feat!: initial generation of beta client

generation of library from public googlewapis/googleapis protos.

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  Lines             ?      490
  Branches          ?        0
=========================================
  Hits              ?      475
  Misses            ?       15
  Partials          ?        0

Impacted Files
Coverage Δ

src/index.js
100% <100%> (ø)

samples/quickstart.js
94.87% <100%> (ø)

...c/v1beta1/document_understanding_service_client.js
96.31% <100%> (ø)

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SH script to create desktop icon.

Hey guys, I really like your application and wanted to have an icon on my desktop for quick-launching it, so I made the following script, maybe you'd like to add it to your release tarballs.
File: create-desktop-icon.sh

#!/usr/bin/bash

###
# Desktop icon maker for Kaku music player.
# @author SkyzohKey
# @license MIT
###

# Lets store the current path script is ran.
DIR=$(pwd)

# Store some informations for later usage.
APP_NAME="Kaku"
APP_DESCRIPTION="The next generation music client http://kaku.rocks"
APP_VERSION="1.7.0"
APP_CATEGORIES="Application;Music;"

# Define the file paths for easier configuration.
DESKTOP_FILE="${APP_NAME}.desktop"
LAUNCH_FILE="${APP_NAME}.sh"
LOGO_FILE="logo.png"

# Lets create our simple start script.
echo "# Set the working directory" > "$LAUNCH_FILE"
echo "DIR=\$(pwd)" >> "$LAUNCH_FILE"
echo "" >> "$LAUNCH_FILE"
echo "# Run the application" >> "$LAUNCH_FILE"
echo "cd \$DIR" >> "$LAUNCH_FILE"
echo "./${APP_NAME}" >> "$LAUNCH_FILE"

# Make it executable.
chmod +x "$LAUNCH_FILE"

# Lets create the desktop file.
echo "[Desktop Entry]" > "$DESKTOP_FILE"
echo "Encoding=UTF-8" >> "$DESKTOP_FILE"
echo "Name=${APP_NAME}" >> "$DESKTOP_FILE"
echo "Comment=${APP_DESCRIPTION}" >> "$DESKTOP_FILE"
echo "Path=${DIR}" >> "$DESKTOP_FILE"
echo "Exec=${DIR}/${LAUNCH_FILE}" >> "$DESKTOP_FILE"
echo "Icon=${DIR}/${LOGO_FILE}" >> "$DESKTOP_FILE"
echo "Categories=${APP_CATEGORIES}" >> "$DESKTOP_FILE"
echo "Version=${APP_VERSION}" >> "$DESKTOP_FILE"
echo "Type=Application" >> "$DESKTOP_FILE"
echo "Terminal=0" >> "$DESKTOP_FILE"

# Make it executable and move it to the user xdg-desktop.
chmod +x "$DESKTOP_FILE"
mv "$DESKTOP_FILE" "$(xdg-user-dir DESKTOP)/${DESKTOP_FILE}"

exit 0

My two cents.
I know that issues are not the place for this, but i was really lazy to make a PR. Shame on me, yeah 🐫
EDIT: I just updated the script, allows u to customize it a bit more.

Where should I put this script into Kaku for linux users ?
In the following releases, i'll release AppImage format for linux users ( http://appimage.org/), with this, does it help to support the same thing that what your script does ?

Thanks for the script by the way :)

@EragonJ Heya, this is a bash script, sort of .bat-like on Windoesn't.
To answer your (not so dumb) questions:

You simply have to put it at the release root (dist/OS-Arch-bla/ folder), then Linux guys just have to chmod it and run to enjoy a swag icon on their desktop. (chmod +x create-desktop-icon.sh; ./create-desktop-icon.sh)
AppImage is sort of .exe, but that's not working the same way. An appimage file is an iso-like file that will be mounted on the filesystem in order to execute the app contained into, unpacking the app's resources too. So yes, if user put that file on its desktop, that's pretty much the same, except that they won't have an icon (iirc appimages doesn't supports icons yet).

Anyway, I'm also a software developer and if you need help with AppImage, I can redirect you to that script, which was made by @probonopd who is the lead dev of AppImage. The main point of this script is to allow me having an appimage for every Travis-CI build. :)
Again, thanks for that super cool software you made :)

@SkyzohKey cooool, thanks for providing these information here, i'll add the script to root folder for linux build. And thanks for telling me details about AppImage thing :P


Fix grafana in all scenarios

Problem:

This is a manual backport of https://github.com/harvester/harvester-installer/pull/665, refer it.
Solution:

Add processing for all cases.
Related Issue:
https://github.com/harvester/harvester/issues/5236
Test plan:

Refer: https://github.com/harvester/harvester-installer/pull/665

gentle ping @w13915984028 , I've tried applied this pr and build with havester v1.2 branch with latest commit https://github.com/harvester/harvester/commit/a822a3a42ba324f11f2b95f4d0d91486eec89233 with 3 nodes while I found the Cluster Metrics still not work. Not sure what am I missing.

this is the chrome console output

And I also found that promethus-ranch-monitoring-prometheus is stuck in Init status, not sure if this is related

The promethues pod was not started successfully, stucking at init, k9s showed the volume was not mounted.
Use kubectl get pvc -n cattle-monitoring-system to find the volume name of pvc prometheus-rancher-monitoring-prometheus-db-prometheus-rancher-monitoring-prometheus-0; then kubectl get volume -n longhorn-system pvc-name -oyaml to get the detailed LH information.

The promethues pod was not started successfully, stucking at init, k9s showed the volume was not mounted.
Use kubectl get pvc -n cattle-monitoring-system to find the volume name of pvc prometheus-rancher-monitoring-prometheus-db-prometheus-rancher-monitoring-prometheus-0; then kubectl get volume -n longhorn-system pvc-name -oyaml to get the detailed LH information.

Huge thanks ! I discovered the volume wass detached due to insufficient disk space.
I've also confirmed that this pr is good to go, the metrics are now visible and Grafana login page no longer 404.


Add 2.x QEMU Guest Agents to the list of supported versions

What this PR does / why we need it:
After inspecting the guest agent API and our code, there's no reason we can't support Older QEMU Guest agents. As far back as 2.x
Which issue(s) this PR fixes
Fixes https://bugzilla.redhat.com/show_bug.cgi?id=1883857
Release note:
Version 2.x QEMU guest agents are supported.

/retest

/approve

/lgtm

/cherry-pick release-0.34


 Thank you for your submission, we really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.:x: Winslohw sign nowYou have signed the CLA already but the status is still pending? Let us recheck it.

 Thank you for your submission, we really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.:x: Winslohw sign nowYou have signed the CLA already but the status is still pending? Let us recheck it.
Originally posted by @microsoft-cla[bot] in https://github.com/Azure/azure-sdk-for-python/issues/23875#issuecomment-1091196422

Label prediction was below confidence level 0.6 for Model:ServiceLabels: 'Machine Learning Compute:0.39064533,Docs:0.10592467,EngSys:0.05654493'


Suggestions for Join Us page on chrisproject.org

Join us:
Maybe “These are some of the skillsets we're looking for.” Could be reworked as: It takes many different skill sets to build ChRIS. If you have any of these skills, you could help develop ChRIS. If you have other skills you would like to contribute to ChRIS, please let us know.
For the skillsets icons, I would like to see some more description links.
See https://github.com/FNNDSC/website/issues/20#issuecomment-740165487

Hey @mairin I would like to work on this.

Hi @mairin I would love to attempt this task, please assign it to me, thank you.


The hosted form example is not rendering.

The hosted form example is not rendering.
Render Error
Cannot read property 'constructor' of undefined
TypeError: Cannot read property 'constructor' of undefined
    at Object.isNothing (https://lumihq.github.io/purescript-lumi-components/main.a02721e2be46a1283be8.js:1:332771)
    at Object.edit (https://lumihq.github.io/purescript-lumi-components/7.581cb57507c7c0eb4284.js:1:38065)
    at Object.edit (https://lumihq.github.io/purescript-lumi-components/main.a02721e2be46a1283be8.js:1:319063)
    at Object.edit (https://lumihq.github.io/purescript-lumi-components/main.a02721e2be46a1283be8.js:1:256328)
    at Object.edit (https://lumihq.github.io/purescript-lumi-components/main.a02721e2be46a1283be8.js:1:836533)
    at Object.edit (https://lumihq.github.io/purescript-lumi-components/main.a02721e2be46a1283be8.js:1:319305)
    at Object.edit (https://lumihq.github.io/purescript-lumi-components/main.a02721e2be46a1283be8.js:1:319294)
    at Object.edit (https://lumihq.github.io/purescript-lumi-components/main.a02721e2be46a1283be8.js:1:319294)
    at https://lumihq.github.io/purescript-lumi-components/main.a02721e2be46a1283be8.js:1:814534
    at https://lumihq.github.io/purescript-lumi-components/main.a02721e2be46a1283be8.js:1:603242

    in UserFormExample
    in lumi-column
    in Column
    in lumi-column
    in Column
    in lumi-tab-content
    in lumi-column
    in Column
    in Tabs
    in Example
    in Example (Wrapper)
    in withRouter(Example (Wrapper))
    in lumi-column
    in Column
    in FormExample
    in Unknown
    in n
    in lumi-column
    in Column
    in t
    in t
    in e
    in lumi-column
    in Column
    in $
    in lumi-row
    in Row
    in div
    in X
    in lumi-column
    in Column
    in U
    in t
    in o
    in DragDropContext(o)
    in e
    in z
    in f
    in withRouter(f)
    in t
    in t

Fixed, thank you


Process Hacker still being detected after uninstall

Brief description of your issue
So I have no idea if this is something you can even fix, because it may not be on your end. Previously, when launching Honkai Star Rail with Process Hacker installed and running, the anticheat would flag it as a hacking tool. Obviously it is not, and I always found it strange that other hoyoverse games would launch fine, but Star Rail was the only one that had an issue with it. The solution was that any time I wanted to play Star Rail, I would simply close Process Hacker, and it would run fine. It only blocked launch if it was running. Unfortunately, that is no longer the case.
Now since switching to HoYoPlay launcher, Star Rail refuses to launch no matter what I do, and it claims Process Hacker is the issue. I tried shutting down the process, I tried upgrading to the new System Informer version, and I tried uninstalling from the windows app menu, and I have restarted my computer multiple times, including full shutdown, and starting the game as administrator. Star Rail tries to launch, but then crashes, blaming process hacker for the issue.
I am at a loss, and I know it is likely this cannot be fixed from your end, and it is simply the fault of the horrid kernel level anticheat, but I need to at least ask in case someone knows something that will work. I cannot find any remnant of process hacker left on my system, but somehow Hoyoverse can.
Here is the error I am given when the game force closes.

Steps to reproduce (optional)
Try to launch new version of Honkai Star Rail with Process Hacker installed
Expected behavior (optional)
The game launches and runs fine.
Actual behavior (optional)
The game closes itself upon detecting kprocesshacker.sys
Environment (optional)
Windows 10 Enterprise LTSC (10.0.19044 Build 19044)
Honkai Star Rail V2.3, HoYoPlay launcher
Originally Process Hacker version 2.39, updated to System Informer 3.0.7660, and uninstalled both.

i suggest that you need to scan all your harddrive for "kprocesshacker.sys" and delete it.
In case this file is locked, you need to find service "KProcessHacker", stop it, set the start mode to "disabled" and reboot. You can do it with SystemInformer on tab "Services".

Thank you! It seems to be working now. Of course it was something so simple


Remove warnings when starting eosio-web-ide

I get the following warnings in the first GitPod workspace window when starting eosio-web-ide project.  @tbfleming mentioned that this is due to a lack of debugging symbols and source maps may cure the issue.
WARNING in ./node_modules/eosjs/dist/eosjs-api-interfaces.js
Module Warning (from ./node_modules/source-map-loader/index.js):
(Emitted value instead of an instance of Error) Cannot find source file '../src/eosjs-api-interfaces.ts': Error: Can't resolve '../src/eosjs-api-interfaces.ts' in '/workspace/eosio-web-ide/webapp/node_modules/eosjs/dist'
 @ ./node_modules/eosjs/dist/index.js 5:20-53
 @ ./src/index.tsx

WARNING in ./node_modules/eosjs/dist/eosjs-api.js
Module Warning (from ./node_modules/source-map-loader/index.js):
(Emitted value instead of an instance of Error) Cannot find source file '../src/eosjs-api.ts': Error: Can't resolve '../src/eosjs-api.ts' in '/workspace/eosio-web-ide/webapp/node_modules/eosjs/dist'
 @ ./node_modules/eosjs/dist/index.js 3:18-40
 @ ./src/index.tsx

WARNING in ./node_modules/eosjs/dist/eosjs-jsonrpc.js
Module Warning (from ./node_modules/source-map-loader/index.js):
(Emitted value instead of an instance of Error) Cannot find source file '../src/eosjs-jsonrpc.ts': Error: Can't resolve '../src/eosjs-jsonrpc.ts' in '/workspace/eosio-web-ide/webapp/node_modules/eosjs/dist'
 @ ./node_modules/eosjs/dist/index.js 7:22-48
 @ ./src/index.tsx

WARNING in ./node_modules/eosjs/dist/eosjs-jssig.js
Module Warning (from ./node_modules/source-map-loader/index.js):
(Emitted value instead of an instance of Error) Cannot find source file '../src/eosjs-jssig.ts': Error: Can't resolve '../src/eosjs-jssig.ts' in '/workspace/eosio-web-ide/webapp/node_modules/eosjs/dist'
 @ ./src/index.tsx 8:22-55

WARNING in ./node_modules/eosjs/dist/eosjs-numeric.js
Module Warning (from ./node_modules/source-map-loader/index.js):
(Emitted value instead of an instance of Error) Cannot find source file '../src/eosjs-numeric.ts': Error: Can't resolve '../src/eosjs-numeric.ts' in '/workspace/eosio-web-ide/webapp/node_modules/eosjs/dist'
 @ ./node_modules/eosjs/dist/eosjs-jssig.js 53:22-48
 @ ./src/index.tsx

WARNING in ./node_modules/eosjs/dist/eosjs-rpc-interfaces.js
Module Warning (from ./node_modules/source-map-loader/index.js):
(Emitted value instead of an instance of Error) Cannot find source file '../src/eosjs-rpc-interfaces.ts': Error: Can't resolve '../src/eosjs-rpc-interfaces.ts' in '/workspace/eosio-web-ide/webapp/node_modules/eosjs/dist'
 @ ./node_modules/eosjs/dist/index.js 11:20-53
 @ ./src/index.tsx

WARNING in ./node_modules/eosjs/dist/eosjs-rpcerror.js
Module Warning (from ./node_modules/source-map-loader/index.js):
(Emitted value instead of an instance of Error) Cannot find source file '../src/eosjs-rpcerror.ts': Error: Can't resolve '../src/eosjs-rpcerror.ts' in '/workspace/eosio-web-ide/webapp/node_modules/eosjs/dist'
 @ ./node_modules/eosjs/dist/index.js 13:23-50
 @ ./src/index.tsx

WARNING in ./node_modules/eosjs/dist/eosjs-serialize.js
Module Warning (from ./node_modules/source-map-loader/index.js):
(Emitted value instead of an instance of Error) Cannot find source file '../src/eosjs-serialize.ts': Error: Can't resolve '../src/eosjs-serialize.ts' in '/workspace/eosio-web-ide/webapp/node_modules/eosjs/dist'
 @ ./node_modules/eosjs/dist/index.js 15:16-44
 @ ./src/index.tsx

WARNING in ./node_modules/eosjs/dist/index.js
Module Warning (from ./node_modules/source-map-loader/index.js):
(Emitted value instead of an instance of Error) Cannot find source file '../src/index.ts': Error: Can't resolve '../src/index.ts' in '/workspace/eosio-web-ide/webapp/node_modules/eosjs/dist'
 @ ./src/index.tsx 7:16-32
ℹ ｢wdm｣: Compiled with warnings.

It'd be ideal if we had some way to distribute source maps without blowing up eosjs NPM package sizes.

Or, if that's not feasible, a way to make it not warn about it.


MFA isssue

We have the text verification code enabled (when logging in off corporate network).
I think this code is not capturing the verification box correctly:
const tfaResult = content("#tfa_results_container>div").filter(function () {
return content(this).css('display') === 'block';
});
This is how the text box html is rendered (in IE):

            Verification code

            Enter verification code
<input name="code" class="login_textfield textfield required tfa_field normaltext" id="tfa_code_inputtext" style="background: none;" spellcheck="false" aria-label="Verification code" type="text" placeholder=" " value="" autocomplete="off">

Any ideas?
Note the Azure login page is showing a prompt for a "new" interface... Assuming the nodejs script is staying with the default option.
Thanks!

We are having the same issue here.

Same problem.

Microsoft must have changed something. It's still working properly for the app verification; it's just the text message verification that seems broken. For some reason the two text message divs are not showing up as visible in PhantomJS. I'll need to dig in some more...

I actually have app verification, not text

It seems that there is some delay between when the page loads and when the appropriate message is made visible. I'll put some fix in to check on a time interval. Hopefully I'll get to this tonight.

ok this solved it for me (works if the verification code box is shown or hidden):
line 154: const tfaResult = content("#tfa_title_text");
line 164: if (tfaMessage && tfaMessage.toLowerCase().indexOf("verify your account") >= 0) {
The 'title text' is immediately displayed but as author pointed out, the verification box as slight delay.

K. I just published v0.6.2 with a time delay loop that seems to resolve this.

I confirm that v0.6.2. works for me.


Can't hide header

When changing setHeadersState(HEADERS_ENABLED) to setHeadersState(HEADERS_DISABLED) on MainFragment.java, nothing is shown except for the logo and the search icon. I believed this is an issue w/ using the androidx.leanback library. Before migrating to use androidx leanback, the header can be disabled w/o affecting the main content. Any idea how to disable the header and still show the contents?

What sample are you referring to? If you think you have found an issue with the Leanback library, that should be filed in the AOSP issue tracker: https://issuetracker.google.com/components/192731

Closing this issue out from no response. If you believe this is a bug in the Leanback library, please file it in the AOSP tracker as Oscar mentioned.


Play specific songs

The user should be able to ask AskSonic to play the song Title by Artist and have AskSonic replace the queue with just that song

This is made horribly complicated by Amazon. They have a MusicRecording intent, but it guesses the song and artist you want even if you only said the song name. I tell it play song hello and it only sends the text "hello" by adele. Amazon does not allow you to use SearchQuery intent with another slot, so no title and artist.
It looks like the only option is to use the Searchquery intent and process the phrase in the code. So much for taking advantage of Alexa smarts.
Have you found anything else that might work?

OK, found the problem. First, your version of flask-ask can be synced with the upstream version. Then, the current version returns the first value in resolutions instead of the raw value. This is fine and indeed desired for most slot types. I propose adding a check for MusicRecording slot type to the first if conditional so that it will return the raw value for that case.
https://github.com/johnwheeler/flask-ask/blob/a93488b700479a3b2d80eb54d0f6585caae15ef3/flask_ask/core.py#L868

Sorry, it is better to handle this case in the new play song function directly. I just get the title from within the quotes and ignore the artist. Artist is only used when the user asks for it. I'm not sure what to do about the artist. The first result from Amazon for Lionel Richie is "Seibu Lionel Richie". REALLY Amazon? Really?
I'll keep looking for solutions.


adding code to check if we can actually write to the

refer to issue #29

Sorry it took so long to get to this, but LGTM


Who uses Backbone-Associations?

A compilation of projects which use Backbone-Associations. Please add your own project or product to this list. It would be wonderful and motivating to hear your story!

Using it heavily on our booking platform at http://breather.com :+1:


[10.0.x] NO-ISSUE: Use maven-m2-repo-via-http to build sonataflow-builder/devmode-images

This change makes the images buildable when the dependencies are not yet published in a remote Maven repository (i.e. when building a pre-release from source).
Note: The images are still not buildable in macOS or Windows, but that can be fixed later.

After this PR goes is merged inmain, we need to forward-port these fixes to main as well.

https://github.com/apache/incubator-kie-tools/pull/2612

PR for main: https://github.com/apache/incubator-kie-tools/pull/2617


ValueError: need at least one array to concatenate For htc

2021-01-06 16:27:00,066 - mmdet - INFO - Environment info:
sys.platform: linux
Python: 3.7.9 (default, Aug 31 2020, 12:42:55) [GCC 7.3.0]
CUDA available: True
GPU 0: GeForce RTX 2080 Ti
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 10.2, V10.2.89
GCC: gcc (Uos <IP_ADDRESS>-3+rebuild) 8.3.0
PyTorch: 1.6.0
PyTorch compiling details: PyTorch built with:

GCC 7.3
C++ Version: 201402
Intel(R) Math Kernel Library Version 2020.0.2 Product Build 20200624 for Intel(R) 64 architecture applications
Intel(R) MKL-DNN v1.5.0 (Git Hash e2ac1fac44c5078ca927cb9b90e1b3066a0b2ed0)
OpenMP 201511 (a.k.a. OpenMP 4.5)
NNPACK is enabled
CPU capability usage: AVX2
CUDA Runtime 10.2
NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_37,code=compute_37
CuDNN 7.6.5
Magma 2.5.2
Build settings: BLAS=MKL, BUILD_TYPE=Release, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DUSE_VULKAN_WRAPPER -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, USE_CUDA=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_STATIC_DISPATCH=OFF,

TorchVision: 0.7.0
OpenCV: 4.4.0
MMCV: 1.2.1
MMCV Compiler: GCC 8.3
MMCV CUDA Compiler: 10.2
MMDetection: 2.7.0+3e902c3
2021-01-06 16:27:00,354 - mmdet - INFO - Distributed training: False
2021-01-06 16:27:00,636 - mmdet - INFO - Config:
dataset_type = 'XipanDataset'
classes = ('crane', 'car')
data_root = '/home/remon/xipan/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
dict(type='Resize', img_scale=(1920, 1080), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='SegRescale', scale_factor=0.125),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg'])
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1920, 1080),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]
data = dict(
samples_per_gpu=1,
workers_per_gpu=1,
train=dict(
classes=('crane', 'car'),
type='XipanDataset',
ann_file='/home/remon/xipan/coco/annotations/instances_train2017.json',
img_prefix='/home/remon/xipan/coco/train2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
with_seg=True),
dict(type='Resize', img_scale=(1920, 1080), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='SegRescale', scale_factor=0.125),
dict(type='DefaultFormatBundle'),
dict(
type='Collect',
keys=[
'img', 'gt_bboxes', 'gt_labels', 'gt_masks',
'gt_semantic_seg'
])
],
seg_prefix='/home/remon/xipan/coco/stuffthingmaps/train2017/'),
val=dict(
classes=('crane', 'car'),
type='XipanDataset',
ann_file='/home/remon/xipan/coco/annotations/instances_val2017.json',
img_prefix='/home/remon/xipan/coco/val2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1920, 1080),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]),
test=dict(
classes=('crane', 'car'),
type='XipanDataset',
ann_file='/home/remon/xipan/coco/annotations/instances_val2017.json',
img_prefix='/home/remon/xipan/coco/val2017/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1920, 1080),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
dict(
type='Normalize',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
to_rgb=True),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img'])
])
]))
evaluation = dict(metric=['bbox', 'segm'])
optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[16, 19])
total_epochs = 20
checkpoint_config = dict(interval=1)
log_config = dict(
interval=50,
hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
model = dict(
type='HybridTaskCascade',
pretrained='open-mmlab://resnext101_64x4d',
backbone=dict(
type='ResNeXt',
depth=101,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
groups=16,
base_width=4),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5),
rpn_head=dict(
type='RPNHead',
in_channels=256,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
scales=[8],
ratios=[0.5, 1.0, 2.0],
strides=[4, 8, 16, 32, 64]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[1.0, 1.0, 1.0, 1.0]),
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
loss_bbox=dict(
type='SmoothL1Loss', beta=0.1111111111111111, loss_weight=1.0)),
roi_head=dict(
type='HybridTaskCascadeRoIHead',
interleaved=True,
mask_info_flow=True,
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
bbox_head=[
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=2,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.1, 0.1, 0.2, 0.2]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=2,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
loss_weight=1.0)),
dict(
type='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=2,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0.0, 0.0, 0.0, 0.0],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss',
use_sigmoid=False,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=[
dict(
type='HTCMaskHead',
with_conv_res=False,
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=2,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
dict(
type='HTCMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=2,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
dict(
type='HTCMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=2,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))
],
semantic_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[8]),
semantic_head=dict(
type='FusedSemanticHead',
num_ins=5,
fusion_level=1,
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=2,
ignore_label=255,
loss_weight=0.2)))
train_cfg = dict(
rpn=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.3,
min_pos_iou=0.3,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=256,
pos_fraction=0.5,
neg_pos_ub=-1,
add_gt_as_proposals=False),
allowed_border=0,
pos_weight=-1,
debug=False),
rpn_proposal=dict(
nms_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=[
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False),
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.7,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
mask_size=28,
pos_weight=-1,
debug=False)
])
test_cfg = dict(
rpn=dict(
nms_across_levels=False,
nms_pre=1000,
nms_post=1000,
max_num=1000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=dict(
score_thr=0.001,
nms=dict(type='nms', iou_threshold=0.5),
max_per_img=100,
mask_thr_binary=0.5))
work_dir = './work_dirs/htc_x101_64x4d_fpn_16x1_20e_coco'
gpu_ids = range(0, 1)
args.seed: None
exp_name: htc_x101_64x4d_fpn_16x1_20e_coco.py
2021-01-06 16:27:01,241 - mmdet - INFO - load model from: open-mmlab://resnext101_64x4d
2021-01-06 16:27:01,361 - mmdet - WARNING - The model and loaded state dict do not match exactly
size mismatch for layer1.0.conv1.weight: copying a param with shape torch.Size([256, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 64, 1, 1]).
size mismatch for layer1.0.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.0.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.0.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.0.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.0.conv2.weight: copying a param with shape torch.Size([256, 4, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 4, 3, 3]).
size mismatch for layer1.0.bn2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.0.bn2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.0.bn2.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.0.bn2.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.0.conv3.weight: copying a param with shape torch.Size([256, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 64, 1, 1]).
size mismatch for layer1.1.conv1.weight: copying a param with shape torch.Size([256, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 256, 1, 1]).
size mismatch for layer1.1.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.1.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.1.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.1.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.1.conv2.weight: copying a param with shape torch.Size([256, 4, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 4, 3, 3]).
size mismatch for layer1.1.bn2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.1.bn2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.1.bn2.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.1.bn2.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.1.conv3.weight: copying a param with shape torch.Size([256, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 64, 1, 1]).
size mismatch for layer1.2.conv1.weight: copying a param with shape torch.Size([256, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([64, 256, 1, 1]).
size mismatch for layer1.2.bn1.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.2.bn1.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.2.bn1.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.2.bn1.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.2.conv2.weight: copying a param with shape torch.Size([256, 4, 3, 3]) from checkpoint, the shape in current model is torch.Size([64, 4, 3, 3]).
size mismatch for layer1.2.bn2.weight: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.2.bn2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.2.bn2.running_mean: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.2.bn2.running_var: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([64]).
size mismatch for layer1.2.conv3.weight: copying a param with shape torch.Size([256, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 64, 1, 1]).
size mismatch for layer2.0.conv1.weight: copying a param with shape torch.Size([512, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).
size mismatch for layer2.0.bn1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.0.bn1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.0.bn1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.0.bn1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.0.conv2.weight: copying a param with shape torch.Size([512, 8, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 8, 3, 3]).
size mismatch for layer2.0.bn2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.0.bn2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.0.bn2.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.0.bn2.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.0.conv3.weight: copying a param with shape torch.Size([512, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 128, 1, 1]).
size mismatch for layer2.1.conv1.weight: copying a param with shape torch.Size([512, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1]).
size mismatch for layer2.1.bn1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.1.bn1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.1.bn1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.1.bn1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.1.conv2.weight: copying a param with shape torch.Size([512, 8, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 8, 3, 3]).
size mismatch for layer2.1.bn2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.1.bn2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.1.bn2.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.1.bn2.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.1.conv3.weight: copying a param with shape torch.Size([512, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 128, 1, 1]).
size mismatch for layer2.2.conv1.weight: copying a param with shape torch.Size([512, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1]).
size mismatch for layer2.2.bn1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.2.bn1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.2.bn1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.2.bn1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.2.conv2.weight: copying a param with shape torch.Size([512, 8, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 8, 3, 3]).
size mismatch for layer2.2.bn2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.2.bn2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.2.bn2.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.2.bn2.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.2.conv3.weight: copying a param with shape torch.Size([512, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 128, 1, 1]).
size mismatch for layer2.3.conv1.weight: copying a param with shape torch.Size([512, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 512, 1, 1]).
size mismatch for layer2.3.bn1.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.3.bn1.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.3.bn1.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.3.bn1.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.3.conv2.weight: copying a param with shape torch.Size([512, 8, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 8, 3, 3]).
size mismatch for layer2.3.bn2.weight: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.3.bn2.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.3.bn2.running_mean: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.3.bn2.running_var: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for layer2.3.conv3.weight: copying a param with shape torch.Size([512, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 128, 1, 1]).
size mismatch for layer3.0.conv1.weight: copying a param with shape torch.Size([1024, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 512, 1, 1]).
size mismatch for layer3.0.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.0.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.0.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.0.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.0.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.0.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.0.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.0.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.0.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.0.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.1.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.1.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.1.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.1.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.1.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.1.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.1.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.1.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.1.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.1.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.1.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.2.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.2.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.2.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.2.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.2.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.2.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.2.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.2.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.2.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.2.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.2.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.3.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.3.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.3.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.3.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.3.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.3.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.3.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.3.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.3.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.3.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.3.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.4.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.4.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.4.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.4.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.4.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.4.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.4.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.4.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.4.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.4.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.4.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.5.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.5.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.5.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.5.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.5.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.5.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.5.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.5.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.5.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.5.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.5.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.6.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.6.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.6.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.6.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.6.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.6.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.6.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.6.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.6.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.6.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.6.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.7.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.7.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.7.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.7.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.7.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.7.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.7.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.7.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.7.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.7.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.7.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.8.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.8.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.8.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.8.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.8.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.8.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.8.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.8.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.8.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.8.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.8.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.9.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.9.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.9.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.9.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.9.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.9.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.9.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.9.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.9.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.9.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.9.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.10.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.10.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.10.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.10.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.10.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.10.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.10.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.10.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.10.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.10.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.10.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.11.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.11.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.11.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.11.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.11.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.11.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.11.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.11.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.11.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.11.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.11.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.12.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.12.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.12.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.12.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.12.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.12.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.12.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.12.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.12.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.12.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.12.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.13.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.13.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.13.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.13.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.13.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.13.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.13.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.13.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.13.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.13.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.13.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.14.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.14.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.14.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.14.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.14.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.14.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.14.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.14.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.14.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.14.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.14.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.15.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.15.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.15.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.15.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.15.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.15.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.15.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.15.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.15.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.15.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.15.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.16.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.16.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.16.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.16.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.16.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.16.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.16.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.16.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.16.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.16.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.16.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.17.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.17.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.17.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.17.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.17.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.17.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.17.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.17.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.17.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.17.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.17.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.18.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.18.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.18.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.18.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.18.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.18.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.18.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.18.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.18.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.18.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.18.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.19.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.19.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.19.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.19.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.19.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.19.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.19.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.19.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.19.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.19.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.19.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.20.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.20.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.20.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.20.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.20.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.20.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.20.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.20.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.20.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.20.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.20.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.21.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.21.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.21.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.21.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.21.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.21.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.21.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.21.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.21.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.21.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.21.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer3.22.conv1.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 1024, 1, 1]).
size mismatch for layer3.22.bn1.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.22.bn1.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.22.bn1.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.22.bn1.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.22.conv2.weight: copying a param with shape torch.Size([1024, 16, 3, 3]) from checkpoint, the shape in current model is torch.Size([256, 16, 3, 3]).
size mismatch for layer3.22.bn2.weight: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.22.bn2.bias: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.22.bn2.running_mean: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.22.bn2.running_var: copying a param with shape torch.Size([1024]) from checkpoint, the shape in current model is torch.Size([256]).
size mismatch for layer3.22.conv3.weight: copying a param with shape torch.Size([1024, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([1024, 256, 1, 1]).
size mismatch for layer4.0.conv1.weight: copying a param with shape torch.Size([2048, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 1024, 1, 1]).
size mismatch for layer4.0.bn1.weight: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.0.bn1.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.0.bn1.running_mean: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.0.bn1.running_var: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.0.conv2.weight: copying a param with shape torch.Size([2048, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 32, 3, 3]).
size mismatch for layer4.0.bn2.weight: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.0.bn2.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.0.bn2.running_mean: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.0.bn2.running_var: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.0.conv3.weight: copying a param with shape torch.Size([2048, 2048, 1, 1]) from checkpoint, the shape in current model is torch.Size([2048, 512, 1, 1]).
size mismatch for layer4.1.conv1.weight: copying a param with shape torch.Size([2048, 2048, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 2048, 1, 1]).
size mismatch for layer4.1.bn1.weight: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.1.bn1.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.1.bn1.running_mean: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.1.bn1.running_var: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.1.conv2.weight: copying a param with shape torch.Size([2048, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 32, 3, 3]).
size mismatch for layer4.1.bn2.weight: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.1.bn2.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.1.bn2.running_mean: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.1.bn2.running_var: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.1.conv3.weight: copying a param with shape torch.Size([2048, 2048, 1, 1]) from checkpoint, the shape in current model is torch.Size([2048, 512, 1, 1]).
size mismatch for layer4.2.conv1.weight: copying a param with shape torch.Size([2048, 2048, 1, 1]) from checkpoint, the shape in current model is torch.Size([512, 2048, 1, 1]).
size mismatch for layer4.2.bn1.weight: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.2.bn1.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.2.bn1.running_mean: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.2.bn1.running_var: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.2.conv2.weight: copying a param with shape torch.Size([2048, 32, 3, 3]) from checkpoint, the shape in current model is torch.Size([512, 32, 3, 3]).
size mismatch for layer4.2.bn2.weight: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.2.bn2.bias: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.2.bn2.running_mean: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.2.bn2.running_var: copying a param with shape torch.Size([2048]) from checkpoint, the shape in current model is torch.Size([512]).
size mismatch for layer4.2.conv3.weight: copying a param with shape torch.Size([2048, 2048, 1, 1]) from checkpoint, the shape in current model is torch.Size([2048, 512, 1, 1]).
loading annotations into memory...
Done (t=0.01s)
creating index...
index created!
loading annotations into memory...
Done (t=0.00s)
creating index...
index created!
[<torch.utils.data.dataloader.DataLoader object at 0x7fe69d809c90>] [('train', 1)] 20
2021-01-06 16:27:02,934 - mmdet - INFO - Start running, host: remon@remon-PC, work_dir: /home/remon/mmdetection2.7/work_dirs/htc_x101_64x4d_fpn_16x1_20e_coco
2021-01-06 16:27:02,934 - mmdet - INFO - workflow: [('train', 1)], max: 20 epochs
Traceback (most recent call last):
File "./tools/train.py", line 182, in
main()
File "./tools/train.py", line 178, in main
meta=meta)
File "/home/remon/mmdetection2.7/mmdet/apis/train.py", line 150, in train_detector
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
File "/home/remon/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 125, in run
epoch_runner(data_loaders[i], **kwargs)
File "/home/remon/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/mmcv/runner/epoch_based_runner.py", line 47, in train
for i, data_batch in enumerate(self.data_loader):
File "/home/remon/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 291, in iter
return _MultiProcessingDataLoaderIter(self)
File "/home/remon/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 764, in init
self._try_put_index()
File "/home/remon/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 994, in _try_put_index
index = self._next_index()
File "/home/remon/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/dataloader.py", line 357, in _next_index
return next(self._sampler_iter)  # may raise StopIteration
File "/home/remon/anaconda3/envs/open-mmlab/lib/python3.7/site-packages/torch/utils/data/sampler.py", line 208, in iter
for idx in self.sampler:
File "/home/remon/mmdetection2.7/mmdet/datasets/samplers/group_sampler.py", line 36, in iter
indices = np.concatenate(indices)
File "<array_function internals>", line 6, in concatenate
ValueError: need at least one array to concatenate
Bug fix
If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated!

Hi, the above error is caused by  concatenating from an empty indices. And the empty indices should be caused by the empty dataset.flag. But I'm not sure whether this is caused by an empty  data_infos or not.
Here we give a more detailed docs for training your customized dataset with COCO format under MMDetection.

Hi, the above error is caused by  concatenating from an empty indices. And the empty indices should be caused by the empty dataset.flag. But I'm not sure whether this is caused by an empty  data_infos or not.
Here we give a more detailed docs for training your customized dataset with COCO format under MMDetection.


fix deprecated ugettext method call for django 4

This pull request contains a fix in the ugettext call in the constants.py file.
That method has been deprecated in Django 4> where the package throws errors when running the Django server.
Fixes #65

@juliomalegria - can you please accept this pull request and release the package update.
Thanks.

@juliomalegria - can you please accept this pull request and release the package update.
Thanks.

Looks like @juliomalegria no longer maintains the package.
@kishanmmohan


predefine font-family for windows 10~11 from ar5iv site

It will be nice to set default font-family for windows 10~11 from ar5iv site, because default font-family on win is not well adjusted as on mac.

Sounds like an issue for the styling side at ar5iv-css. We can keep this open here.
Could you share a screenshot of what you see on your windows device?
Also, hopefully, the font family is explicitly set on all platforms. It is defined in the two CSS variables here:
https://github.com/dginev/ar5iv-css/blob/main/css/ar5iv.css#L7-L8

default with Microsot Yahei :

adjusted with Lucia bright font:

Excellent to have these screenshots, thank you! Some more deliberate preparation seems to be necessary then. Either to get Noto distributed within ar5iv, or to choose a good Windows fallback...

Sounds like an issue for the styling side at ar5iv-css. We can keep this open here.
Could you share a screenshot of what you see on your windows device? Also, hopefully, the font family is explicitly set on all platforms. It is defined in the two CSS variables here: https://github.com/dginev/ar5iv-css/blob/main/css/ar5iv.css#L7-L8

seems Noto does not match Cambria Math very well:

Maybe need to use Lucida, Times New Rome  instead, or change Cambria Math to other font

Hi @cailiang9 ! Could you take a look at https://ar5iv.org/html/1801.04381 and check if your system can load the intended Noto fonts now? I just deployed a method to serve them together with the article content.
If this works as intended, the abstract should appear as:

Excellent, thank you for confirming.


Missing instructions on how to setup development IIS express host with Visual Studio Code

https://docs.microsoft.com/en-us/aspnet/core/host-and-deploy/aspnet-core-module?view=aspnetcore-3.0

[x] .NET Core 3.0
[ ] .NET Framework
[ ] .NET Standard

Hello @wrkettlitz ... btw -- Please use the This Page feedback button at the bottom of any topic. It helps us track work on the repo.
That's not a supported scenario for VSC. VSC uses Omnisharp for debugging.
I think you probably can find some resources out around the Net (at your own risk in some cases). For example, you'll probably find some cats explaining how to use IIS Express from the command line and then making that a task in VSC. There is also at least one 3rd party extension that will do it. You could try some kind of 🙉 HACK! 🙈 that might work (for example, see this), but I don't recommend it. Outside of IIS Express from the CLI, these approaches are NOT supported by Microsoft!  Unfortunately, our local dev with IIS scenario is VS-only at this time ... covered in Development-time IIS support in Visual Studio for ASP.NET Core.
To request a scenario like that for VSC, open an issue with the Omnisharp folks at https://github.com/OmniSharp/omnisharp-vscode/issues. They might be open to mirroring the kind of scenario you see in that VSC extension in their feature set OOB.