: [426 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Relu_21 for ONNX node: Relu_21
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 427 for ONNX tensor: 427
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Relu_21 [Relu] outputs: [427 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Conv_22 [Conv]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 427
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.1.conv3.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Conv_22 [Conv] inputs: [427 -> (1, 64, 138, 138)], [backbone.layers.0.1.conv3.weight -> (256, 64, 1, 1)],
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:442: Convolution input dimensions: (1, 64, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] Bias weights are not set yet. Bias weights can be set using setInput(2, bias_tensor) API call.
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:524: Using kernel: (1, 1), strides: (1, 1), padding: (0, 0), dilations: (1, 1), numOutputs: 256
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:525: Convolution output dimensions: (1, 256, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Conv_22 for ONNX node: Conv_22
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 428 for ONNX tensor: 428
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Conv_22 [Conv] outputs: [428 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: BatchNormalization_23 [BatchNormalization]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 428
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.1.bn3.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.1.bn3.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.1.bn3.running_mean
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.1.bn3.running_var
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: BatchNormalization_23 [BatchNormalization] inputs: [428 -> (1, 256, 138, 138)], [backbone.layers.0.1.bn3.weight -> (256)], [backbone.layers.0.1.bn3.bias -> (256)], [backbone.layers.0.1.bn3.running_mean -> (256)], [backbone.layers.0.1.bn3.running_var -> (256)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: BatchNormalization_23 for ONNX node: BatchNormalization_23
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 429 for ONNX tensor: 429
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: BatchNormalization_23 [BatchNormalization] outputs: [429 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Add_24 [Add]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 429
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 421
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Add_24 [Add] inputs: [429 -> (1, 256, 138, 138)], [421 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Add_24 for ONNX node: Add_24
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 430 for ONNX tensor: 430
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Add_24 [Add] outputs: [430 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Relu_25 [Relu]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 430
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Relu_25 [Relu] inputs: [430 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Relu_25 for ONNX node: Relu_25
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 431 for ONNX tensor: 431
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Relu_25 [Relu] outputs: [431 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Conv_26 [Conv]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 431
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.2.conv1.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Conv_26 [Conv] inputs: [431 -> (1, 256, 138, 138)], [backbone.layers.0.2.conv1.weight -> (64, 256, 1, 1)],
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:442: Convolution input dimensions: (1, 256, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] Bias weights are not set yet. Bias weights can be set using setInput(2, bias_tensor) API call.
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:524: Using kernel: (1, 1), strides: (1, 1), padding: (0, 0), dilations: (1, 1), numOutputs: 64
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:525: Convolution output dimensions: (1, 64, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Conv_26 for ONNX node: Conv_26
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 432 for ONNX tensor: 432
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Conv_26 [Conv] outputs: [432 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: BatchNormalization_27 [BatchNormalization]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 432
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.2.bn1.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.2.bn1.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.2.bn1.running_mean
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.2.bn1.running_var
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: BatchNormalization_27 [BatchNormalization] inputs: [432 -> (1, 64, 138, 138)], [backbone.layers.0.2.bn1.weight -> (64)], [backbone.layers.0.2.bn1.bias -> (64)], [backbone.layers.0.2.bn1.running_mean -> (64)], [backbone.layers.0.2.bn1.running_var -> (64)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: BatchNormalization_27 for ONNX node: BatchNormalization_27
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 433 for ONNX tensor: 433
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: BatchNormalization_27 [BatchNormalization] outputs: [433 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Relu_28 [Relu]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 433
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Relu_28 [Relu] inputs: [433 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Relu_28 for ONNX node: Relu_28
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 434 for ONNX tensor: 434
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Relu_28 [Relu] outputs: [434 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Conv_29 [Conv]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 434
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.2.conv2.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Conv_29 [Conv] inputs: [434 -> (1, 64, 138, 138)], [backbone.layers.0.2.conv2.weight -> (64, 64, 3, 3)],
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:442: Convolution input dimensions: (1, 64, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] Bias weights are not set yet. Bias weights can be set using setInput(2, bias_tensor) API call.
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:524: Using kernel: (3, 3), strides: (1, 1), padding: (1, 1), dilations: (1, 1), numOutputs: 64
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:525: Convolution output dimensions: (1, 64, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Conv_29 for ONNX node: Conv_29
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 435 for ONNX tensor: 435
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Conv_29 [Conv] outputs: [435 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: BatchNormalization_30 [BatchNormalization]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 435
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.2.bn2.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.2.bn2.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.2.bn2.running_mean
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.2.bn2.running_var
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: BatchNormalization_30 [BatchNormalization] inputs: [435 -> (1, 64, 138, 138)], [backbone.layers.0.2.bn2.weight -> (64)], [backbone.layers.0.2.bn2.bias -> (64)], [backbone.layers.0.2.bn2.running_mean -> (64)], [backbone.layers.0.2.bn2.running_var -> (64)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: BatchNormalization_30 for ONNX node: BatchNormalization_30
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 436 for ONNX tensor: 436
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: BatchNormalization_30 [BatchNormalization] outputs: [436 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Relu_31 [Relu]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 436
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Relu_31 [Relu] inputs: [436 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Relu_31 for ONNX node: Relu_31
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 437 for ONNX tensor: 437
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Relu_31 [Relu] outputs: [437 -> (1, 64, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Conv_32 [Conv]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 437
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.2.conv3.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Conv_32 [Conv] inputs: [437 -> (1, 64, 138, 138)], [backbone.layers.0.2.conv3.weight -> (256, 64, 1, 1)],
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:442: Convolution input dimensions: (1, 64, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] Bias weights are not set yet. Bias weights can be set using setInput(2, bias_tensor) API call.
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:524: Using kernel: (1, 1), strides: (1, 1), padding: (0, 0), dilations: (1, 1), numOutputs: 256
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:525: Convolution output dimensions: (1, 256, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Conv_32 for ONNX node: Conv_32
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 438 for ONNX tensor: 438
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Conv_32 [Conv] outputs: [438 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: BatchNormalization_33 [BatchNormalization]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 438
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.2.bn3.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.2.bn3.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.2.bn3.running_mean
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.0.2.bn3.running_var
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: BatchNormalization_33 [BatchNormalization] inputs: [438 -> (1, 256, 138, 138)], [backbone.layers.0.2.bn3.weight -> (256)], [backbone.layers.0.2.bn3.bias -> (256)], [backbone.layers.0.2.bn3.running_mean -> (256)], [backbone.layers.0.2.bn3.running_var -> (256)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: BatchNormalization_33 for ONNX node: BatchNormalization_33
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 439 for ONNX tensor: 439
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: BatchNormalization_33 [BatchNormalization] outputs: [439 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Add_34 [Add]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 439
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 431
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Add_34 [Add] inputs: [439 -> (1, 256, 138, 138)], [431 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Add_34 for ONNX node: Add_34
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 440 for ONNX tensor: 440
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Add_34 [Add] outputs: [440 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Relu_35 [Relu]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 440
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Relu_35 [Relu] inputs: [440 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Relu_35 for ONNX node: Relu_35
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 441 for ONNX tensor: 441
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Relu_35 [Relu] outputs: [441 -> (1, 256, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Conv_36 [Conv]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 441
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.1.0.conv1.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Conv_36 [Conv] inputs: [441 -> (1, 256, 138, 138)], [backbone.layers.1.0.conv1.weight -> (128, 256, 1, 1)],
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:442: Convolution input dimensions: (1, 256, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] Bias weights are not set yet. Bias weights can be set using setInput(2, bias_tensor) API call.
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:524: Using kernel: (1, 1), strides: (1, 1), padding: (0, 0), dilations: (1, 1), numOutputs: 128
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:525: Convolution output dimensions: (1, 128, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Conv_36 for ONNX node: Conv_36
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 442 for ONNX tensor: 442
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Conv_36 [Conv] outputs: [442 -> (1, 128, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: BatchNormalization_37 [BatchNormalization]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 442
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.1.0.bn1.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.1.0.bn1.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.1.0.bn1.running_mean
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.1.0.bn1.running_var
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: BatchNormalization_37 [BatchNormalization] inputs: [442 -> (1, 128, 138, 138)], [backbone.layers.1.0.bn1.weight -> (128)], [backbone.layers.1.0.bn1.bias -> (128)], [backbone.layers.1.0.bn1.running_mean -> (128)], [backbone.layers.1.0.bn1.running_var -> (128)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: BatchNormalization_37 for ONNX node: BatchNormalization_37
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 443 for ONNX tensor: 443
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: BatchNormalization_37 [BatchNormalization] outputs: [443 -> (1, 128, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Relu_38 [Relu]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 443
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Relu_38 [Relu] inputs: [443 -> (1, 128, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Relu_38 for ONNX node: Relu_38
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 444 for ONNX tensor: 444
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Relu_38 [Relu] outputs: [444 -> (1, 128, 138, 138)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Conv_39 [Conv]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 444
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.1.0.conv2.conv_offset_mask.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.1.0.conv2.conv_offset_mask.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Conv_39 [Conv] inputs: [444 -> (1, 128, 138, 138)], [backbone.layers.1.0.conv2.conv_offset_mask.weight -> (27, 128, 3, 3)], [backbone.layers.1.0.conv2.conv_offset_mask.bias -> (27)],
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:442: Convolution input dimensions: (1, 128, 138, 138)
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:524: Using kernel: (3, 3), strides: (2, 2), padding: (1, 1), dilations: (1, 1), numOutputs: 27
[09/29/2020-01:59:31] [V] [TRT] builtin_op_importers.cpp:525: Convolution output dimensions: (1, 27, 69, 69)
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:122: Registering layer: Conv_39 for ONNX node: Conv_39
[09/29/2020-01:59:31] [V] [TRT] ImporterContext.hpp:97: Registering tensor: 445 for ONNX tensor: 445
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:180: Conv_39 [Conv] outputs: [445 -> (1, 27, 69, 69)],
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:107: Parsing node: Plugin_40 [DCNv2_TRT]
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 444
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: 445
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.1.0.conv2.weight
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:123: Searching for input: backbone.layers.1.0.conv2.bias
[09/29/2020-01:59:31] [V] [TRT] ModelImporter.cpp:129: Plugin_40 [DCNv2_TRT] inputs: [444 -> (1, 128, 138, 138)], [445 -> (1, 27, 69, 69)], [backbone.layers.1.0.conv2.weight -> (128, 128, 3, 3)], [backbone.layers.1.0.conv2.bias -> (128)],
While parsing node number 40 [DCNv2_TRT -> "446"]:
--- Begin node ---
input: "444"
input: "445"
input: "backbone.layers.1.0.conv2.weight"
input: "backbone.layers.1.0.conv2.bias"
output: "446"
name: "Plugin_40"
op_type: "DCNv2_TRT"
attribute {
  name: "name"
  s: "DCNv2"
  type: STRING
}
attribute {
  name: "dilation"
  ints: 1
  ints: 1
  type: INTS
}
attribute {
  name: "padding"
  ints: 1
  ints: 1
  type: INTS
}
attribute {
  name: "stride"
  ints: 2
  ints: 2
  type: INTS
}
attribute {
  name: "deformable_groups"
  i: 1
  type: INT
}

--- End node ---
ERROR: ModelImporter.cpp:134 In function parseGraph:
[8] No importer registered for op: DCNv2_TRT
[09/29/2020-01:59:31] [E] Failed to parse onnx file
[09/29/2020-01:59:31] [E] Parsing model failed
[09/29/2020-01:59:31] [E] Engine creation failed
[09/29/2020-01:59:31] [E] Engine set up failed
&&&& FAILED TensorRT.trtexec # trtexec --onnx=../y++.onnx --plugins=build/dcn_plugin.so --workspace=2000 --saveEngine=y++.trt --verbose

Also, I will try to use tensorrt7.1

I have tried docker image version 20.08:py3, with cuda 11.0, cudnn 8.0.3 and tensorrt 7.1.3, the --verbose log shows that:
[09/30/2020-01:55:55] [I] [TRT] /home/jenkins/workspace/OSS/L0_MergeRequest/oss/parsers/onnx/ModelImporter.cpp:139: No importer registered for op: DCNv2_TRT. Attempting to import as plugin.
[09/30/2020-01:55:55] [I] [TRT] /home/jenkins/workspace/OSS/L0_MergeRequest/oss/parsers/onnx/builtin_op_importers.cpp:3710: Searching for plugin: DCNv2_TRT, plugin_version: 1, plugin_namespace:
[09/30/2020-01:55:55] [I] [TRT] /home/jenkins/workspace/OSS/L0_MergeRequest/oss/parsers/onnx/builtin_op_importers.cpp:3727: Successfully created plugin: DCNv2_TRT
[09/30/2020-01:55:55] [V] [TRT] /home/jenkins/workspace/OSS/L0_MergeRequest/oss/parsers/onnx/ImporterContext.hpp:141: Registering layer: Plugin_40 for ONNX node: Plugin_40
[09/30/2020-01:55:55] [V] [TRT] /home/jenkins/workspace/OSS/L0_MergeRequest/oss/parsers/onnx/ImporterContext.hpp:116: Registering tensor: 446 for ONNX tensor: 446
[09/30/2020-01:55:55] [V] [TRT] /home/jenkins/workspace/OSS/L0_MergeRequest/oss/parsers/onnx/ModelImporter.cpp:183: Plugin_40 [DCNv2_TRT] outputs: [446 -> (1, 128, 138, 138)],

the plugin works now.

@Kyuuki93 ,if it modified to custom_ops::DCNv2_TRT， it will output:
RuntimeError: ONNX export failed: Couldn't export operator custom_ops::DCNv2_TRT

Couldn't export operator custom_ops that is beacause of the version of pytorch; After i export the onnx file successfully, it still cannot convert the onnx file to trt file and output the error that [8] No importer registered for op: DCNv2_TRT. But I check the log  and I found that the plugin DCNv2_TRT is registered successfully. shows below:
[01/11/2022-09:52:27] [V] [TRT] Plugin creator registration succeeded - ::DCNv2_TRT


minidec: thread main panicked at called Option::unwrap() on a None value

I have seen similar "unwrap on none" issues, but this one seems to happen in a different source location.
[+] Analyzing: fcn.402119dc @ 0x402119dc
  [*] Eliminating Dead Code
  [*] Propagating Constants
  [*] Eliminating More DeadCode
thread 'main' panicked at 'called `Option::unwrap()` on a `None` value', src/libcore/option.rs:325
stack backtrace:
   1:     0x55a94460f641 - std::sys::backtrace::tracing::imp::write::h46e546df6e4e4fe6
   2:     0x55a944614c41 - std::panicking::default_hook::_$u7b$$u7b$closure$u7d$$u7d$::h077deeda8b799591
   3:     0x55a94461487c - std::panicking::default_hook::heb8b6fd640571a4f
   4:     0x55a9446057c4 - std::panicking::rust_panic_with_hook::hd7b83626099d3416
   5:     0x55a944614ece - std::panicking::begin_panic::h941ea76fc945d925
   6:     0x55a94460636f - std::panicking::begin_panic_fmt::h30280d4dd3f149f5
   7:     0x55a944614e77 - rust_begin_unwind
   8:     0x55a94461eb5d - core::panicking::panic_fmt::h2d3cc8234dde51b4
   9:     0x55a94461ee03 - core::panicking::panic::heeca72c448510af4
  10:     0x55a9444ddad2 - _<core..option..Option<T>>::unwrap::hf974143bfcbe386e
                        at /builddir/build/BUILD/rustc-1.11.0/src/libcore/macros.rs:21
  11:     0x55a9444dd618 - radeco_lib::middle::ssa::ssastorage::SSAStorage::external::h1f5b13fa02c4fa7d
                        at /opt/radeco-lib/src/middle/ssa/ssastorage.rs:233
  12:     0x55a9444e47c3 - _<radeco_lib..middle..ssa..ssastorage..SSAStorage as radeco_lib..middle..ssa..cfg_traits..CFG>::exit_node::h63e586e9c197f278
                        at /opt/radeco-lib/src/middle/ssa/ssastorage.rs:337
  13:     0x55a94447c708 - radeco_lib::middle::dce::sweep::h725eb14d32f93186
                        at /opt/radeco-lib/src/middle/dce.rs:64
  14:     0x55a94447a32d - radeco_lib::middle::dce::collect::h52dafd5a519e870c
                        at /opt/radeco-lib/src/middle/dce.rs:20
  15:     0x55a944436ef7 - minidec::main::he08289d7edeea7b6
                        at /opt/radeco-lib/minidec/main.rs:46
  16:     0x55a94461445d - std::panicking::try::call::hca715a47aa047c49
  17:     0x55a94461c36b - __rust_try
  18:     0x55a94461c315 - __rust_maybe_catch_panic
  19:     0x55a944613ec9 - std::rt::lang_start::h162055cb2e4b9fe7
  20:     0x55a9444ad6c9 - main
  21:     0x7fac33c60730 - __libc_start_main
  22:     0x55a944436398 - _start
  23:                0x0 - <unknown>

I can share the binary if necessary.

Ok, this seems like a different issue and has something to do with accessing a node that does not exist. Could you please generate a log and attach it to the issue? That would help me a ton. If the binary is redistributable, please add a copy of that too.

Ok, seems like I don't have instructions on how to generate logs anywhere. I'll add that to minidec soon. But in the meanwhile if you can, please point me to the binary that causes this.

Binary: https://resetnow.ru/temp/r2-xtensa-crash

When I try to open that binary with objdump I get the error File truncated. Is it just me?

It appears it's broken, sorry! I will reupload it in about 6 hours.

Just remembered I shared it on Telegram earlier
esp8266-display.zip


chore: Update Example Setup

Reorganizing examples into a directory structure, starting with a quickstart.

It might be nice to also update the wasmcloud.com docs to reflect these updates if you wouldn't mind opening a similar PR over there as well

@joonas this is ready for another pass!


Rip out libvirt support

We are de-scoping libvirt support from Rancher and consequentially Cattle also.  We need to delete all the code refering to libvirt

Closed in favor of https://github.com/rancher/rancher/issues/3822


fix: anvil canvas highlight

Description

[!TIP]
Add a TL;DR when the description is longer than 500 words or extremely technical (helps the content, marketing, and DevRel team).
Please also include relevant motivation and context. List any dependencies that are required for this change. Add links to Notion, Figma or any other documents that might be relevant to the PR.

Fixes #Issue Number
or
Fixes Issue URL

[!WARNING]
If no issue exists, please create an issue first, and check with the maintainers if the issue is valid.

Automation
/ok-to-test<EMAIL_ADDRESS>:mag: Cypress test results

[!CAUTION]
🔴 🔴 🔴 Some tests have failed.
Workflow run: https://github.com/appsmithorg/appsmith/actions/runs/9694231211
Commit: 5e6ddee8a1be391e20477d37307026040e5e020f
Cypress dashboard.
Tags: @tag.Anvil
The following are new failures, please fix them before merging the PR:
cypress/e2e/Regression/ClientSide/Anvil/Widgets/AnvilCurrencyInputWidgetSnapshot_spec.ts
cypress/e2e/Regression/ClientSide/Anvil/Widgets/AnvilInputWidgetSnapshot_spec.ts
cypress/e2e/Regression/ClientSide/Anvil/Widgets/AnvilPhoneInputWidgetSnapshot_spec.ts
List of identified flaky tests.

Communication
Should the DevRel and Marketing teams inform users about this change?

[ ] Yes
[ ] No

/build-deploy-preview skip-tests=true


dhcpcd: 7.1.1 -> 7.2.0

Motivation for this change
https://roy.marples.name/blog/dhcpcd-7-2-0-released
D'oh there's a test to run that I haven't checked yet!
Things done

[x] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS)
Built on platform(s)

[x] NixOS
[ ] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nix-review --run "nix-review wip"
[ ] Tested execution of all binary files (usually in ./result/bin/)
[ ] Determined the impact on package closure size (by running nix path-info -S before and after)
[ ] Assured whether relevant documentation is up to date
[ ] Fits CONTRIBUTING.md.

@GrahamcOfBorg test networking.scripted


config question

Hello,
I've been looking at your image and have a question.  In the Dockerfile you copy: config to: /
and set ENV DJANGO_SETTINGS_MODULE config .
When I try to build from scratch I get :
ImportError: Could not import settings 'config' (Is it on sys.path? Is there an import error in the settings file?): No module named config
Strangely your image (on the docker hub), does not have the /config directory in / but still has the DJANGO_SETTINGS_MODULE=config environment variable set.
Am I missing something or did you later delete the /config directory?
Thanks
Chris

never mind... my bad.  I see that you are copying these files into /etc and /bin.
Thanks
Chris

no problem :)


Bug file unmanged

Huawei Kobackup version: 20200705
Host: [Windows / Linux ] Windows
Kobackup script version: 20200705
Kobackup output log (use -vvv) used -vvv
I have this message when I try to decode some files of one folder. The other files of that folder have been all decrypted, except some files, which are copied in a folder called "Uknown". Here is the output console message:
INFO:root:working on IMG_20190718_155409.jpg.enc
WARNING:root:entry IMG_20190718_155409.jpg.enc unmanged, copying it
How can I solve this?

Unmanaged means that the script was unable to find the decryption material related to that/those file(s). You could search in the XML files if there is any reference to that file (aka, bug in the script): if not, there is something I missed when reversing the app.