the result of onnx is different from use model  inference.

the result of onnx is different from use model  inference.
how to sovle it ?
the result of use my model

the result of use onnx

what code did you change, and what code did you run? More details are needed.

i have deletde the simple_test to export the onnx。 and the my self model is code is that:
@DETECTORS.register_module()
class LGPMA(TwoStageDetector):
"""Implementation of LGPMA detector model.
Ref: Qiao L, Li Z, Cheng Z, et al. LGPMA: Complicated Table Structure Recognition with Local and Global Pyramid Mask
 Alignment[J]. arXiv preprint arXiv:2105.06224, 2021. (Accepted by ICDAR 2021, Best Industry Paper)

"""

def __init__(self,
             backbone,
             rpn_head,
             roi_head,
             train_cfg,
             test_cfg,
             neck=None,
             pretrained=None,
             global_seg_head=None
             ):
    """
    Args:
        backbone(dict): network backbone (e.g. ResNet)
        rpn_head(dict): rpn head
        roi_head(dict): roi_head
        train_cfg(dict): related parameters for training
        test_cfg(dict): related parameters for test
        neck(dict): network neck (e.g., FPN)
        pretrained(dict): pretrained model
        global_seg_head: global segmentation head
    """

    super().__init__(
        backbone=backbone,
        neck=neck,
        rpn_head=rpn_head,
        roi_head=roi_head,
        train_cfg=train_cfg,
        test_cfg=test_cfg,
        pretrained=pretrained)

    if global_seg_head is not None:
        self.global_seg_head = builder.build_head(global_seg_head)
        if isinstance(self.global_seg_head, nn.Sequential):
            for module in self.global_seg_head:
                module.init_weights()
        else:
            self.global_seg_head.init_weights()
    else:
        self.global_seg_head = None

@property
def with_global_seg(self):
    """
    Returns:
        boolean:Determine the model with the global_seg_head or not
    """

    return hasattr(self, 'global_seg_head') and self.global_seg_head is not None

def forward_train(self,
                  img,
                  img_metas,
                  gt_bboxes,
                  gt_labels,
                  gt_bboxes_ignore=None,
                  gt_masks=None,
                  proposals=None,
                  gt_semantic_seg=None,
                  **kwargs):
    """ Forward train process.

    Args:
        img (Tensor): of shape (N, C, H, W) encoding input images.
            Typically these should be mean centered and std scaled.
        img_metas (list[dict]): list of image info dict where each dict has: 'img_shape', 'scale_factor', 'flip',
            and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
            For details on the values of these keys see `mmdet/datasets/pipelines/formatting.py:Collect`.
        gt_bboxes (list[Tensor]): Ground truth bboxes for each image with shape (num_gts, 4)
            in [tl_x, tl_y, br_x, br_y] format.
        gt_labels (list[Tensor]): class indices corresponding to each box
        gt_bboxes_ignore (None | list[Tensor]): specify which bounding boxes can be ignored when computing the loss.
        gt_masks (None | Tensor) : true segmentation masks for each box used if the architecture supports a
            segmentation task.
        proposals: override rpn proposals with custom proposals. Use when `with_rpn` is False.
        gt_semantic_seg (None | Tensor) : true global segmentation masks for the whole image used if the
            architecture supports a global segmentation task.

    Returns:
        dict[str, Tensor]: a dictionary of loss components
    """

    x = self.extract_feat(img)

    losses = dict()

    # RPN forward and loss
    if self.with_rpn:
        proposal_cfg = self.train_cfg.get('rpn_proposal',
                                          self.test_cfg.rpn)
        rpn_losses, proposal_list = self.rpn_head.forward_train(
            x,
            img_metas,
            gt_bboxes,
            gt_labels=None,
            gt_bboxes_ignore=gt_bboxes_ignore,
            proposal_cfg=proposal_cfg)
        losses.update(rpn_losses)
    else:
        proposal_list = proposals

    roi_losses = self.roi_head.forward_train(x, img_metas, proposal_list,
                                             gt_bboxes, gt_labels,
                                             gt_bboxes_ignore, gt_masks,
                                             **kwargs)
    losses.update(roi_losses)

    # global forward and loss
    if self.with_global_seg:
        # Change shape to compatible DavarDefaultFormatBundle
        gt_semantic_seg = gt_semantic_seg[:, 0, :, :, :]
        seg_pred = self.global_seg_head(x)
        seg_targets = self.global_seg_head.get_target(gt_semantic_seg)
        loss_global_seg = self.global_seg_head.loss(seg_pred, seg_targets)
        losses.update(loss_global_seg)

    return losses

# 导出onnx需要注释掉这里
# def simple_test(self, img, img_metas, proposals=None, rescale=False):
#     """ Forward test process

#     Args:
#         img(Tensor): input images
#         img_metas(dict): image meta infos
#         proposals(None | list): if region proposals is assigned before, using it
#         rescale(boolean): if the image be re-scaled

#     Returns:
#         list(str): Format results, like [html of table1 (str), html of table2 (str), ...]

#     """
#     assert self.with_mask, 'Mask head must be implemented.'
#     results = super(LGPMA, self).simple_test(img, img_metas, proposals, rescale)
#     if self.with_global_seg:
#         x = self.extract_feat(img)
#         h_img, w_img = img.shape[1], img.shape[2]
#         global_seg_pred = self.global_seg_head(x)
#         global_seg_results = self.global_seg_head.get_seg_masks(global_seg_pred, img_metas, (h_img, w_img))

#         results = [res + (seg,) for res, seg in zip(results, global_seg_results)]
#     if hasattr(self.test_cfg, 'postprocess'):
#         post_processor = build_postprocess(self.test_cfg.postprocess)
#         results = post_processor(results)

#     return results

I use the mmdection  2.10.0 version pytorch2onnx.py scripy to export onnx.

Please @RunningLeon

@cumthxy

Hello, i used style of mmdetection2.10.0 to convert LGPMA to ONNX, but it show onnx does not support argsort. can you share the  relevant coda, thank you

@xjl-le Hi, your torch version is too low. May need to update to torch>=1.6.0.

@RunningLeon my torch version is 1.8.0, onnx:1.8.0, torchvision:0.9.0. the LGPMA is implemented based on Mask rcnn, could this problem be caused by mmdetection2.10.0 not supporting Mask rcnn?


feat: pm2 ecosystem file icon

Description
Closes: #2707
Contribution Guidelines

[x] By creating this pull request, I acknowledge that I have read the Contribution Guidelines for this project
[x] I have read the Code Of Conduct and promise to abide by these rules

There are a few issues.

The icon doesn't use de material color palette
It doesn't have margins (we usually design icons at 16x16 -or a multiple of 16x16, like 32x32, etc- with at least 1px of margin; designing your icon using a 1:1 viewport will help you!
Ideally, the icon should be aligned to a 16x16 grid. It's going to be hard for this one, but at least straight horizontal lines can be aligned

Please read the Contributing guide for more details.

Also, it seems to me that there are too many details in this icon

I made this simplified version... it doesn't have all the details from the original as I tried to optimize it to be rendered at 16px
What do you guys think?

I made this simplified version... it doesn't have all the details from the original as I tried to optimize it to be rendered at 16px
What do you guys think?

This is great. We really need an ecosystem.config.* icon for the projects relying on PM2. I think this is great.

I made this simplified version... it doesn't have all the details from the original as I tried to optimize it to be rendered at 16px
What do you guys think?

It looks good to me, from my point of view we can add this icon! Are you able to change this PR with that icon?

I forgot about this one! Sure, let me search for it (I hope I didn’t delete the file haha)

Aight, I think we're good. Sorry for all the reviews-mess. I'm on a new dev pc and I haven't installed most dev tools yet 😅


Not able to install in Ubuntu 19.10/ Python 3.7

Hi,
I'm not able to install subsync due to following error:
$ pip3 install subsync
...
ERROR: Could not find a version that satisfies the requirement tensorflow==1.5.1 (from subsync) (from versions: 1.13.0rc1, 1.13.0rc2, 1.13.1, 1.13.2, 1.14.0rc0, 1.14.0rc1, 1.14.0, 1.15.0rc0, 1.15.0rc1, 1.15.0rc2, 1.15.0rc3, 1.15.0, 1.15.2, 2.0.0a0, 2.0.0b0, 2.0.0b1, 2.0.0rc0, 2.0.0rc1, 2.0.0rc2, 2.0.0, 2.0.1, 2.1.0rc0, 2.1.0rc1, 2.1.0rc2, 2.1.0)
ERROR: No matching distribution found for tensorflow==1.5.1 (from subsync)

Seems to me that tensorflow 1.5.1 is not available for python 3.7 (just 1.13 onwards).
This issue is slightly different to this one, as in that case it seem like a network issue or something and there was no version at all.
https://github.com/tympanix/subsync/issues/9
Thanks!

I think you should try to force install another version of tensorflow. The versions listed above should be those compatible with your system. Although subsync was tested and implemented with tensorflow 1.5.1, I think other versions of tensorflow should work. Maybe the hardcoded tensorflow version should be removed from requirements.txt?


Security Warning for Birt Report Engine

Summary
A security warning appears for the URL of the Birt Reporting Engine at the attempt to show a Report.
In Chrome it is possible to "Accept the Risk" and proceed to the URL, but in Firefox not. Therefore in Firefox the Reports cannot be displayed properly.
Acceptance criteria

[ ] Solution for Firefox for enabling the option to proceed to the unsafe URL, just like in Chrome.

Additional context
N/A.

This works as expected. The browser warns on the selfsigned certificates. You need obtain and import a valid SSL certificate for your OSCM host domain in OSCM. The detailled procedure is described in the Getting Started Guide.
In order to work with the self-signed certificates, e.g. for example in staging invironments, you need to accept such warnings. Just open a new browser tab and copy the URL displayed in the BIRT Iframe from the report dialog into the location bar in the new tab. Then your browser will give you the button that let's you to bypass and accept with the unsecure certificate.


[RESOLVED] Interface eth0(): High error rate (>2 for 5m)

Problem started at 10:03:29 on 2023.05.19
Problem name: Interface eth0(): High error rate (>2 for 5m)
Host: S100-Primario
Severity: Warning
Operational data: errors in: 1, errors out: 0
Original problem ID: 20947
Event details in Zabbix: http://<IP_ADDRESS>/tr_events.php?triggerid=23483&eventid=20947

Problem has been resolved in 3m 0s at 10:06:29 on 2023.05.19
Problem name: Interface eth0(): High error rate (>2 for 5m)
Host: S100-Primario
Severity: Warning
Original problem ID: 20947


Check plaintext transmission such as front-end password login

At present, passwords may be exposed in the browser during login or registration, which is a vulnerability

Fixed


New API basic support

Short Description:

Changed the api.activate_signature functions for the new api.set_signature and api.set_hash_lib functions.
Modified the requirements.txt to include the latest pgoapi API.

I made some changes now.
I still need a condition for the freebsd-64.so file.
And the web map is not showing Pokemon or the player in the map so far.
The bot client is working correctly so far.

if _platform.lower() == "linux" or _platform.lower() == "linux2":
    file_name = 'src/pgoapi/pgoapi/lib/libniantichash-linux-x86-64.so'

In get_encrypt_lib(), the file_name should be `libencrypt-linux-x86-64.so'.

@lukasz138 try @enginebai solution, i've updated my branch (in a very ugly way) in order for it to work on linux now.

Exception: Could not find linux2 encryption library /root/pgoapi/PokemonGo-Bot/src/pgoapi/pgoapi/lib/libencrypt-linux-x86-32.so
2016-11-07 14:16:36,491 [sentry.errors] [ERROR] Sentry responded with an error: 'module' object has no attribute 'HTTPSHandler' (url: https://app.getsentry.com/api/90254/store/)
Traceback (most recent call last):
File "/root/pgoapi/PokemonGo-Bot/local/lib/python2.7/site-packages/raven/transport/threaded.py", line 174, in send_sync
super(ThreadedHTTPTransport, self).send(data, headers)
File "/root/pgoapi/PokemonGo-Bot/local/lib/python2.7/site-packages/raven/transport/http.py", line 47, in send
ca_certs=self.ca_certs,
File "/root/pgoapi/PokemonGo-Bot/local/lib/python2.7/site-packages/raven/utils/http.py", line 44, in urlopen
class ValidHTTPSHandler(urllib2.HTTPSHandler):
AttributeError: 'module' object has no attribute 'HTTPSHandler'

@lukasz138 run pip install -r requirements.txt to download the new API to the src directory.

still the same

@lukasz138 I encountered this problem before, and solved by locating the current encryption and hash lib file. Make sure ./pokemon_bot/__init__.py is updated as this PR.

Problem is in pgoapi/utilities.py : (there is no 32-bit libraries)
elif sys.platform.startswith('linux'):
if "centos" in platform.platform():
if platform.architecture()[0] == '64bit':
encrypt_lib = "libencrypt-centos-x86-64.so"
hash_lib = "libniantichash-centos-x86-64.so"
else:
encrypt_lib = "libencrypt-linux-x86-32.so"
hash_lib = "libniantichash-linux-x86-32.so"
else:
if platform.architecture()[0] == '64bit':
encrypt_lib = "libencrypt-linux-x86-64.so"
hash_lib = "libniantichash-linux-x86-64.so"
else:
encrypt_lib = "libencrypt-linux-x86-32.so"
hash_lib = "libniantichash-linux-x86-32.so"

Can you make the PR for extra libs here? https://github.com/pogodevorg/pgoapi/pull/

OK, tell me how ?

This was intended for original op

@nullpixel1 This same PR? to the pogo dev branch? Id be honored.
Please tell me exactly what you need and ill be glad to help.

@pablossp Just PR your binaries 😀 We need people to compile them for us, as we don't have all the binaries

Ohh, I think you might be mistaken, we are actually missing them, @lukasz138 is saying he is having an error because he does not have them. If I get to compile any of them when Im back home Ill be glad to send any missing binaries to the project.

We've got a PR that's got them. I'll review and merge them later

👍

Let me merge and have it run on Linux :) Everybody can't wait to see the green log.

Is bot working after latest update with CP changes?


Bug: Rename error modal doesn't display in the correct app window

Description
When renaming a file from properties window the file extension rename dialog appears on the main Files window and the properties window overlaps it
Steps To Reproduce

Open properties window
Change the file extension from there
Click ok

Expected behavior
Display the dialog in the properties window. As a workaround since the user has already click ok, closing the property window before displaying the dialog would be enough
Files Version
<IP_ADDRESS>
Windows Version
Windows 11 21H2 22000.346
Relevant Assets

I don't think we should display this dialog at all if you're making the change from the properties window

I don't think we should display this dialog at all if you're making the change from the properties window

Why? In the end the same action is being performed even if from a different place

The properties window is already a more advanced tool and someone renaming the file through it is more likely to know what they're doing, the option to rename files from the regular layout is used by a much wider range of users who may not understand the concept of file extensions.


Fix minor grammar issues in dtrace.md

"windows" -> "Windows"
"feedback hub" -> "Feedback Hub"
"open source" -> "open-source" (to be consistent with other documentation)
inconsistent imperfect tense -> present tense
Added oxford comma and a few missing periods.

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@Cajunvoodoo - Thanks for these edits! Good to have Windows, with an upper-case W, for sure. I guess when open-source is used as a noun ("I like open source") it may not be hyphenated, but I think the hyphenated version, is what we want here. Thanks again!


Remove table layouts from Hive

This should wait for https://github.com/prestosql/presto/pull/731. The legacy PushPredicateIntoTableScan logic processes the predicates through DomainTranslator, which normalizes expressions in the same way UnwrapCastsInComparison in that PR does. However, the normalization is not done for the new applyFilter. Leaving casts "wrapped" causes some of the cost estimations to fail, so it affects the ability for the join ordering optimizer to do its job. #731 introduces the normalization for expressions anywhere in the plan, so it compensates for the "missing" behavior in handling applyFilter (arguably, it should have never been there in the first place)

@martint updated


whenTapped gets leaked

When using the whenTapped method my view is retained and doesn't get released properly even if i use [myview whenTapped:nil];
when i remove my gestures manually:
for (UIGestureRecognizer *tempGesture in [self gestureRecognizers] )
[self removeGestureRecognizer:tempGesture];
after i set whenTapped to nil and i release my view it gets released properly.
maybe this should be done blockskit ? or maybe the problem exists somewhere else?

Internally, the whenTapped: et. al. methods aren't setters; they just auto-initialize a UITapGestureRecognizer, which is noted in our documentation and will be clarified in the future. So calling [view whenTapped:nil] isn't useful.
UIGestureRecognizers shouldn't be retaining the view on release; that behavior isn't documented by Apple and it doesn't quite make sense. You should be autoreleasing (or in ARC, not doing anything at all) gesture recognizers as the view retains them. I've never seen it work any other way. All of this should be taken care of automatically unless you still have a reference to the gesture recognizer and/or view. I will investigate this further.
If you can't get it to work, my personal recommendation is to try setting up the gesture recognizers yourself using UIGestureRecognizer+BlocksKit. I'm not a huge fan of those methods myself, and I'll look into improving them.
Thank you for your support!

Part of this is already being fixed in the experimental branch, and the rest I'll fix myself by 1.0.


Colorization of collapsed code in interactive window doesn't match expanded

Collapsed code:

Expanded code:

Need to figure out how to get the collapsed code to make the same token requests.

@rchiodo does the final colorization contain semantic syntax highlighting?

Not sure what you mean by final. The expanded code has semantic tokens enabled. I can see the request being made when the expansion happens.

We use tokenizeToStringSync to tokenize the content, which won't take semantic syntax highlighting into account (which is async).

though mine didn't include the comment like Rich's did. Expected @rebornix?

@TylerLeonhardt that should be fine and irrelevant.


redis-consul isn't sending complete register message

For some reason, redis-consul is sending empty "Address" field inside the register request. Consul version is 0.7.1.
Here's an excerpt from tcpdump:
IP <IP_ADDRESS>.42980 > <IP_ADDRESS>.fmtp: tcp 311
E..k..@.@.|...........!4..\.M%.r...V._.....
.$...$..PUT /v1/catalog/register HTTP/1.1
Host: <IP_ADDRESS>:8500
User-Agent: Go-http-client/1.1
Content-Length: 176
Accept-Encoding: gzip

{"Node":"ip-10-5-4-97","Address":"","Datacenter":"live","Service":{"ID":"redishappy-consul-redis-live","Service":"redis-live","Tags":["redis","master"],"Port":0},"Check":null}

IP <IP_ADDRESS>.fmtp > <IP_ADDRESS>.42980: tcp 0
E..4*.@.@...........!4..M%.r..]J...^.(.....
.$...$..
IP <IP_ADDRESS>.fmtp > <IP_ADDRESS>.42980: tcp 176
E...*.@.@..K........!4..M%.r..]J...^.......
.$...$..HTTP/1.1 500 Internal Server Error
Date: Tue, 20 Dec 2016 10:55:22 GMT
Content-Length: 40
Content-Type: text/plain; charset=utf-8

rpc error: Must provide node and address

Config file:
{
  "SentinelTCPKeepAlive": 0,
  "Clusters": [
    {
      "name": "redis-live",
      "externalPort": 6380
    }
  ],
  "sentinels": [
    {
      "host": "<IP_ADDRESS>",
      "port": 26379
    }
  ],
  "consul": {
    "address": "<IP_ADDRESS>:8500",
    "services": [
      {
        "cluster": "redis-live",
        "node": "ip-10-5-4-97",
        "datacenter": "live",
        "tags": [
          "redis",
          "master"
        ]
      }
    ]
  }
}

Sentinels are discovered:
[root@ip-10-5-4-97 ~]# curl localhost:8000/api/sentinels 2>/dev/null | python -m json.tool
{
    "sentinels": {
        "<IP_ADDRESS>:26379": {
            "clusters": [
                "mymaster"
            ],
            "lastUpdated": "2016-12-20T11:07:03.835668822Z",
            "sentinelLocation": "<IP_ADDRESS>:26379",
            "state": 3
        },
        "<IP_ADDRESS>:26379": {
            "clusters": [
                "mymaster"
            ],
            "lastUpdated": "2016-12-20T11:07:03.223261374Z",
            "sentinelLocation": "<IP_ADDRESS>:26379",
            "state": 3
        },
        "<IP_ADDRESS>:26379": {
            "clusters": [
                "mymaster"
            ],
            "lastUpdated": "2016-12-20T11:07:03.835097067Z",
            "sentinelLocation": "<IP_ADDRESS>:26379",
            "state": 3
        }
    }
}

Just found the problem. Not a bug, but a misunderstanding of the documentation.
Cluster name should be the name of the redis cluster, as defined in redis/sentinel, and not how it should appear in consul (as I originally thought).
  "Clusters": [
    {
      "name": "redis-live",
      "externalPort": 6380
    }
  ],


[#1408]Add missing tag gifs

Fixes #1408

The create tags image was deleted by mistake?

@lucapette  The file for the previous create tag gif was too large. So I created another gif for this. So Create, Edit and Delete tags are sort of in uniform since they are created by the same user.
See Below
https://deploy-preview-1496--docs-airy.netlify.app/ui/tags


Initial folder structure commit

# git commit -m "Add travis.yaml file with basic instructions"
[WARNING] normalizing pre-commit configuration to a top-level map.  support for top level list will be removed in a future version.  run: `pre-commit migrate-config` to automatically fix this.
[WARNING] The 'rev' field of repo 'https://github.com/ibm/detect-secrets' appears to be a mutable reference (moving tag / branch).  Mutable references are never updated after first install and are not supported.  See https://pre-commit.com/#using-the-latest-version-for-a-repository for more details.
Detect secrets...........................................................Passed
[folders fac61cc] Add travis.yaml file with basic instructions
 1 file changed, 30 insertions(+)
 create mode 100644 .travis.yaml


Obsolete repository description

🐛 Bug
Obsolete repository description in "About" sidebox: "Identify the type of disease present on Appletree leafs", although this repository is now a compilation of various classification tasks.
To Reproduce
Not applicable here.
Expected behavior
The description should be updated to fit current repository content.
Additional context
None

Yeah, I was a bit out recently... would you be interested in sending a PR with suggested updates? :chipmunk:

How about "Various Kaggle image classification challenges solutions" ?

How about "Various Kaggle image classification challenges solutions" ?

updated, thank you for your suggestion


openresty - Permission denied connecting to sockexec

Hi, I'm trying it out for the first time in an openresty docker installation.  I'm not sure if I simply have a permissions setup problem, or am doing something else wrong in my config.
I successfully tested sockexec in my container by manually running sockexec, then running one of the perl example scripts in the container.  Now when trying to execute a command in a lua script in openresty, I'm getting this permission error:
connect() to unix:/tmp/exec.sock failed (13: Permission denied)

Here's how I'm starting sockexec and openresty.  This is from a sh script referenced in my Dockerfile ENTRYPOINT:
/opt/sockexec/bin/sockexec /tmp/exec.sock &
/usr/local/openresty/bin/openresty -p / -g "daemon off;" -c /nginx.conf

This is how I'm trying to test calling a command in the lua script:
     local exec = require'resty.exec'
     local prog = exec.new('/tmp/exec.sock')
     local res, err = prog("uname")

Here's the process information when running in the docker container - they're both initiated as root:
root        11  0.0  0.2  50956  6916 ?        S    15:44   0:00 nginx: master process /usr/local/openresty/bin/openresty -p / -g daemon off; -c /nginx.conf
root         8  0.0  0.0    252     4 ?        S    16:47   0:00 /opt/sockexec/bin/sockexec /tmp/exec.sock

Any tips on what I'm doing wrong?  Maybe just missing a setup step?  Thanks a ton for any help.

What user is the nginx worker process running as? Do you have anything in your nginx.conf that changes the user?

Ah - i'm not very experienced with nginx - that was it.  I mistakenly assumed that nginx would run as the user that started the process, but that's not the case.  I configured nginx to run as the intended user, and that fixed the issue.  Thanks again for your help!

No problem! Always happy to help :)

Should add to readme
sockexec /tmp/exec.sock
chown www-data:www-data /tmp/exec.sock


Bad build of grpc.tools.protoc in latest grpcio-tools wheel

grpcio_tools-0.15.0 pushed today contains a non-functional protoc. The following issues are reproducible using grpcio_tools-0.15.0-cp27-cp27mu-manylinux1_x86_64.whl
All generated Python contains the following snippet with an incomplete and syntactically invalid import statement, for example:
import
from grpc.beta import implementations as beta_implementations
from grpc.beta import interfaces as beta_interfaces

Attempting to import the generated code predictably fails:
>>> import test_pb2
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "test_pb2.py", line 186
    import
          ^
SyntaxError: invalid syntax

Additionally, the --proto_path flag is documented in --help with:

If not given, the current working directory will be used

But attempting to invoke grpc.tools.protoc without specifying an explicit --proto_path flag returns the following error:
test.proto: File does not reside within any path specified using --proto_path (or -I)

Duplicate of #7101.

Will the proto_path behavior be fixed by the fixed linked from #7101?

Is that behavior on the current release (... if so how'd you check that...?), or for a previous release?

Setting aside the broken codegen in #7101, there's a visible behavior change between 0.14.0 and 0.15.0 that prevents codegen unless you specify --proto_path which should be using the current working directory as a default.
~ % source grpc-0.14.0/bin/activate
~ % pip show 'grpcio-tools'
---
Metadata-Version: 2.0
Name: grpcio-tools
Version: 0.14.0
Summary: UNKNOWN
Home-page: UNKNOWN
Author: UNKNOWN
Author-email: UNKNOWN
Installer: pip
License: 3-clause BSD
Location: /home/bgilmore/grpc-0.14.0/lib/python2.7/site-packages
Requires: protobuf
Classifiers:
~ % python -m grpc.tools.protoc test.proto --python_out=. --grpc_python_out=.
[codegen succeeds]

~ % source grpc-0.15.0/bin/activate
~ % pip show 'grpcio-tools'
---
Metadata-Version: 2.0
Name: grpcio-tools
Version: 0.15.0
Summary: UNKNOWN
Home-page: UNKNOWN
Author: UNKNOWN
Author-email: UNKNOWN
Installer: pip
License: 3-clause BSD
Location: /home/bgilmore/grpc-0.15.0/lib/python2.7/site-packages
Requires: grpcio, protobuf
Classifiers:
~ % python -m grpc.tools.protoc test.proto --python_out=. --grpc_python_out=.
D0628 22:15:57.197173456   22016 ev_posix.c:101]             Using polling engine: poll
test.proto: File does not reside within any path specified using --proto_path (or -I).  You must specify a --proto_path which encompasses this file.
D0628 22:20:28.452959186   22086 ev_posix.c:101]             Using polling engine: poll
[codegen succeeds, but code is broken per #7101]

Ah, okay, I think the problem is that when we started packaging the well-known protos, the underlying protoc implementation was made aware of them in locations via user-space flags, which then (for whatever reason) disables automatic inclusion of the present working directory.
Gonna hijack this thread for just that issue.

@kpayson64 @nathanielmanistaatgoogle So, AFAICT this is a really, really, really quick fix (just tack on a -I. [or "-I{}".format(os.getcwd()), or whatever] in the invocation at the end of tools/distrib/python/grpcio_tools/grpc/tools/protoc.py).
I don't know if protoc has some sort of ordering deal with multiple include paths and conflicting file names along identical relative paths to the include paths. Also, from the protobuf tutorial:

In general you should set the --proto_path flag to the root of your project and use fully qualified names for all imports.

... How deep down the rabbit hole do we want to go with this? I can imagine that somewhere in protobuf there's a way to inject the WKTs as something other than a --proto_path flag that mucks with the default behavior (but that involves possibly complicating the compilation process and code maintenance). Or that there's a way to argparse those flags at the Python-level, munge them a bit, then pass them through to protoc.


Small (sparkline) bar charts, Chrome/Firefox incompatibilities

I'm trying to draw small sparkline bar charts within a table with Ruby on Rails, but I'm having trouble getting the chart to scale appropriately. On Firefox, nothing draws at all, though I can find the bars in the SVG with the debugger. On Chrome, the chart draws, but it allocates more space than it needs, and doesn't appear centered within the table cell.
The code, just using a sample graph, looks like below. Screenshots from Firefox and Chrome are attached.
<%= javascript_include_tag 'chartist.min.js' %>

<%= render "application/sidenav_dashboard", selected: "admin_dashboard" %>

<%= t("dashboards.nav.analytics") %>

Sample chart

Sorry. My oversight. I just realized I still had ct-golden-section specified for aspect ratio, even though I'm passing fixed dimensions. Removing that solved the problem.


feat: Add option to hide enums

Related issue: #192
Hi, there! I added option to hide enums.
But I'm not good at English, so I haven't been able to update the document. Sorry.
I would be happy to have someone help me with this task.
@keonik Could you review this PR?

Tests have failed, but this is also failing in PR #193.
I wonder how long this has been occurring...

Sorry, I closed this PR by mistake.
Reopen in #201!


Fix issue with updating when targeting Nougat or higher

This pull request mainly fixes an issue when targeting Nougat.
The authorities section in of the provider tag in plugin.xml was set to "com.vaenow.appupdate.android.provider.${applicationId}".  However, in DownloadHandler.java, the plugin was calling com.vaenow.appupdate.android.provider.
I changed plugin.xml.
I also updated the Javascript function to be called without success or error callback functions (to be compatible with ionic-native).

#42 #21

Awsome @LanceJenkinZA 🙂


xkeyboard-config: bump 2.35.1

xkeyboard-config has a new release.
I did notice you initially did the update, but reverted it. Ive built it myself, and nothing broke on my end anyway.
I might aswell missed your reason for why you reverted the build aswell, so please let me know in any case.

I reverted the build, since I always receive the error:
Couldn't find rules file (evdev)

From setxkbmap after updating. I don't know why this occurs, even when the manifests of the 2.34 and 2.35.1 release are identical. This is even after a rebuild of setxkbmap.

ah, so that was the issue for the revert. I dont use setxkbmap myself any more, but I tried it on my system aswell, and gave me the same output. Ill see if I cant find anything related to it.

Ive identified the issue. The sed seems to break setxkbmap. removing the sed now makes setxkbmap work on my end atleast.
This does add perl as a makedep, which is less fortunate though, but most people probably have perl installed anyway.
Does this fix the issue for you?
I peeked at alpine linux's apkbuild, aswell as tested their binary build, which worked. They didnt add the sed, and neither does arch afaik. So I just tested without it, and it seemed to work.


Unable to find suitable setter or adder for property XYZ

Hi :)
The avalonia controls's error behaviour is somehow very unexpectable in my case.
Whenever I add something to the MainWindow.xaml, there are some errors.
Errors like this:

Unable to find suitable setter or adder for property Command of type Avalonia.Controls:Avalonia.Controls.Button for argument System.Runtime:System.String, available setter parameter lists are:
Avalonia.UnsetValueType
Avalonia.Data.IBinding
System.Windows.Input.ICommand (line 88 position 21) Line 88, position 21. XXX\Views\MainWindow.xaml

The code:
<TabItem>           <TabItem.Header>             <TextBlock VerticalAlignment="Center">History</TextBlock>           </TabItem.Header>           <StackPanel>             <Button Command="RefreshHistory">Reload from database</Button>             <controls1:DataGrid AutoGenerateColumns="True">               <controls1:DataGrid.Items>                 <data:Binding Path="JobHistory" />               </controls1:DataGrid.Items>             </controls1:DataGrid>           </StackPanel>         </TabItem>
I work with:

Avalonia v0.9.10
Avalonia.Controls.DataGrid v0.9.10
Avalonia.Desktop v0.9.10
Avalonia.ReactiveUI v0.9.10

Maybe related to #3145

There is no conversion between string and ICommand, yes

That's true, but the "RefreshHistory" is a ReactiveCommand in the ViewModel.

Nevermind I know what was wrong ^^
Stupid mistake

What ended up being the solution?


Several errors for my own Lidar data

Dear,
I tried to test the Point-GNN on my own Lidar data, sometimes, there are errors, as is shown below.
Could anyone tell me the reason for those errors? I am really appreciative for any advices!
`(py37) objloc@deeplearning:~/OBJLOC/Point-GNN$ python3 run.py checkpoints/car_auto_T3_trainval/ --test --dataset_root_dir pointGNN_dataset_small --output_dir OUTPUT --level 2
/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
@ level 0 Graph, Add layer: layer1, type: scatter_max_point_set_pooling
WARNING:tensorflow:From /home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
Feature Dim:300
@ level 1 Graph, Add layer: layer2, type: scatter_max_graph_auto_center_net
Feature Dim:300
@ level 1 Graph, Add layer: layer3, type: scatter_max_graph_auto_center_net
Feature Dim:300
@ level 1 Graph, Add layer: layer4, type: scatter_max_graph_auto_center_net
Feature Dim:300
Final Feature Dim:300
Prediction 4 classes
2020-03-31 17:26:28.163067: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2020-03-31 17:26:28.185768: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2020-03-31 17:26:28.186787: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x55d32ffca7f0 executing computations on platform Host. Devices:
2020-03-31 17:26:28.186831: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): ,
2020-03-31 17:26:28.324051: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: TITAN X (Pascal) major: 6 minor: 1 memoryClockRate(GHz): 1.531
pciBusID: 0000:01:00.0
totalMemory: 11.91GiB freeMemory: 11.07GiB
2020-03-31 17:26:28.324083: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2020-03-31 17:26:28.324847: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-03-31 17:26:28.324860: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2020-03-31 17:26:28.324867: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2020-03-31 17:26:28.324936: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 10772 MB memory) -> physical GPU (device: 0, name: TITAN X (Pascal), pci bus id: 0000:01:00.0, compute capability: 6.1)
2020-03-31 17:26:28.326513: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x55d3306e2920 executing computations on platform CUDA. Devices:
2020-03-31 17:26:28.326531: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): TITAN X (Pascal), Compute Capability 6.1
Restore from checkpoint checkpoints/car_auto_T3_trainval/model-1400000
WARNING:tensorflow:From /home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
0%|                                                                                | 0/21 [00:00<?, ?it/s]2020-03-31 17:26:29.195655: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally
Configure the viewpoint as you want and press [q]
5%|████▌                                                                                | 1/21 [00:04<01:24,  4.22s/it]Configure the viewpoint as you want and press [q]
10%|█████████▏                                                                                | 2/21 [00:05<01:03,  3.37s/it]Configure the viewpoint as you want and press [q]
14%|█████████████▋                                                                                | 3/21 [00:06<00:49,  2.74s/it]Configure the viewpoint as you want and press [q]
19%|██████████████████▎                                                                             | 4/21 [00:08<00:38,  2.26s/it]Configure the viewpoint as you want and press [q]
24%|██████████████████████▊                                                                         | 5/21 [00:09<00:31,  1.94s/it]Configure the viewpoint as you want and press [q]
29%|███████████████████████████▍                                                                    | 6/21 [00:10<00:25,  1.73s/it]Configure the viewpoint as you want and press [q]
33%|████████████████████████████████                                                                | 7/21 [00:11<00:21,  1.57s/it]Configure the viewpoint as you want and press [q]
38%|████████████████████████████████████▌                                                           | 8/21 [00:12<00:18,  1.44s/it]Configure the viewpoint as you want and press [q]
43%|█████████████████████████████████████████▏                                                      | 9/21 [00:14<00:17,  1.49s/it]Configure the viewpoint as you want and press [q]
48%|█████████████████████████████████████████████▏                                                 | 10/21 [00:15<00:15,  1.42s/it]Configure the viewpoint as you want and press [q]
52%|█████████████████████████████████████████████████▊                                             | 11/21 [00:16<00:13,  1.34s/it]Configure the viewpoint as you want and press [q]
57%|██████████████████████████████████████████████████████▎                                        | 12/21 [00:29<00:43,  4.79s/it]Qt: Cannot set locale modifiers:
Configure the viewpoint as you want and press [q]
62%|██████████████████████████████████████████████████████████▊                                    | 13/21 [00:33<00:36,  4.54s/it]Configure the viewpoint as you want and press [q]
67%|███████████████████████████████████████████████████████████████▎                               | 14/21 [00:59<01:15, 10.82s/it]Configure the viewpoint as you want and press [q]
71%|███████████████████████████████████████████████████████████████████▊                           | 15/21 [01:08<01:02, 10.35s/it]2020-03-31 17:27:47.639016: W tensorflow/core/common_runtime/bfc_allocator.cc:267] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.30GiB.  Current allocation summary follows.
2020-03-31 17:27:47.639090: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (256): 	Total Chunks: 93, Chunks in use: 93. 23.2KiB allocated for chunks. 23.2KiB in use in bin. 7.3KiB client-requested in use in bin.
2020-03-31 17:27:47.639113: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (512): 	Total Chunks: 6, Chunks in use: 6. 3.8KiB allocated for chunks. 3.8KiB in use in bin. 3.8KiB client-requested in use in bin.
2020-03-31 17:27:47.639134: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (1024): 	Total Chunks: 36, Chunks in use: 36. 46.8KiB allocated for chunks. 46.8KiB in use in bin. 44.2KiB client-requested in use in bin.
2020-03-31 17:27:47.639153: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (2048): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-03-31 17:27:47.639170: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (4096): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-03-31 17:27:47.639188: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (8192): 	Total Chunks: 1, Chunks in use: 1. 8.0KiB allocated for chunks. 8.0KiB in use in bin. 8.0KiB client-requested in use in bin.
2020-03-31 17:27:47.639208: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (16384): 	Total Chunks: 4, Chunks in use: 4. 64.0KiB allocated for chunks. 64.0KiB in use in bin. 64.0KiB client-requested in use in bin.
2020-03-31 17:27:47.639228: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (32768): 	Total Chunks: 1, Chunks in use: 1. 32.0KiB allocated for chunks. 32.0KiB in use in bin. 32.0KiB client-requested in use in bin.
2020-03-31 17:27:47.639249: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (65536): 	Total Chunks: 9, Chunks in use: 9. 721.5KiB allocated for chunks. 721.5KiB in use in bin. 721.4KiB client-requested in use in bin.
2020-03-31 17:27:47.639269: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (131072): 	Total Chunks: 1, Chunks in use: 1. 150.0KiB allocated for chunks. 150.0KiB in use in bin. 150.0KiB client-requested in use in bin.
2020-03-31 17:27:47.639289: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (262144): 	Total Chunks: 15, Chunks in use: 13. 5.43MiB allocated for chunks. 4.58MiB in use in bin. 4.47MiB client-requested in use in bin.
2020-03-31 17:27:47.639309: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (524288): 	Total Chunks: 1, Chunks in use: 1. 555.0KiB allocated for chunks. 555.0KiB in use in bin. 355.1KiB client-requested in use in bin.
2020-03-31 17:27:47.639327: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (1048576): 	Total Chunks: 1, Chunks in use: 0. 1.36MiB allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-03-31 17:27:47.639345: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (2097152): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-03-31 17:27:47.639361: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (4194304): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-03-31 17:27:47.639381: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (8388608): 	Total Chunks: 4, Chunks in use: 3. 42.66MiB allocated for chunks. 34.49MiB in use in bin. 27.42MiB client-requested in use in bin.
2020-03-31 17:27:47.639402: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (16777216): 	Total Chunks: 3, Chunks in use: 2. 75.98MiB allocated for chunks. 47.18MiB in use in bin. 46.70MiB client-requested in use in bin.
2020-03-31 17:27:47.639419: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (33554432): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-03-31 17:27:47.639436: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (67108864): 	Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-03-31 17:27:47.639453: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (134217728): 	Total Chunks: 1, Chunks in use: 0. 128.00MiB allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
2020-03-31 17:27:47.639472: I tensorflow/core/common_runtime/bfc_allocator.cc:597] Bin (268435456): 	Total Chunks: 6, Chunks in use: 1. 8.25GiB allocated for chunks. 2.28GiB in use in bin. 2.28GiB client-requested in use in bin.
2020-03-31 17:27:47.639491: I tensorflow/core/common_runtime/bfc_allocator.cc:613] Bin for 2.30GiB was 256.00MiB, Chunk State:
2020-03-31 17:27:47.639511: I tensorflow/core/common_runtime/bfc_allocator.cc:619]   Size: 256.00MiB | Requested Size: 174.22MiB | in_use: 0
2020-03-31 17:27:47.639530: I tensorflow/core/common_runtime/bfc_allocator.cc:619]   Size: 1.00GiB | Requested Size: 408.34MiB | in_use: 0
2020-03-31 17:27:47.639547: I tensorflow/core/common_runtime/bfc_allocator.cc:619]   Size: 1.00GiB | Requested Size: 827.68MiB | in_use: 0
2020-03-31 17:27:47.639566: I tensorflow/core/common_runtime/bfc_allocator.cc:619]   Size: 1.72GiB | Requested Size: 1.36MiB | in_use: 0, prev:   Size: 2.28GiB | Requested Size: 2.28GiB | in_use: 1
2020-03-31 17:27:47.639583: I tensorflow/core/common_runtime/bfc_allocator.cc:619]   Size: 2.00GiB | Requested Size: 1.01GiB | in_use: 0
2020-03-31 17:27:47.639601: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faada000000 of size<PHONE_NUMBER>
2020-03-31 17:27:47.639615: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Free  at 0x7fab6bed8c00 of size<PHONE_NUMBER>
2020-03-31 17:27:47.639628: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Free  at 0x7fabe0000000 of size<PHONE_NUMBER>
2020-03-31 17:27:47.639641: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Free  at 0x7facc8000000 of size<PHONE_NUMBER>
2020-03-31 17:27:47.639654: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Free  at 0x7fad08000000 of size<PHONE_NUMBER>
2020-03-31 17:27:47.639667: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Free  at 0x7fad48000000 of size 268435456
2020-03-31 17:27:47.639680: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Free  at 0x7fad58000000 of size 134217728
2020-03-31 17:27:47.639693: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fad60000000 of size 12432128
2020-03-31 17:27:47.639708: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fad60bdb300 of size 24482816
2020-03-31 17:27:47.639721: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Free  at 0x7fad62334700 of size 30193920
2020-03-31 17:27:47.639734: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Free  at 0x7fadd8000000 of size 1427456
2020-03-31 17:27:47.639748: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadd815c800 of size 15349760
2020-03-31 17:27:47.639761: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Free  at 0x7fadd9000000 of size 8564224
2020-03-31 17:27:47.639774: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadd982ae00 of size 24990208
2020-03-31 17:27:47.639788: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800000 of size 256
2020-03-31 17:27:47.639801: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800100 of size 256
2020-03-31 17:27:47.639814: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800200 of size 256
2020-03-31 17:27:47.639827: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800300 of size 256
2020-03-31 17:27:47.639839: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800400 of size 256
2020-03-31 17:27:47.639852: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800500 of size 256
2020-03-31 17:27:47.639865: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800600 of size 256
2020-03-31 17:27:47.639878: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800700 of size 256
2020-03-31 17:27:47.639891: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800800 of size 256
2020-03-31 17:27:47.639903: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800900 of size 256
2020-03-31 17:27:47.639916: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800a00 of size 256
2020-03-31 17:27:47.639929: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800b00 of size 256
2020-03-31 17:27:47.639941: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800c00 of size 256
2020-03-31 17:27:47.639954: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800d00 of size 256
2020-03-31 17:27:47.639967: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800e00 of size 256
2020-03-31 17:27:47.639980: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde800f00 of size 256
2020-03-31 17:27:47.639992: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801000 of size 256
2020-03-31 17:27:47.640005: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801100 of size 256
2020-03-31 17:27:47.640018: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801200 of size 256
2020-03-31 17:27:47.640031: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801300 of size 256
2020-03-31 17:27:47.640044: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801400 of size 256
2020-03-31 17:27:47.640056: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801500 of size 256
2020-03-31 17:27:47.640069: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801600 of size 256
2020-03-31 17:27:47.640082: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801700 of size 256
2020-03-31 17:27:47.640094: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801800 of size 256
2020-03-31 17:27:47.640107: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801900 of size 256
2020-03-31 17:27:47.640120: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801a00 of size 256
2020-03-31 17:27:47.640132: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801b00 of size 256
2020-03-31 17:27:47.640145: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801c00 of size 256
2020-03-31 17:27:47.640158: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801d00 of size 256
2020-03-31 17:27:47.640170: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801e00 of size 256
2020-03-31 17:27:47.640183: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde801f00 of size 256
2020-03-31 17:27:47.640196: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde802000 of size 256
2020-03-31 17:27:47.640209: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde802100 of size 256
2020-03-31 17:27:47.640222: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde802200 of size 256
2020-03-31 17:27:47.640235: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde802300 of size 256
2020-03-31 17:27:47.640248: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde802400 of size 256
2020-03-31 17:27:47.640260: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde802500 of size 256
2020-03-31 17:27:47.640273: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde802600 of size 256
2020-03-31 17:27:47.640287: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde802700 of size 1280
2020-03-31 17:27:47.640300: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde802c00 of size 256
2020-03-31 17:27:47.640312: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde802d00 of size 256
2020-03-31 17:27:47.640325: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde802e00 of size 1280
2020-03-31 17:27:47.640338: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde803300 of size 256
2020-03-31 17:27:47.640351: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde803400 of size 1280
2020-03-31 17:27:47.640364: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde803900 of size 256
2020-03-31 17:27:47.640377: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde803a00 of size 1280
2020-03-31 17:27:47.640390: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde803f00 of size 256
2020-03-31 17:27:47.640403: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde804000 of size 256
2020-03-31 17:27:47.640416: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde804100 of size 256
2020-03-31 17:27:47.640429: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde804200 of size 256
2020-03-31 17:27:47.640442: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde804300 of size 256
2020-03-31 17:27:47.640455: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde804400 of size 1280
2020-03-31 17:27:47.640467: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde804900 of size 256
2020-03-31 17:27:47.640480: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde804a00 of size 1280
2020-03-31 17:27:47.640493: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde804f00 of size 256
2020-03-31 17:27:47.640506: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde805000 of size 1280
2020-03-31 17:27:47.640519: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde805500 of size 256
2020-03-31 17:27:47.640532: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde805600 of size 1280
2020-03-31 17:27:47.640545: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde805b00 of size 256
2020-03-31 17:27:47.640558: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde805c00 of size 256
2020-03-31 17:27:47.640570: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde805d00 of size 256
2020-03-31 17:27:47.640584: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde805e00 of size 256
2020-03-31 17:27:47.640597: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde805f00 of size 256
2020-03-31 17:27:47.640610: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde806000 of size 1280
2020-03-31 17:27:47.640622: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde806500 of size 256
2020-03-31 17:27:47.640635: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde806600 of size 1280
2020-03-31 17:27:47.640649: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde806b00 of size 256
2020-03-31 17:27:47.640665: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde806c00 of size 1280
2020-03-31 17:27:47.640676: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde807100 of size 256
2020-03-31 17:27:47.640687: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde807200 of size 1280
2020-03-31 17:27:47.640700: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde807700 of size 256
2020-03-31 17:27:47.640724: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde807800 of size 256
2020-03-31 17:27:47.640737: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde807900 of size 1280
2020-03-31 17:27:47.640749: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde807e00 of size 256
2020-03-31 17:27:47.640762: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde807f00 of size 256
2020-03-31 17:27:47.640775: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde808000 of size 512
2020-03-31 17:27:47.640788: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde808200 of size 256
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2020-03-31 17:27:47.640877: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde808900 of size 1280
2020-03-31 17:27:47.640890: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde808e00 of size 256
2020-03-31 17:27:47.640903: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde808f00 of size 1280
2020-03-31 17:27:47.640915: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde809400 of size 1280
2020-03-31 17:27:47.640929: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde809900 of size 1792
2020-03-31 17:27:47.640941: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde80a000 of size 256
2020-03-31 17:27:47.640955: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde80a100 of size 16384
2020-03-31 17:27:47.640968: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde80e100 of size 256
2020-03-31 17:27:47.640981: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde80e200 of size 76800
2020-03-31 17:27:47.640994: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde820e00 of size 256
2020-03-31 17:27:47.641006: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde820f00 of size 1792
2020-03-31 17:27:47.641019: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde821600 of size 256
2020-03-31 17:27:47.641032: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde821700 of size 16384
2020-03-31 17:27:47.641045: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde825700 of size 256
2020-03-31 17:27:47.641057: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde825800 of size 76800
2020-03-31 17:27:47.641071: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde838400 of size 256
2020-03-31 17:27:47.641084: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde838500 of size 1792
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2020-03-31 17:27:47.641110: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde838d00 of size 16384
2020-03-31 17:27:47.641123: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde83cd00 of size 256
2020-03-31 17:27:47.641136: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde83ce00 of size 76800
2020-03-31 17:27:47.641148: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde84fa00 of size 256
2020-03-31 17:27:47.641161: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde84fb00 of size 1792
2020-03-31 17:27:47.641174: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde850200 of size 256
2020-03-31 17:27:47.641186: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde850300 of size 16384
2020-03-31 17:27:47.641199: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde854300 of size 256
2020-03-31 17:27:47.641211: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde854400 of size 76800
2020-03-31 17:27:47.641224: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde867000 of size 256
2020-03-31 17:27:47.641237: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde867100 of size 1024
2020-03-31 17:27:47.641250: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde867500 of size 256
2020-03-31 17:27:47.641263: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde867600 of size 76800
2020-03-31 17:27:47.641275: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde87a200 of size 256
2020-03-31 17:27:47.641289: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde87a300 of size 768
2020-03-31 17:27:47.641301: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde87a600 of size 256
2020-03-31 17:27:47.641314: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde87a700 of size 76800
2020-03-31 17:27:47.641327: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde88d300 of size 256
2020-03-31 17:27:47.641340: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadde88d400 of size 470016
2020-03-31 17:27:47.641353: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddec00000 of size 1280
2020-03-31 17:27:47.641366: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddec00500 of size 363776
2020-03-31 17:27:47.641379: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddec59200 of size 1280
2020-03-31 17:27:47.641392: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddec59700 of size 360192
2020-03-31 17:27:47.641405: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddecb1600 of size 1280
2020-03-31 17:27:47.641418: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddecb1b00 of size 360192
2020-03-31 17:27:47.641431: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadded09a00 of size 1280
2020-03-31 17:27:47.641444: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadded09f00 of size 768
2020-03-31 17:27:47.641456: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadded0a200 of size 256
2020-03-31 17:27:47.641469: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadded0a300 of size 76800
2020-03-31 17:27:47.641482: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadded1cf00 of size 256
2020-03-31 17:27:47.641495: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadded1d000 of size 360192
2020-03-31 17:27:47.641507: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadded74f00 of size 1280
2020-03-31 17:27:47.641520: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7fadded75400 of size 568320
2020-03-31 17:27:47.641534: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddee00000 of size 1280
2020-03-31 17:27:47.641546: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddee00500 of size 360192
2020-03-31 17:27:47.641559: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddee58400 of size 1280
2020-03-31 17:27:47.641572: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddee58900 of size 360192
2020-03-31 17:27:47.641584: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddeeb0800 of size 1280
2020-03-31 17:27:47.641597: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddeeb0d00 of size 768
2020-03-31 17:27:47.641610: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddeeb1000 of size 256
2020-03-31 17:27:47.641622: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddeeb1100 of size 76800
2020-03-31 17:27:47.641662: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddeec3d00 of size 256
2020-03-31 17:27:47.641678: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddeec3e00 of size 360192
2020-03-31 17:27:47.641690: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddef1bd00 of size 1280
2020-03-31 17:27:47.641703: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddef1c200 of size 363776
2020-03-31 17:27:47.641716: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddef74f00 of size 1280
2020-03-31 17:27:47.641728: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddef75400 of size 360192
2020-03-31 17:27:47.641741: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddefcd300 of size 1280
2020-03-31 17:27:47.641754: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddefcd800 of size 360192
2020-03-31 17:27:47.641766: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddf025700 of size 1280
2020-03-31 17:27:47.641779: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddf025c00 of size 153600
2020-03-31 17:27:47.641792: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddf04b400 of size 1280
2020-03-31 17:27:47.641805: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddf04b900 of size 32768
2020-03-31 17:27:47.641818: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddf053900 of size 512
2020-03-31 17:27:47.641831: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddf053b00 of size 8192
2020-03-31 17:27:47.641843: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddf055b00 of size 256
2020-03-31 17:27:47.641856: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddf055c00 of size 512
2020-03-31 17:27:47.641869: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddf055e00 of size 256
2020-03-31 17:27:47.641882: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddf055f00 of size 360192
2020-03-31 17:27:47.641894: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddf0ade00 of size 1280
2020-03-31 17:27:47.641907: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddf0ae300 of size 360192
2020-03-31 17:27:47.641920: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddf106200 of size 1280
2020-03-31 17:27:47.641932: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Free  at 0x7faddf106700 of size 429568
2020-03-31 17:27:47.641946: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddf16f500 of size 124416
2020-03-31 17:27:47.641958: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Free  at 0x7faddf18db00 of size 468224
2020-03-31 17:27:47.641972: I tensorflow/core/common_runtime/bfc_allocator.cc:632] Chunk at 0x7faddf800000 of size 8388608
2020-03-31 17:27:47.641984: I tensorflow/core/common_runtime/bfc_allocator.cc:638]      Summary of in-use Chunks by size:
2020-03-31 17:27:47.642001: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 93 Chunks of size 256 totalling 23.2KiB
2020-03-31 17:27:47.642017: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 3 Chunks of size 512 totalling 1.5KiB
2020-03-31 17:27:47.642032: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 3 Chunks of size 768 totalling 2.2KiB
2020-03-31 17:27:47.642047: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 1024 totalling 1.0KiB
2020-03-31 17:27:47.642063: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 31 Chunks of size 1280 totalling 38.8KiB
2020-03-31 17:27:47.642078: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 4 Chunks of size 1792 totalling 7.0KiB
2020-03-31 17:27:47.642093: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 8192 totalling 8.0KiB
2020-03-31 17:27:47.642109: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 4 Chunks of size 16384 totalling 64.0KiB
2020-03-31 17:27:47.642124: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 32768 totalling 32.0KiB
2020-03-31 17:27:47.642140: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 8 Chunks of size 76800 totalling 600.0KiB
2020-03-31 17:27:47.642156: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 124416 totalling 121.5KiB
2020-03-31 17:27:47.642172: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 153600 totalling 150.0KiB
2020-03-31 17:27:47.642187: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 10 Chunks of size 360192 totalling 3.43MiB
2020-03-31 17:27:47.642203: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 2 Chunks of size 363776 totalling 710.5KiB
2020-03-31 17:27:47.642219: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 470016 totalling 459.0KiB
2020-03-31 17:27:47.642234: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 568320 totalling 555.0KiB
2020-03-31 17:27:47.642249: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 8388608 totalling 8.00MiB
2020-03-31 17:27:47.642266: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 12432128 totalling 11.86MiB
2020-03-31 17:27:47.642282: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 15349760 totalling 14.64MiB
2020-03-31 17:27:47.642297: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 24482816 totalling 23.35MiB
2020-03-31 17:27:47.642313: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size 24990208 totalling 23.83MiB
2020-03-31 17:27:47.642328: I tensorflow/core/common_runtime/bfc_allocator.cc:641] 1 Chunks of size<PHONE_NUMBER> totalling 2.28GiB
2020-03-31 17:27:47.642343: I tensorflow/core/common_runtime/bfc_allocator.cc:645] Sum Total of in-use chunks: 2.37GiB
2020-03-31 17:27:47.642361: I tensorflow/core/common_runtime/bfc_allocator.cc:647] Stats:
Limit:<PHONE_NUMBER>1
InUse:<PHONE_NUMBER>
MaxInUse:<PHONE_NUMBER>
NumAllocs:                    1316
MaxAllocSize:<PHONE_NUMBER>
2020-03-31 17:27:47.642408: W tensorflow/core/common_runtime/bfc_allocator.cc:271] *************************_______________________________________________________________________
2020-03-31 17:27:47.642457: W tensorflow/core/framework/op_kernel.cc:1401] OP_REQUIRES failed at concat_op.cc:153 : Resource exhausted: OOM when allocating tensor with shape[2040220,303] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
Traceback (most recent call last):
File "/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1334, in _do_call
return fn(*args)
File "/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1319, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1407, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[2040220,303] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node layer2/concat}}]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
 [[{{node ArgMax}}]]

Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "run.py", line 260, in
results = sess.run(fetches, feed_dict=feed_dict)
File "/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 929, in run
run_metadata_ptr)
File "/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1152, in _run
feed_dict_tensor, options, run_metadata)
File "/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1328, in _do_run
run_metadata)
File "/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/client/session.py", line 1348, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[2040220,303] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node layer2/concat (defined at /home/objloc/OBJLOC/Point-GNN/models/gnn.py:352) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
 [[node ArgMax (defined at run.py:142) ]]

Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
Caused by op 'layer2/concat', defined at:
File "run.py", line 140, in
t_is_training)
File "/home/objloc/OBJLOC/Point-GNN/models/models.py", line 146, in predict
**layer_kwargs)
File "/home/objloc/OBJLOC/Point-GNN/models/gnn.py", line 352, in apply_regular
axis=-1)
File "/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py", line 180, in wrapper
return target(*args, **kwargs)
File "/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/ops/array_ops.py", line 1256, in concat
return gen_array_ops.concat_v2(values=values, axis=axis, name=name)
File "/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/ops/gen_array_ops.py", line 1149, in concat_v2
"ConcatV2", values=values, axis=axis, name=name)
File "/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3300, in create_op
op_def=op_def)
File "/home/objloc/anaconda3/envs/py37/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1801, in init
self._traceback = tf_stack.extract_stack()
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[2040220,303] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node layer2/concat (defined at /home/objloc/OBJLOC/Point-GNN/models/gnn.py:352) ]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
 [[node ArgMax (defined at run.py:142) ]]

Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
71%|███████████████████████████████████████████████████████████████████▊                           | 15/21 [01:18<00:31,  5.25s/it]
(py37) objloc@deeplearning:~/OBJLOC/Point-GNN$
`

Hi, I think you run out of GPU memory during the test. You can try to increase GPU memory, downsampling, cropping the point cloud, etc. If you do not mind little accuracy loss, you can also modify the config file and replace the content in "runtime_graph_gen_kwargs" with the same as in "graph_gen_kwargs". This usually boosts the speed and reduces the memory requirement a lot with little accuracy loss.

Hi, thank you for your reply, and it works well!

@Juuustin,
Hi, have you got promising results?
I am also trying to do the test on my own data.  But the problem is that I don't have images in accordance with lidar data, so the obtained files are all empty when using the KITTI images since they don't match with my lidar data.  How do you deal with this issue? Do you have your own images? Really appreciate if you can provide some advice. Thanks.


enhance/unify list access methods

As really using the library to read SML data and use the contained
details I found missing access methods and also I found that
„PeriodList“ used different wording. In my eyes its more convenient and
easy to use if the usage methods for the various list methods are equal.

is it possible that the 0.3.0 is without these changes? Thank you for an 0.3.1 :-)

I am so sorry. Now it is published as version 0.3.1

Thank you!


Feat:Switch Login and Newsletter Email Setup from EmailJS to Nodemailer

Describe the feature
We currently use EmailJS for handling email functionalities in our project, including user login and newsletter subscriptions. While EmailJS has been a convenient solution, its free plan limits the number of emails we can send each month. To enable unlimited email transfers and gain more control over the email-sending process, we should switch to Nodemailer.
Add ScreenShots
And Checked it works with multiple mail services as well

Record

[X] I agree to follow this project's Code of Conduct
[X] I'm a GSSOC'24 contributor
[ ] I'm a VSoC'24 contributor
[X] I want to work on this issue

@nishant0708 you are assigned.

Hope you read the CONTRIBUTING Guidelines.

Please try to add comments about where and what type of changes you made.

Please don't add unnecessary code if there is no need! Thanks :)

And don't forget to ⭐ our repo

@nishant0708 The newsletter feature is already done using nodemailer bro. Please dont make false claims


Butler-based notebooks for CMU DC2 tutorial session

@jchiang87 Could you rebase to master?
This rebase should also remove the merge at 0388ba0.
* ad6f81c        (origin/u/jchiang/cmu_tutorials) add markdown descriptions
* b5337f7        make comments fit in github source code text panel.
* 4cc63a0        add discussion to skymap plotting notebook
* 26bfca1        add pedagogical descriptions and docstrings
* 5807593        Render html with htmlpreview
* 6c45565        Fixed formatting, link to html
* 8ba9125        Started table, added links
* 74c5bca        Working script #188
*   0388ba0      Removed outputs
|\
| * 6a24c08      rename skymap plotting example; switch to desc-stack kernel; use butler.get('camera') instead of calling LsstSimWrapper().camera
* | 0b2192e      Renamed as suggested by Jim
* | 99e30b4      Switched kernel to desc-stack
|/
* 4897847        make sure color option is passed to patch plotting code
* ce3d48a        add code to match a visit-level src catalog
* f4122e3        add notebooks for accessing DRP outputs with the butler

I've worked through all three of these notebooks, adding to the markdown cells in a few places. I chopped out the FieldRotator class into a little utility module in a new folder called Matching (since it is only used in the matching notebook). I'll paste in Fig 22 from Mandelbaum et al (2018) to the HSC selection notebook, too (once I've generated a URL by pasting it in to this comment). Nice job, @jchiang87 !

I'm going to take the liberty of rebasing onto master.


Add CI scripts

Do Helm lint validation and Helm unittest for the charts
It uses the same test tools as the rancher chart, but a newer version of unittest plugin to address some issues of it.
It does linting for all charts, and run unit tests when a tests directory is found in a chart directory.

LGTM.

LGTM


telic tracker history query

Hi,
Telic trackers sends different event ID's which describes reasons why that GPS position is sent to server, i already see them under "State" section but that is only last message event ID. Example from Telic event ID's is below.
Would it be possible to do so that when i query tracker history (route messages) then i see sent event ID code numbers behind every message (to add new column to history view table) ? It gives lot of information to user about what is going on with tracker.

You can see "state" for each of the item in history.


Behavior for links with target "_self" has changed in v5

Hi!
I'm not sure if this is a bug or not, but since v4.4.0-beta.4 using target="_self" in a link component doesn't let the browser handle the route.
The  target="_self" approach was dicussed in https://github.com/ReactTraining/react-router/issues/3109, and we have been relying on this for a very long time.
The change was introduced in https://github.com/ReactTraining/react-router/pull/6138 but I don't understand why, so maybe @ericyang89 can provide more information about it.
We faced this issue when upgrading to v5. I know we can just use anchor tags, but I'd like to know if this change was intended. If so, I think it would be nice to add the new behavior to a breaking changes section.
Thanks!

If you want the browser to handle it, just use an <a> tag instead.

Thanks @timdorr


bedtools shift returns incorrectly shifted bases

% cat b.bed
chr1    66999638    67216822    NM_032291   0   +
chr1    92145899    92351836    NR_036634   0   -
% cat huge.genome
chr1    249250621
% bedtools shift -i b.bed -s 1000 -g huge.genome
chr1    67000640    67217824    NM_032291   0   +
chr1    92146896    92352840    NR_036634   0   -
All of shifted start and end positions are incorrect in this example.
This symptom occur shifting a large genome bed file.

On v2.26.0 I am still encountering this issue on the same sized .genome file. Can you reproduce?
$ cat test.bed

chr1	249250600	249250601	test	0	+
chr1	249250603	249250604	test	0	-

$ head -n2 hg19.genome

chrom	size
chr1	249250621

$ bedtools shift -i test.bed -s 1 -g human_g1k_v37.genome

chr1	249250592	249250608	test	0	+
chr1	249250608	249250608	test	0	-

Yes I can reproduce.
Because fix for issue #415 were merged as commit 7384628, but it's not released yet.
I think v2.26.0 is older than commit 7384628 isn't it?

You're totally right, this PR missed v2.26.0 release by 2 days. Thanks anyway!

I confirmed that the fix was released.


typo: teh -> the

GitHub web editor decided the line endings where inconsistent and converted to CRLF. Might be good to add a .gitattributes files

:white_check_mark: Validation status: passed

File
Status
Preview URL
Details

windows-iotcore/manage-your-device/DevicePortal.md
:white_check_mark:Succeeded

For more details, please refer to the build report.
Note: If you changed an existing file name or deleted a file, broken links in other files to the deleted or renamed file are listed only in the full build report.

Great, thanks @nschonni!


Undesired error message about runing tikv-ctl without any parameters

Bug Report

What version of TiKV are you using?

master branch
What operating system and CPU are you using?

processor	: 23
vendor_id	: GenuineIntel
cpu family	: 6
model		: 22
model name	: Ksyun Virtual Senior CPU(ksyun-cpu64-senior)
stepping	: 3
microcode	: 0x1
cpu MHz		: 2599.998
cache size	: 19712 KB
physical id	: 23
siblings	: 1
core id		: 0
cpu cores	: 1
apicid		: 23
initial apicid	: 23
fpu		: yes
fpu_exception	: yes
cpuid level	: 13
wp		: yes
flags		: fpu vme de pse tsc msr pae mce cx8 apic mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss syscall nx pdpe1gb lm constant_tsc rep_good nopl xtopology eagerfpu pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 x2apic popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm fsgsbase bmi1 avx2 smep bmi2 erms xsaveopt
bugs		:
bogomips	: 5199.99
clflush size	: 64
cache_alignment	: 64
address sizes	: 46 bits physical, 48 bits virtual
power management:

Steps to reproduce

What did you expect?
➜  debug git:(issue_10734) ✗ ./tikv-ctl
TiKV Control (tikv-ctl)
Release Version:   5.1.0-alpha
Edition:           Community
Git Commit Hash:   Unknown (env var does not exist when building)
Git Commit Branch: Unknown (env var does not exist when building)
UTC Build Time:    2021-09-16 09:03:55
Rust Version:      Unknown (env var does not exist when building)
Enable Features:   Unknown (env var does not exist when building)
Profile:           Unknown (env var does not exist when building)

A tool for interacting with TiKV deployments.

USAGE:
    TiKV Control (tikv-ctl) [FLAGS] [OPTIONS] [SUBCOMMAND]

FLAGS:
    -h, --help                    Prints help information
        --skip-paranoid-checks    Skip paranoid checks when open rocksdb
    -V, --version                 Prints version information

OPTIONS:
        --ca-path <ca_path>              Set the CA certificate path
        --cert-path <cert_path>          Set the certificate path
        --config <config>                TiKV config path, by default it's <deploy-dir>/conf/tikv.toml
        --data-dir <data_dir>            TiKV data-dir, check <deploy-dir>/scripts/run.sh to get it
        --db <db>                        Set the rocksdb path
        --decode <decode>                Decode a key in escaped format
        --encode <encode>                Encode a key in escaped format
        --to-hex <escaped-to-hex>        Convert an escaped key to hex key
        --to-escaped <hex-to-escaped>    Convert a hex key to escaped key
        --host <host>                    Set the remote host
        --key-path <key_path>            Set the private key path
        --log-level <log_level>          Set the log level [default: info]
        --pd <pd>                        Set the address of pd
        --raftdb <raftdb>                Set the raft rocksdb path

SUBCOMMANDS:
    bad-regions           Get all regions with corrupt raft
    bad-ssts              Print bad ssts related infos
    cluster               Print the cluster id
    compact               Compact a column family in a specified range
    compact-cluster       Compact the whole cluster in a specified range in one or more column families
    consistency-check     Force a consistency-check for a specified region
    decrypt-file          Decrypt an encrypted file
    diff                  Calculate difference of region keys from different dbs
    dump-snap-meta        Dump snapshot meta file
    encryption-meta       Dump encryption metadata
    fail                  Inject failures to TiKV and recovery
    help                  Prints this message or the help of the given subcommand(s)
    metrics               Print the metrics
    modify-tikv-config    Modify tikv config, eg. tikv-ctl --host ip:port modify-tikv-config -n
                          rocksdb.defaultcf.disable-auto-compactions -v true
    mvcc                  Print the mvcc value
    print                 Print the raw value
    raft                  Print a raft log entry
    range-properties      Show range properties
    raw-scan              Print all raw keys in the range
    recover-mvcc          Recover mvcc data on one node by deleting corrupted keys
    recreate-region       Recreate a region with given metadata, but alloc new id for it
    region-properties     Show region properties
    scan                  Print the range db range
    size                  Print region size
    split-region          Split the region
    store                 Print the store id
    tombstone             Set some regions on the node to tombstone by manual
    unsafe-recover        Unsafely recover when the store can not start normally, this recover may lose data%

What did happened?
➜  debug git:(issue_10734) ✗ ./tikv-ctl
thread 'main' panicked at 'called `Option::unwrap()` on a `None` value', cmd/tikv-ctl/src/main.rs:104:29
note: run with `RUST_BACKTRACE=1` environment variable to display a backtrace
➜  debug git:(issue_10734) ✗

/assign

What TiKV version are you using? I found no issue in current master (f9838276):
$ target/debug/tikv-ctl
TiKV Control (tikv-ctl)
Release Version:   5.3.0-alpha
Edition:           Community
Git Commit Hash:   Unknown (env var does not exist when building)
Git Commit Branch: Unknown (env var does not exist when building)
UTC Build Time:    2021-09-24 04:00:05
Rust Version:      Unknown (env var does not exist when building)
Enable Features:   Unknown (env var does not exist when building)
Profile:           Unknown (env var does not exist when building)

A tool for interacting with TiKV deployments.

USAGE:
    TiKV Control (tikv-ctl) [FLAGS] [OPTIONS] [SUBCOMMAND]

<...rest discard>

This problem have been fixed. So i close it