text:
Improve markdown rendering

In my case the markdown is not rendered, there is something I have to configure for that?

Hi @Mte90 ,
The file type of the buffer is set to markdown. Do you have the corresponding Tree-Sitter parser? What happens if you open a markdown file, is it rendered correctly?

It seems that works


text:
markdownlint-disable-next-line should apply to the next non-empty line

Ideally this should work.
<!-- markdownlint-disable-next-line MD013 -->

Some really looooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooong line

Additional Context
Prettier will always insert an empty line after comments in markdown.

If you ask me, Prettier shouldn't break stuff like this just for fun. The meaning of "next line" is pretty clear in my mind and if we start trying to redefine it to mean "next line with content but hey is white space content well I don't know just take a guess", I think that could get confusing. :)
The example you show is possible today with the following pattern shown in the README:
<!-- markdownlint-capture -->
<!-- markdownlint-disable MD013 -->

looong line

<!-- markdownlint-restore -->

I am not inclined to add more complexity for this scenario as it seems pretty niche.

source: gharchive/issue

authors: DavidAnson RebeccaStevens

repo: DavidAnson/markdownlint

license: mit

license_type: permissive

license_source: bigquery


text:
New rule: No line wrapping

As a sort of antithesis to MD013, I'd want a new (off by default) rule that enforces all paragraphs to be on a single line, with a blank line in between.
Under this proposed rule, this would fail:
A paragraph of text, formatted to fit 80 characters per line, for the most
orthodox way of writing Markdown.  However, imagine having to add a sentence
before this one, and then having to spend the time reorganising all this text
to fully utilise the 80 characters per line to keep it tidy.  Sounds like a
lot of effort for an unclear benefit.

Paragraphs should have an empty line in between.

But this would pass:
This paragraph is on a single line. Most places where you'd write Markdown these days, like GitHub and VSCode, already have built-in dynamic line wrapping that's appropriate to the width of the editor.  You can concentrate on authoring text, rather than tidying it.

Paragraphs should still have an empty line in between.

For what it's worth, I definitely understand the rationale for MD013, and I don't think it should be turned off by default.
But in many modern contexts, code editors and diff viewers have built-in line wrapping, which adjust based on the width of the editor, rather than baked in at <80 characters.
And as far as I can tell, there's also no enforcement that the author does indeed utilise all 80 characters fully, so the linter can let through some annoyingly formatted text;
In this paragraph, there is no consistent line wrapping, and so the
words are wherever
they
happen to
be

On the other hand, if you want to utilise the 80 characters to its fullest, but then had to add a sentence in the middle of a long paragraph, you'd need to spend time manually re-flowing the subsequent text, and cause a less clear git diff in the process.
Additionally, for the work I do, a single line break between text is more often an unintended side effect of pasting content from a WYSIWYG text editor, rather than a deliberate effort by the author.
This is then exacerbated by the fact that some Markdown parsers (like GitHub) treat a newline in the middle of a paragraph as a <br/>, and so this baked-in line wrapping then bleeds into presentation as well. It can become ambiguous where the author did intend a forced line break, and where they were wrapping lines.
For these reasons, I'd like to have the option for a rule that enforces no line wrapping by the author. Turning this rule on would automatically turn MD013 off.

Duplicate of https://github.com/DavidAnson/markdownlint/issues/298

Agreed. Closing this as Nick has established a link here from there. Thanks for the thorough issue!

authors: DavidAnson groenroos nschonni


text:
Control cache directories

Bring the cache directories for both PIP and UV into the ducktools/env folder unless overridden.
This is mostly so if someone clears the ducktools cache it can also clear everything that env has downloaded.

For now I think I'm going to avoid doing this. If someone is already a Python user they may have significant data already in cache that this would end up re-downloading.

repo: DavidCEllis/ducktools-env

license: MIT

license_source: github-api


text:
Add basic 3D reconstruction datasets

Add a basic dataset (tf.data and torch.utils.DataLoader) for a 3D reconstruction dataset, to go along with NeRFs

Issue is too vague - should be separated into multiple issues on specific datasets. Closed as part of #92


text:
If initial images have values < 0, combined images will be subtracting counts

If initial images (images that XGA is pointed to in the configuration file), have values of < 0 in them (like XCS EPN images almost always do due to readout streak subtraction), they will be capped at 0 in code but when emosaic is run it will not be changed.
This means that combining images could actually be subtracting some counts.

My initials are that the simplest solution is to make a copy of the particular image in the local XGA output directory with corrected data, then just use that one.

I no longer use those XCS images, so this isn't a particular problem right now. Perhaps at some point I'll switch to some other way of combining images actually within Python and this will no longer be an issue. For now I'm closing this.


text:
Withdrawal error

I thought that the error Insufficient amount was cleared meanwhile. I transferdd 3,8 DFI form Cake to the App. Then  I tried to withdrawal the new amount from the App to Cake. Doesnt work. Is it still the Token problem? When you will fix that?

Hi @JoergHoffmann, can you try it on the latest version? We have addressed this issue of selecting token from address on the latest version. https://github.com/DeFiCh/app/releases/tag/v2.1.15

Doesnt work, with any  earlierer versions. I had have adress that issue more than 4 weeks ago. So, how can I transfer my DFI to Cake?

You can download the latest version manually and install them @JoergHoffmann.

Problem solved! Version 2.1.15 works

authors: JoergHoffmann thedoublejay

repo: DeFiCh/app


text:
🛑 UK Datacentre is down

In 2911cbf, UK Datacentre (http://www.thehostingfolks.org) was down:

HTTP code: 403
Response time: 621 ms

Resolved: UK Datacentre is back up in 6c7427a after 5 minutes.

repo: Deathbyteacup/thfstatuspage


text:
🛑 UK Datacentre is down

In 70e5a25, UK Datacentre (http://www.thehostingfolks.org) was down:

HTTP code: 403
Response time: 617 ms

Resolved: UK Datacentre is back up in aa0b629 after 15 minutes.


text:
No module named 'thirdparty.kpconv.lib'

hi when i try to run bash eval.sh or bash eval_lmocc.sh it gives me the no module named error, and i dont see thirdparty.kpconv.lib in this case, could you help me on this? am i missing something?

Hi, have you ever run the commands below? If you have, please drop me a message again.
cd RNNPose/scripts
bash compile_3rdparty.sh

Sorry. I find this directory is ignored by the .gitignore file.  I have corrected it and now everything should be ok. Thanks for your feedback.

thanks!

repo: DecaYale/RNNPose


text:
YOLONAS: load ckpt_best.pth after doing qat_from_recepie and export with different settings

💡 Your Question
After training and applying QAT with python -m super_gradinets.qat_from_recepie, the model is exported automatically. However, I don't have control over how it is exported. For example, the exported model had a batch size of 12 and didn't come with pre and post processing steps included. But I need that as I don't know what the pre and post processing steps are.
So I tried to load and export the model with the following code (as per this guide):
yolonas = models.get(Models.YOLO_NAS_S, checkpoint_path="<path-to-ckpt_best.pth>", num_classes=80)
yolonas.export("yolonas_s_int8.onnx", preprocessing=True, postprocessing=True, engine=ExportTargetBackend.TENSORRT, quantization_mode=ExportQuantizationMode.INT8)

But am getting error:
RuntimeError                              Traceback (most recent call last)
File ~/venvs/s-grads/lib/python3.10/site-packages/super_gradients/training/utils/checkpoint_utils.py:99, in adaptive_load_state_dict(net, state_dict, strict, solver)
     98     strict_bool = strict if isinstance(strict, bool) else strict != StrictLoad.OFF
---> 99     net.load_state_dict(state_dict, strict=strict_bool)
    100 except (RuntimeError, ValueError, KeyError) as ex:

File ~/venvs/s-grads/lib/python3.10/site-packages/torch/nn/modules/module.py:2152, in Module.load_state_dict(self, state_dict, strict, assign)
   2151 if len(error_msgs) > 0:
-> 2152     raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
   2153                        self.__class__.__name__, "\n\t".join(error_msgs)))
   2154 return _IncompatibleKeys(missing_keys, unexpected_keys)

RuntimeError: Error(s) in loading state_dict for YoloNAS_S:
        Missing key(s) in state_dict: "backbone.stem.conv.branch_3x3.conv.weight", "backbone.stem.conv.branch_3x3.bn.weight", "backbone.stem.conv.branch_3x3.bn.bias", "backbone.stem.conv.branch_3x3.bn.running_mean", "backbone.stem.conv.branch_3x3.bn.running_var", "backbone.stem.conv.branch_1x1.weight", "backbone.stem.conv.branch_1x1.bias", "backbone.stage1.downsample.branch_3x3.conv.weight", "backbone.stage1.downsample.branch_3x3.bn.weight", "backbone.stage1.downsample.branch_3x3.bn.bias", "backbone.stage1.downsample.branch_3x3.bn.running_mean", "backbone.stage1.downsample.branch_3x3.bn.running_var", "backbone.stage1.downsample.branch_1x1.weight", "backbone.stage1.downsample.branch_1x1.bias", "backbone.stage1.blocks.bottlenecks.0.cv1.branch_3x3.conv.weight", "backbone.stage1.blocks.bottlenecks.0.cv1.branch_3x3.bn.weight", "backbone.stage1.blocks.bottlenecks.0.cv1.branch_3x3.bn.bias", "backbone.stage1.blocks.bottlenecks.0.cv1.branch_3x3.bn.running_mean", "backbone.stage1.blocks.bottlenecks.0.cv1.branch_3x3.bn.running_var", "backbone.stage1.blocks.bottlenecks.0.cv1.branch_1x1.weight", "backbone.stage1.blocks.bottlenecks.0.cv1.branch_1x1.bias", "backbone.stage1.blocks.bottlenecks.0.cv2.branch_3x3.conv.weight", "backbone.stage1.blocks.bottlenecks.0.cv2.branch_3x3.bn.weight", "backbone.stage1.blocks.bottlenecks.0.cv2.branch_3x3.bn.bias", "backbone.stage1.blocks.bottlenecks.0.cv2.branch_3x3.bn.running_mean", "backbone.stage1.blocks.bottlenecks.0.cv2.branch_3x3.bn.running_var", "backbone.stage1.blocks.bottlenecks.0.cv2.branch_1x1.weight", "backbone.stage1.blocks.bottlenecks.0.cv2.branch_1x1.bias", "backbone.stage1.blocks.bottlenecks.1.cv1.branch_3x3.conv.weight", "backbone.stage1.blocks.bottlenecks.1.cv1.branch_3x3.bn.weight", "backbone.stage1.blocks.bottlenecks.1.cv1.branch_3x3.bn.bias", "backbone.stage1.blocks.bottlenecks.1.cv1.branch_3x3.bn.running_mean", "backbone.stage1.blocks.bottlenecks.1.cv1.branch_3x3.bn.running_var", "backbone.stage1.blocks.bottlenecks.1.cv1.branch_1x1.weight", "backbone.stage1.blocks.bottlenecks.1.cv1.branch_1x1.bias", "backbone.stage1.blocks.bottlenecks.1.cv2.branch_3x3.conv.weight", "backbone.stage1.blocks.bottlenecks.1.cv2.branch_3x3.bn.weight", "backbone.stage1.blocks.bottlenecks.1.cv2.branch_3x3.bn.bias", "backbone.stage1.blocks.bottlenecks.1.cv2.branch_3x3.bn.running_mean", "backbone.stage1.blocks.bottlenecks.1.cv2.branch_3x3.bn.running_var", "backbone.stage1.blocks.bottlenecks.1.cv2.branch_1x1.weight", "backbone.stage1.blocks.bottlenecks.1.cv2.branch_1x1.bias", "backbone.stage2.downsample.branch_3x3.conv.weight", "backbone.stage2.downsample.branch_3x3.bn.weight", "backbone.stage2.downsample.branch_3x3.bn.bias", "backbone.stage2.downsample.branch_3x3.bn.running_mean", "backbone.stage2.downsample.branch_3x3.bn.running_var", "backbone.stage2.downsample.branch_1x1.weight", "backbone.stage2.downsample.branch_1x1.bias", "backbone.stage2.blocks.bottlenecks.0.cv1.branch_3x3.conv.weight", "backbone.stage2.blocks.bottlenecks.0.cv1.branch_3x3.bn.weight", "backbone.stage2.blocks.bottlenecks.0.cv1.branch_3x3.bn.bias", "backbone.stage2.blocks.bottlenecks.0.cv1.branch_3x3.bn.running_mean", "backbone.stage2.blocks.bottlenecks.0.cv1.branch_3x3.bn.running_var", "backbone.stage2.blocks.bottlenecks.0.cv1.branch_1x1.weight", "backbone.stage2.blocks.bottlenecks.0.cv1.branch_1x1.bias", "backbone.stage2.blocks.bottlenecks.0.cv2.branch_3x3.conv.weight", "backbone.stage2.blocks.bottlenecks.0.cv2.branch_3x3.bn.weight", "backbone.stage2.blocks.bottlenecks.0.cv2.branch_3x3.bn.bias", "backbone.stage2.blocks.bottlenecks.0.cv2.branch_3x3.bn.running_mean", "backbone.stage2.blocks.bottlenecks.0.cv2.branch_3x3.bn.running_var", "backbone.stage2.blocks.bottlenecks.0.cv2.branch_1x1.weight", "backbone.stage2.blocks.bottlenecks.0.cv2.branch_1x1.bias", "backbone.stage2.blocks.bottlenecks.1.cv1.branch_3x3.conv.weight", "backbone.stage2.blocks.bottlenecks.1.cv1.branch_3x3.bn.weight", "backbone.stage2.blocks.bottlenecks.1.cv1.branch_3x3.bn.bias", "backbone.stage2.blocks.bottlenecks.1.cv1.branch_3x3.bn.running_mean", "backbone.stage2.blocks.bottlenecks.1.cv1.branch_3x3.bn.running_var", "backbone.stage2.blocks.bottlenecks.1.cv1.branch_1x1.weight", "backbone.stage2.blocks.bottlenecks.1.cv1.branch_1x1.bias", "backbone.stage2.blocks.bottlenecks.1.cv2.branch_3x3.conv.weight", "backbone.stage2.blocks.bottlenecks.1.cv2.branch_3x3.bn.weight", "backbone.stage2.blocks.bottlenecks.1.cv2.branch_3x3.bn.bias", "backbone.stage2.blocks.bottlenecks.1.cv2.branch_3x3.bn.running_mean", "backbone.stage2.blocks.bottlenecks.1.cv2.branch_3x3.bn.running_var", "backbone.stage2.blocks.bottlenecks.1.cv2.branch_1x1.weight", "backbone.stage2.blocks.bottlenecks.1.cv2.branch_1x1.bias", "backbone.stage2.blocks.bottlenecks.2.cv1.branch_3x3.conv.weight", "backbone.stage2.blocks.bottlenecks.2.cv1.branch_3x3.bn.weight", "backbone.stage2.blocks.bottlenecks.2.cv1.branch_3x3.bn.bias", "backbone.stage2.blocks.bottlenecks.2.cv1.branch_3x3.bn.running_mean", "backbone.stage2.blocks.bottlenecks.2.cv1.branch_3x3.bn.running_var", "backbone.stage2.blocks.bottlenecks.2.cv1.branch_1x1.weight", "backbone.stage2.blocks.bottlenecks.2.cv1.branch_1x1.bias", "backbone.stage2.blocks.bottlenecks.2.cv2.branch_3x3.conv.weight", "backbone.stage2.blocks.bottlenecks.2.cv2.branch_3x3.bn.weight", "backbone.stage2.blocks.bottlenecks.2.cv2.branch_3x3.bn.bias", "backbone.stage2.blocks.bottlenecks.2.cv2.branch_3x3.bn.running_mean", "backbone.stage2.blocks.bottlenecks.2.cv2.branch_3x3.bn.running_var", "backbone.stage2.blocks.bottlenecks.2.cv2.branch_1x1.weight", "backbone.stage2.blocks.bottlenecks.2.cv2.branch_1x1.bias", "backbone.stage3.downsample.branch_3x3.conv.weight", "backbone.stage3.downsample.branch_3x3.bn.weight", "backbone.stage3.downsample.branch_3x3.bn.bias", "backbone.stage3.downsample.branch_3x3.bn.running_mean", "backbone.stage3.downsample.branch_3x3.bn.running_var", "backbone.stage3.downsample.branch_1x1.weight", "backbone.stage3.downsample.branch_1x1.bias", "backbone.stage3.blocks.bottlenecks.0.cv1.branch_3x3.conv.weight", "backbone.stage3.blocks.bottlenecks.0.cv1.branch_3x3.bn.weight", "backbone.stage3.blocks.bottlenecks.0.cv1.branch_3x3.bn.bias", "backbone.stage3.blocks.bottlenecks.0.cv1.branch_3x3.bn.running_mean", "backbone.stage3.blocks.bottlenecks.0.cv1.branch_3x3.bn.running_var", "backbone.stage3.blocks.bottlenecks.0.cv1.branch_1x1.weight", "backbone.stage3.blocks.bottlenecks.0.cv1.branch_1x1.bias", "backbone.stage3.blocks.bottlenecks.0.cv2.branch_3x3.conv.weight", "backbone.stage3.blocks.bottlenecks.0.cv2.branch_3x3.bn.weight", "backbone.stage3.blocks.bottlenecks.0.cv2.branch_3x3.bn.bias", "backbone.stage3.blocks.bottlenecks.0.cv2.branch_3x3.bn.running_mean", "backbone.stage3.blocks.bottlenecks.0.cv2.branch_3x3.bn.running_var", "backbone.stage3.blocks.bottlenecks.0.cv2.branch_1x1.weight", "backbone.stage3.blocks.bottlenecks.0.cv2.branch_1x1.bias", "backbone.stage3.blocks.bottlenecks.1.cv1.branch_3x3.conv.weight", "backbone.stage3.blocks.bottlenecks.1.cv1.branch_3x3.bn.weight", "backbone.stage3.blocks.bottlenecks.1.cv1.branch_3x3.bn.bias", "backbone.stage3.blocks.bottlenecks.1.cv1.branch_3x3.bn.running_mean", "backbone.stage3.blocks.bottlenecks.1.cv1.branch_3x3.bn.running_var", "backbone.stage3.blocks.bottlenecks.1.cv1.branch_1x1.weight", "backbone.stage3.blocks.bottlenecks.1.cv1.branch_1x1.bias", "backbone.stage3.blocks.bottlenecks.1.cv2.branch_3x3.conv.weight", "backbone.stage3.blocks.bottlenecks.1.cv2.branch_3x3.bn.weight", "backbone.stage3.blocks.bottlenecks.1.cv2.branch_3x3.bn.bias", "backbone.stage3.blocks.bottlenecks.1.cv2.branch_3x3.bn.running_mean", "backbone.stage3.blocks.bottlenecks.1.cv2.branch_3x3.bn.running_var", "backbone.stage3.blocks.bottlenecks.1.cv2.branch_1x1.weight", 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"heads.head1.cls_pred._weight_quantizer._amax", "heads.head1.reg_pred._input_quantizer._amax", "heads.head1.reg_pred._weight_quantizer._amax", "heads.head2.stem.seq.conv._input_quantizer._amax", "heads.head2.stem.seq.conv._weight_quantizer._amax", "heads.head2.cls_convs.0.seq.conv._input_quantizer._amax", "heads.head2.cls_convs.0.seq.conv._weight_quantizer._amax", "heads.head2.reg_convs.0.seq.conv._input_quantizer._amax", "heads.head2.reg_convs.0.seq.conv._weight_quantizer._amax", "heads.head2.cls_pred._input_quantizer._amax", "heads.head2.cls_pred._weight_quantizer._amax", "heads.head2.reg_pred._input_quantizer._amax", "heads.head2.reg_pred._weight_quantizer._amax", "heads.head3.stem.seq.conv._input_quantizer._amax", "heads.head3.stem.seq.conv._weight_quantizer._amax", "heads.head3.cls_convs.0.seq.conv._input_quantizer._amax", "heads.head3.cls_convs.0.seq.conv._weight_quantizer._amax", "heads.head3.reg_convs.0.seq.conv._input_quantizer._amax", "heads.head3.reg_convs.0.seq.conv._weight_quantizer._amax", "heads.head3.cls_pred._input_quantizer._amax", "heads.head3.cls_pred._weight_quantizer._amax", "heads.head3.reg_pred._input_quantizer._amax", "heads.head3.reg_pred._weight_quantizer._amax".

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
Cell In[2], line 1
----> 1 model = models.get('yolo_nas_s', checkpoint_path="checkpoints/coco2017_yolo_nas_s_qat/RUN_20240131_184123_827969/ckpt_best.pth", num_classes=80)

File ~/venvs/s-grads/lib/python3.10/site-packages/super_gradients/common/decorators/factory_decorator.py:36, in resolve_param.<locals>.inner.<locals>.wrapper(*args, **kwargs)
     34             new_value = factory.get(args[index])
     35             args = _assign_tuple(args, index, new_value)
---> 36 return func(*args, **kwargs)

File ~/venvs/s-grads/lib/python3.10/site-packages/super_gradients/training/models/model_factory.py:241, in get(model_name, arch_params, num_classes, strict_load, checkpoint_path, pretrained_weights, load_backbone, download_required_code, checkpoint_num_classes, num_input_channels)
    239     load_processing = "processing_params" in ckpt_entries
    240     load_ema_as_net = "ema_net" in ckpt_entries
--> 241     _ = load_checkpoint_to_model(
    242         ckpt_local_path=checkpoint_path,
    243         load_backbone=load_backbone,
    244         net=net,
    245         strict=strict_load,
    246         load_weights_only=True,
    247         load_ema_as_net=load_ema_as_net,
    248         load_processing_params=load_processing,
    249     )
    250 if checkpoint_num_classes != num_classes:
    251     net.replace_head(new_num_classes=num_classes)

File ~/venvs/s-grads/lib/python3.10/site-packages/super_gradients/common/decorators/factory_decorator.py:36, in resolve_param.<locals>.inner.<locals>.wrapper(*args, **kwargs)
     34             new_value = factory.get(args[index])
     35             args = _assign_tuple(args, index, new_value)
---> 36 return func(*args, **kwargs)

File ~/venvs/s-grads/lib/python3.10/site-packages/super_gradients/training/utils/checkpoint_utils.py:1523, in load_checkpoint_to_model(net, ckpt_local_path, load_backbone, strict, load_weights_only, load_ema_as_net, load_processing_params)
   1521     adaptive_load_state_dict(net.backbone, checkpoint, strict)
   1522 else:
-> 1523     adaptive_load_state_dict(net, checkpoint, strict)
   1525 message_suffix = " checkpoint." if not load_ema_as_net else " EMA checkpoint."
   1526 message_model = "model" if not load_backbone else "model's backbone"

File ~/venvs/s-grads/lib/python3.10/site-packages/super_gradients/training/utils/checkpoint_utils.py:102, in adaptive_load_state_dict(net, state_dict, strict, solver)
    100 except (RuntimeError, ValueError, KeyError) as ex:
    101     if strict == StrictLoad.NO_KEY_MATCHING:
--> 102         adapted_state_dict = adapt_state_dict_to_fit_model_layer_names(net.state_dict(), state_dict, solver=solver)
    103         net.load_state_dict(adapted_state_dict["net"], strict=True)
    104     elif strict == StrictLoad.KEY_MATCHING:

File ~/venvs/s-grads/lib/python3.10/site-packages/super_gradients/training/utils/checkpoint_utils.py:1458, in adapt_state_dict_to_fit_model_layer_names(model_state_dict, source_ckpt, exclude, solver)
   1455 if len(exclude):
   1456     model_state_dict = {k: v for k, v in model_state_dict.items() if not any(x in k for x in exclude)}
-> 1458 new_ckpt_dict = solver(model_state_dict, source_ckpt)
   1459 return {"net": new_ckpt_dict}

File ~/venvs/s-grads/lib/python3.10/site-packages/super_gradients/training/utils/checkpoint_utils.py:212, in DefaultCheckpointSolver.__call__(self, model_state_dict, checkpoint_state_dict)
    210 for (ckpt_key, ckpt_val), (model_key, model_val) in zip(checkpoint_state_dict.items(), model_state_dict.items()):
    211     if ckpt_val.shape != model_val.shape:
--> 212         raise ValueError(f"ckpt layer {ckpt_key} with shape {ckpt_val.shape} does not match {model_key}" f" with shape {model_val.shape} in the model")
    213     new_ckpt_dict[model_key] = ckpt_val
    214 return new_ckpt_dict

ValueError: ckpt layer backbone.stem.conv.post_bn.weight with shape torch.Size([48]) does not match backbone.stem.conv.branch_3x3.conv.weight with shape torch.Size([48, 3, 3, 3]) in the model

It would also help if this guide came with the pre and post processing code when doing inference with TensorRT.
I believe I am missing something. There must be a way to load the quantized model right?
Versions
SG version: 3.6.0
PyTorch version: 2.1.2+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.35
Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.133.1-microsoft-standard-WSL2-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3060 Laptop GPU
Nvidia driver version: 546.17
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      39 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             12
On-line CPU(s) list:                0-11
Vendor ID:                          GenuineIntel
Model name:                         12th Gen Intel(R) Core(TM) i5-12450H
CPU family:                         6
Model:                              154
Thread(s) per core:                 2
Core(s) per socket:                 6
Socket(s):                          1
Stepping:                           3
BogoMIPS:                           4991.93
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology tsc_reliable nonstop_tsc cpuid pni pclmulqdq vmx ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves avx_vnni umip waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm serialize flush_l1d arch_capabilities
Virtualization:                     VT-x
Hypervisor vendor:                  Microsoft
Virtualization type:                full
L1d cache:                          288 KiB (6 instances)
L1i cache:                          192 KiB (6 instances)
L2 cache:                           7.5 MiB (6 instances)
L3 cache:                           12 MiB (1 instance)
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected
Versions of relevant libraries:
[pip3] numpy==1.23.0
[pip3] onnx==1.13.0
[pip3] onnx-graphsurgeon==0.3.27
[pip3] onnxruntime==1.13.1
[pip3] onnxsim==0.4.35
[pip3] pytorch-quantization==2.1.2
[pip3] torch==2.1.2+cu118
[pip3] torchmetrics==0.8.0
[pip3] torchvision==0.16.2+cu118
[pip3] triton==2.1.0
[conda] Could not collect

I decided to just save the model object with pickle although I don't like it.
I think the weight file should be loadable by SG.

No, saving model as pickle it definitely not the right way to save the model state.
As of now, qat_from_recepie don't have much control on how to export a final model (with or without postprocessing). This feature is lacking unfortunately.  This is a good place for improvement, but currently I'm unable to give a time estimate when this may be introduced.

repo: Deci-AI/super-gradients


text:
Updated packaging format

Created setup.cfg and pyproject.toml files, moved package directory to src/, applied black formatting. Resolves #9.

Thank you for this pull request and your work on this. We appreciate your efforts and really like your changes with respect to the updated docstrings, the introduction of types, and the overall (toml) architecture. However there are several edits that would need to be reverted in order for this PR to become useful:

In the toml file black and flake8 (as dependency and their config) is very special to your setup and would need to be removed.
Some files appear to be deleted and recreated in another folder (e.g. Solver.py), while others (e.g. Conformal.py) are simply moved (at least on GitHub when viewing the changed files). This is problematic as the diff for these files is lost, in contrast to those that were moved. This makes it hard for us to assess any potential changes within these files. If possible treat all files the same and make sure that they are moved and diffs show correctly.
Code formatting lines: a line width of 79 characters is too restrictive, and we’d like to avoid multi-line wraps such as ll. 428 ff. in src/lapy/Solver.py, for example. Further your lint formatter breaks in strange places (leaving closing brackets on their own line etc). So please remove the automated line breaking. In some places line splits look good (e.g. in Functions with parameter initialisations) and can stay.
Code formatting quotes: also we do not care about single or double quotes. Developers can decide what they prefer. So those don't have to be changed. In fact we slightly prefer single quotes, to make it easier to use " inside (which is more common than the other way around). But we don't really care.
configurations.py: constants that are not used across functions / modules such as ta00, ta11, tb, … in src/lapy/configuration.py should stay local in their respective files (src/Solver.py in this case). Since only the default config is left, we would like to revert the configurations.py file completely.
messages.py: similarly most messages are very specific, and should stay in their original files / functions, also to avoid too much switching between files during development. Please revert both messages and config files. There are also no plans to support other languages than english.
Your gitignore contains many paths that are special to your setup. Optimally we try to avoid polluting the code with personal settings.

Will send a similar reply to your other PR in brainprint. And thanks again!

I would like to give short feedback on this PR as I am discovering both tools LaPy and FastSurfer, and as it relates to #16.
@ZviBaratz If you don't want to continue this PR, I don't mind picking it up and addressing the comments from @m-reuter.

black and flake8 are not specific to a setup. They are listed as dev dependencies, which can be installed with the additional keyword. Code style dependencies are common and usually enforced in CI or even in pre-commit. I would argue you could also add codespell, isort, pydocstyle and mypy. Those tools are here to improve the overall code quality and readability.
The line width of 79 characters is indeed restrictive. This now old standard has been replaced and is not the default black uses. black defaults to 88, and other projects tend to use 100.
black: code formatting quotes, code format, line length.. black brings a convention, a standard to how a python code should look like: fix line-length, line breaks in a certain reproducible way, double quotes, and so on.. It's now adopted by more and more projects (IIRC numpy, scikit-learn, ..) and is improving code readability. The standard is also very easy to follow as a simple command black folder will blackify all the python files in the given folder. More and more IDEs also have black available to auto-format files, removing the need for the command mentioned.
And finally, the point relating to #16, the codebase should indeed not be polluted by personal settings, but the .gitignore should capture files generated by the most common IDEs and by editable installation, commonly used by developers. Else, uncommitted files are constantly present in the directory tree. As an example, here is the .gitignore from numpy and from scikit-learn.

My 2c, from a fellow developer.

Hi @mscheltienne, thank you for your comment. I actually created this PR following a PR in another project from that lab (Deep-MI/BrainPrint-python#5, please see for more context). I agree with everything you wrote, I'll just add that if you decide to continue to work on this and single quotes are for some reason a hard requirement, you can also use blue. In any case, I will not be working on this further, you are very welcome to pick things up from here.

OK. Also I now realize that both this package and BrainPrint-python are not released on Pypi or a conda distribution channel. IMO, it's a shame to not distribute those 2 packages widely.. and it would be very quick to do so.

Again, I entirely agree, but I am not sure what are the authors' intentions. My impression from the comments here and in the other PR were that these tools are developed exclusively by and for the purposes of that particular lab. Without the core maintainers' support in implementing Python best practices and promoting collaborative development, I would recommend either adopting a different approach or working independently.

Hi, thanks for you valuable comments. It is our intention to support collaborative development and we are very happy about your support! We did initially not agree with all your edits, some of which are style design decisions that we did not share. If there is good reason (e.g. standards that are adopted by other large packages) we are happy to revise our position.
Also it would be great to have Pypi and/or conda distributions.

@m-reuter glad to hear, and thank you.
In that case, @mscheltienne please let me know if you need help with anything or need in clarifications picking up where I left off.

I merged this branch into #16 to keep the changes from @ZviBaratz and its contribution in the git tree, but I did change the packaging/configuration slightly compared to this PR.

I removed the nesting of the package in a src directory. This is common practice for other programming languages, but not so much for Python. See for instance numpy or scipy .
This change also fixes your comment "Some files appear to be deleted and recreated in another folder", since they are now back in the original folder.

There are 3 main ways to package a python library: setup.py (now legacy), setup.cfg and pyproject.toml. setup.cfg is still very popular, but it is more and more replaced by the newer pyproject.toml standard which I used here.

I added CI workflows for:

build (not so important for pure Python packages, but it makes sure that a package is correctly installed from the different sources)
code-style, running different style checker among which flake8, black, isort which format the code in a standard way; codespell which helps to correct some typos, pydocstyle to enforce numpy-style documentation.
pytest to run unit tests
publish to release the project on Pypi. This workflow is for now configured with a manual trigger, but ideally you wuold enable the trigger on 'published new release'. It also requires a PYPI_API_TOKEN secrets, I can give you detail instruction on how to generate one and where to save it later, it's very simple.

Changes to the actual code:

isort sorting out imports (there is a standard order :smile:) and black formatting the code.
an util  development tool with an entry-point lapy-sys_info to display information about the system, the interpreter, the dependencies (inspired from MNE-Python)
an util to manage optional dependencies, sksparse in this case (inspired from pandas), used in the Solver.

I removed configurations.py and messages.py as requested by @m-reuter and actually reverted changes to the code except the one mentioned above, bringing the focus of this PR on packaging.

With those changes, the pure packaging aspects are covered and the library can be released easily on pip and conda-forge. Since they are no unit tests, I invite you to give a shot to the library as it is in #16 to confirm that everything is working, you can install it with pip install git+https://github.com/mscheltienne/LaPy/tree/gitignore.
One more point, all the changes here aim at both improving the user experience by offering an easier install via pip and conda, while keeping or even reducing maintenance. With the workflows, cutting a new release is as easy as using the "Create a new release" feature of GitHub, and then letting the workflows do the release on Pypi and on conda-forge.
Finally, further changes which are not related to packaging could be added in a future PR, e.g.

refactoring of the doc strings as started in this PR by @ZviBaratz since the current doc strings do not comply with conventions (my IDE is full of DXXX warnings :sweat_smile:). Usually, I use the numpy convention.
After the docstrings are updated, the pydocstyle workflow could be enable and a documentation build could be added, generating a website from the code (and the doc strings) and deploying it automatically on pushes to the main branch.
Replacing prints with logger outputs
...

If you want, I don't mind opening those subsequent PR, transforming this repository and project further. You will then be able to use this repository as a 'template' to follow good practices and conventions for future projects of your lab.

Wow, thanks so much for these fast edits. We will take a look. It would be great to automate the release in the future. Also the future PR ideas sound great.

source: gharchive/pull-request

authors: ZviBaratz m-reuter mscheltienne

repo: Deep-MI/LaPy


text:
API for Write

Is it possible to have API for https://www.deepl.com/write

Hi, we don't have a public API for DeepL Write at the moment, as it is in beta. If we were to offer one, we will publish its OpenAPI spec here.

authors: JanEbbing banana-dog

repo: DeepLcom/openapi


text:
Zmz/aclnn4

Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.
Motivation and Context

Description

Use cases (Optional)

BC-breaking (Optional)

Checklist
Before PR:

[ ] I have read and followed the workflow indicated in the Contributors.md to create this PR.
[ ] Pre-commit or linting tools indicated in Contributors.md are used to fix the potential lint issues.
[ ] Bug fixes are covered by unit tests, the case that causes the bug should be added in the unit tests.
[ ] New functionalities are covered by complete unit tests. If not, please add more unit test to ensure the correctness.
[ ] The documentation has been modified accordingly, including docstring or example tutorials.

After PR:

[ ] CLA has been signed and all committers have signed the CLA in this PR.

 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.You have signed the CLA already but the status is still pending? Let us recheck it.

authors: CLAassistant hellozmz


text:
Some obfuscation in the instance function "to_numpy" of class"BVH_file" in retargeting/datasets/bvh_parser.py

Hi, I was kind of confused about a code snippet in the instance function "to_numpy" of class"BVH_file" in retargeting/datasets/bvh_parser.py.
from line #180 to line #184
if edge:
 >>>>index = []
>>>> for e in self.edges:
>>>>>>>> index.append(e[0])
>>>>rotations = rotations[:, index, :]
rotations = rotations.reshape(rotations.shape[0], -1)
According to my understanding so far, after doing:
rotations = self.anim.rotations[:, self.corps, :]  in line #174
the variable "rotation" will hold the rotation info of the simplified skeleton.  Then why we still need to do the operation from line #180 to line #184?
B.T.W could you please explain the "MOTION" part of the .bvh file for me? I searched some explainations from the website, but I didn't get a satisfied one. I guessed each line of the "MOTION" part presents the rotation information of the skeleton's joints in one frame.  But how to understand the order of these numbers？
Many thanks!

The "MOTION" part works like this: in the "HIERARCHY" part, there are "CHANNELS" segment, in which there are identifiers like "Xposition", "Yrotation". In the "MOTION" part, each line represents one frame and the numbers follow the order of identifiers in HIERARCHY part, e.g. the first 6 numbers represents the position and rotation (Euler angles) for the root joint, respectively. If it's still not clear, I would recommend you to read utils/BVH.py to get more specific description.
After understanding how bvh works, you will find that for a joint with more than one child joint, the child joints are rigid: a same rotation is always applied to all of them, because the rotation of a joint in bvh file is applied to its children instead of itself. But we use "armature" in our paper, which requires each armature (edge) to have different rotations, so we reorganize the representation of rotations, as line #180 - #184 do, to make the rotations of each joint applied to itself.

Thanks for the patient explaination!
But I still had a question, for example, a .bvh file "HIERARCHY" defined as blow:
HIERARCHY
ROOT Hips
{
>>>>OFFSET 0.000000 35.082581 0.000000
>>>>CHANNELS 6 Xposition Yposition Zposition Xrotation Yrotation Zrotation
>>>>JOINT Spine
>>>>{
>>>>>>>>OFFSET 0.000000 6.876099 0.125700
>>>>>>>>CHANNELS 3 Xrotation Yrotation Zrotation
>>>>>>>>JOINT Spine1
>>>>>>>>{...}
>>>>>>>> ...
>>>>}
>>>>JOINT LeftUpLeg
>>>>{
>>>>>>>>OFFSET 10.205400 -3.823500 0.155100
>>>>>>>>CHANNELS 3 Xrotation Yrotation Zrotation
>>>>>>>>JOINT LeftLeg
>>>>>>>>{...}
>>>>}
}
Now the 1st ~ 6th number in "MOTION" part represent  the position and rotation (Euler angles) for the root joint "Hips", and 7th~9th numbers represent the rotation of the joint "Spine", then how about the 10th~12th number? Do them belongs to the joint "Spine1" or the joint "LeftUpLeg"?
Many thanks!

They belong to Spine1. More specifically, the numbers follow the order that the identifiers appear in plain text.

They belong to Spine1. More specifically, the numbers follow the order that the identifiers appear in plain text.

Thanks! That's exactly the point that I'm doubting of!

The "MOTION" part works like this: in the "HIERARCHY" part, there are "CHANNELS" segment, in which there are identifiers like "Xposition", "Yrotation". In the "MOTION" part, each line represents one frame and the numbers follow the order of identifiers in HIERARCHY part, e.g. the first 6 numbers represents the position and rotation (Euler angles) for the root joint, respectively. If it's still not clear, I would recommend you to read utils/BVH.py to get more specific description.
After understanding how bvh works, you will find that for a joint with more than one child joint, the child joints are rigid: a same rotation is always applied to all of them, because the rotation of a joint in bvh file is applied to its children instead of itself. But we use "armature" in our paper, which requires each armature (edge) to have different rotations, so we reorganize the representation of rotations, as line #180 - #184 do, to make the rotations of each joint applied to itself.

If so, does it mean the root joint will never have a rotation applied to itself?

I think I made some mistake or inaccuracy before. From the perspective of bvh file definition, each joint is a local coordinate system and connects to its child joints, which are also coordinate systems, with a fixed offset in its local coordinate system. And the rotation of a joint is applied to its coordinate system. So from this perspective, it's applied to itself. Since the offset is fixed, the child joints of one joint is "rigid". It also means a rotation on a joint doesn't affect its position.
But from the perspective of edge (armature), it's kind of subtle. In our paper we represent an edge with the joint corresponding to its end point. (As I mentioned before, we want to make the child joints of a joint not to be rigid, so each edge should be assigned to different rotations.) This means that the rotation corresponding to a joint, is not applied to its corresponding edge (the edge connects it and its parent).

So from this perspective, it's applied to itself. Since the offset is fixed, the child joints of on

Ah, got it! So the rotation part of "global root position & rotation" feature vector which mentioned in the paper(in Fig.4, the blue one) just means the rotation applied on the root joint's local coordinate system?

Yes. However, in the implementation it is copied three times, by line #180 ~ $184. We keep a rotation for each edge.

Yes. However, in the implementation it is copied three times, by line #180 ~ $184. We keep a rotation for each edge.

Thank you so much!

@PeizhuoLi Hi, Thanks for your great contribution of this repo ! I am also confused about the code snippet in #180 ~ #184. As far as I know, the e[0] is the parent joint index of each joint, if we only use parent indexes to select rotations, would the complete rotations be preserved? How can we recover original rotations from edges?  For example, the effectors (hand, head, and etc.) are not the parents of any joints, where are the original rotations of effectors placing, if we just use the parent indexes to reorder rotation topology?

Hi, regarding the edge representation, a simple solution could be attach one placeholder child joint to those end-effectors. Alternatively, you may use the joint representation and make some slight change to the pooling process.
The root orientation is not ignored, it actually is duplicated for three times since the joint joints has three children.

I think not only root rotation is repeated ,but also Spine2's rotation is repeated.

authors: ANYMS-A LuletterSoul PeizhuoLi crissallan mjdxyr

repo: DeepMotionEditing/deep-motion-editing


text:
+!>!+[!Here's freE Watch!]!! ++ Buffalo Bills vs Miami Dolphins Live Streams Free ON TV CHANNEL 3 NOV 2024

NFL RedZone FREE LIVE STREAM (11/3/24): Time TV, channel for Week 9 NFL games NFL RedZone — an around-the-horn live highlight show demonstrating all the action on NFL Sunday — is back for Week 9 of the 2024 season.
Week 9 starts with the Dallas Cowboys vs. Atlanta Falcons, Denver Broncos vs. Baltimore Ravens, Miami Dolphins vs. Buffalo Bills and more games at 1 p.m. ET. From there, the Chicago Bears vs. Arizona Cardinals kick off the late afternoon games.
Here are the best streaming options for NFL RedZone this year:
FuboTV (free trial): NFL RedZone is available in FuboTV’s Sports Plus add-on. The add-on service costs $10.99 per month.
DIRECTV STREAM (free trial): DirecTV Stream customers can get NFL RedZone by subscribing to DirectTV Stream’s “Premier” package. The plan costs $155 a month plus taxes and fees.
Sling TV (Half off the first month): This is the cheapest option to watch NFL RedZone via a streaming service. It will cost $11 dollars to add NFL RedZone to Sling TV’s Blue Package that is $45 per month.
Here’s what you need to know:
What: NFL Week 9, RedZone
Who: Falcons, Bills, Commanders, Eagles, more
When: Sunday, Nov. 3, 2024
Time: Broadcast begins at 12:30 p.m. ET
TV: NFL RedZone
Live stream: FUBOTV (free trial), DIRECTV STREAM (free trial), Sling TV (Half of the first month)
Here are the NFL Week 9 games that will be featured on RedZone:
Sunday, Nov. 3
Early Slate
Cowboys vs. Falcons at 1 p.m. ET on FOX
FREE LIVE STREAM: Fubo TV, DirecTV Stream
Broncos vs. Ravens at 1 p.m. ET on CBS
FREE LIVE STREAM: Fubo TV, DirecTV Stream
Dolphins vs. Bills at 1 p.m. ET on CBS
FREE LIVE STREAM: Fubo TV, DirecTV Stream
Saints vs. Panthers at 1 p.m. ET on CBS
FREE LIVE STREAM: Fubo TV, DirecTV Stream
Raiders vs. Bengals at 1 p.m. ET on FOX
FREE LIVE STREAM: Fubo TV, DirecTV Stream
Chargers vs. Browns at 1 p.m. ET on CBS
FREE LIVE STREAM: Fubo TV, DirecTV Stream
Commanders vs. Giants at 1 p.m. ET on FOX
FREE LIVE STREAM: Fubo TV, DirecTV Stream
Patriots vs. Titans at 1 p.m. ET on FOX
FREE LIVE STREAM: Fubo TV, DirecTV Stream
Late Slate
Bears vs. Cardinals at 4:05 p.m. ET on CBS
FREE LIVE STREAM: Fubo TV, DirecTV Stream
Jaguars vs. Eagles at 4:05 p.m. ET on CBS
FREE LIVE STREAM: Fubo TV, DirecTV Stream
Lions vs. Packers at 4:25 p.m. ET on FOX
FREE LIVE STREAM: Fubo TV, DirecTV Stream
Rams vs. Seahawks at 4:25 p.m. ET on FOX
FREE LIVE STREAM: Fubo TV, DirecTV Stream
Here’s a recent NFL story via the Associated Press:
People are buzzing about New York Jets receiver Malachi Corley having his first-ever touchdown overturned when he dropped the ball right before crossing the goal line.
How is that possible? Well, it happens more frequently than you might imagine with the Jets rookie only the latest one wanting the celebrate too soon.
New York Jets WR Malachi Corley, 2024
With the Jets at the Texans' 19 early in the second quarter, Corley took a flip from Aaron Rodgers and zipped down the right sideline. Corley sped into the end zone, but just before he crossed the goal line, he dropped the ball out of his right hand to celebrate. Video review showed what happened so Corley was credited with an 18-yard run and a fumble. Worse for the Jets, the ball rolled out of the end zone for a touchback and turnover.
Atlanta TE Kyle Pitts, 2024
Call this one a near miss of a mess. Pitts was finishing off a 49-yard touchdown catch in the second quarter against Tampa Bay when he let off the gas and was caught right before the end zone by safety Antoine Winfield Jr. Winfield knocked the ball out as Pitts dangled it at his side, but an official ruled Pitts broke the plane of the goal line first.
Clemson WR Ray Ray McCloud, 2016
Sometimes it happens in college. That was the case for Pitts' current Atlanta teammate, receiver Ray Ray McCloud, who was finishing off a 75-yard punt return score against Troy eight years ago when he tossed the ball behind him at the 1. “You’re going to leave this game thinking about” my mistake, McCloud said after the Tigers 30-24 victory.
Philadelphia WR DeSean Jackson, 2008
Jackson was a rookie who was celebrating his apparent touchdown against Dallas by flipping the ball behind him. Only he was at the 1-yard line when he did it, nullifying the score.
Pittsburgh TE Dave Smith, 1971
Considered the granddaddy of such celebration gaffes was that of Smith, who was on his way to a 50-yard TD catch against Kansas City when he pumped his arms over his head — with the ball in his left hand — at the 5 when the ball slips out and rolls through the end zone. The play was seen across the country on Monday Night Football.
Dallas DL Leon Lett, 1993
RECOMMENDED•nj.com
How to watch NFL RedZone online without cable for Week 9 (11/3/24) | Free trials, live stream, costNov. 3, 2024, 6:00 a.m.
NFL odds Week 9: Today’s betting lines include this week’s Lions vs. Packers point spreadOct. 28, 2024, 11:41 a.m.
Perhaps the most famous chase down and celebration mistake in NFL history came in Super Bowl XXVII on Jan. 31, 1993, when speedy Buffalo receiver Don Beebe caught the Cowboys' 290-pound lineman who was holding the football out with his right hand in celebration of his should-be defensive score. Dallas led 52-17 at the time, so Lett’s error didn’t keep the Cowboys from celebrating a title.

ALL Spo

ALL Spo

ALL Spo

ALL Spo

ALL Spo

ALL Spo

ALL Spo

DDD

repo: DeepSourceCorp/good-first-issue


text:
IEcsAutoReset Analyzer

[ ] Non-static members cannot be accessed
[ ] T in IEcsAutoReset<T> should be the same as struct name

this.ToString()

ToString()


text:
Use a smaller font on items for better conversational flow

Tweak the css.

done

repo: DemocracyEarth/sovereign


text:
Add Locationiq as a Provider

https://locationiq.org/#docs Looks like a nice provider with generous free tier!

Send a PR and I'll merge it.

all looks good 👍
closing the issue !

authors: DenisCarriere ebreton jamesharding

repo: DenisCarriere/geocoder


text:
Consider using IP2Location.io API as a failover

Currently, you're calling ipinfo.io for the geolocation. As a failover, please consider using the IP2Location.io API via the endpoint https://api.ip2location.io/.

Just for context:

IPinfo.io provides 1,000 requests per day without a token.
IPinfo provides 50,000 requests per month with a free tier token.
Universal access to a globally distributed load-balancing network regardless of access tier.
Higher accuracy driven by latency-based IP geolocation methods. (Blog post)

In addition to offering generous no-tier and free-tier access, we also have incredible uptime hours. If access or limits to our service is an issue, we are happy to look into it. Thanks.


text:
Не устанавливается руссификатор!

Из-за отсутствия руссификатора его установка не возможна, пофиксь пжпжпж

Поддерживаю, пытался сегодня установить, но когда обнаружил, что ничего нет, из окна выйти захотелось, но я переселил себя и теперь пишу этот коммент.

Руссификатор пока вроде как еще не вышел.

Не стал ждать этого перевода и перевел сам(Машинный перевод). https://github.com/Turokirill/RusRotN.git

Готово, русификатор ещё сыроват и в бете, но уже существует здесь:
https://github.com/DenisMasterHerobrine/RotN-Translation/releases

authors: DenisMasterHerobrine FlaxyDev Hu4uTa Suiranoil Turokirill

repo: DenisMasterHerobrine/RotN-Translation


text:
Publish inverter ID via simple_mqtt

I was wondering why this didn't happen for simple_mqtt, but for Home Assistant it did. It makes sense when you want to push data from multiple identical inverters to a single MQTT server, so you can distinguish between them.

[x] run cargo clippy and fix all issues
[x] run cargo fmt to format all source files

i also have another version running where the inverter ID is part of the published topic and not the payload, which for my personal setup makes more sense. if that's something you'd prefer, i can open another PR and close this one.

I think it would make sense to have the inverter ID as part of the published topic. It would be a breaking change however for the existing users and their applications that depend on the previously used topic. Maybe we can add a parameter "use_sn_in_topic" or something like that to make it optional

@DennisOSRM already seems to have started to implement reading environment variables in #61 as it looks like. what's the status of this?
the fix in this PR here should be done regardless i think, since the home assistant config already does that.
also there is #116 which requests that, basically. let me know if i should pick this up.

Oh yes, happy to consider PRs since I am somewhat spread thin with day job and other stuff right now. 👍🏽

alright. closing this one then.

authors: DennisOSRM bellackn dominikandreas

repo: DennisOSRM/hms-mqtt-publisher


text:
Universal Blog Card Feedback

Hello everyone!
I've created a versatile and dynamic Blog Card component using HTML and CSS. This project showcases a blog card designed to display blog posts with images and tags. It features an animated color transition, responsive design, and interactive hover effects. You can view the project here: Universal Blog Card.
I welcome any advice, suggestions, or contributions to help enhance the project. Your feedback is greatly appreciated!

Hello everyone,
As it has been more 2 week without any activity or feedback, I am closing this issue for now. Although no contributions or feedback were provided, I appreciate your consideration. Please note that the project remains open for any suggestions or improvements. If you have any ideas or changes you would like to propose in the future, feel free to open a new issue or submit a pull request.
Thank you!
Best regards,
Der12kl

license: ISC


text:
Citation to vaspvis

Hi,
How should I cite excellent vaspvis package?
Don't see information on that.
Thanks,

Hi Shahid,
I just updated the README with information on how to cite the package. Thank you for asking!

authors: DerekDardzinski shahid-sattar

repo: DerekDardzinski/vaspvis


text:
🛑 LUFi is down

In 1fd7c8d, LUFi (https://drop.deuchnord.fr) was down:

HTTP code: 502
Response time: 420 ms

Resolved: LUFi is back up in 6bc9566.

repo: Deuchnord/uptime


text:
🐛 [Bug]: DatePickerPro打开日历下拉面板时，不应该闪现两个滚动条

Version
latest
Vue Version
latest
Link to minimal reproduction
https://vue-devui.github.io/components/date-picker-pro/
官网第一个demo即可复现该问题：

Step to reproduce
https://vue-devui.github.io/components/date-picker-pro/

点击日期选择器输入框
弹出日历面板下拉框
此时会闪现两个滚动条，我觉得不应该出现滚动条，滚动条应该是鼠标移到相应的区域才出现

What is expected
No response
What is actually happening
No response
Any additional comments (optional)
No response

DatePickPro组件在第一次加载时没有判断 是否有滚动（ Math.abs(newScrollTop - state.scrollTop) !== 0） 的条件

DatePickPro组件在第一次加载时没有判断 是否有滚动（ Math.abs(newScrollTop - state.scrollTop) !== 0） 的条件

#1431 解决，应该是没有对初次打开面板时的条件做判断导致的，加上对 (Math.abs(newScrollTop - state.scrollTop) !== 0) 就好啦

repo: DevCloudFE/vue-devui


text:
🐛 [Bug]: 菜单选中效果

Version
"devui-theme": "^0.0.4",
Vue Version
"vue": "^3.3.4",
Link to minimal reproduction
代码地址
Step to reproduce
代码如下
<template>

      <d-menu
        mode="vertical"
        router
        v-for="item in userApps"
      >
        <d-sub-menu
          :title="item.meta?.title"
          v-if="item.children && item.children.length"
          :key="item.path"
        >
          <template #icon>
            {{ item.path }}

          </template>
          <d-menu-item
            v-for="children in item.children"
            :key="`${item.path}/${children.path}`"
          >
            {{ `${item.path}/${children.path}` }}

            {{ children.meta?.title }}
          </d-menu-item>
        </d-sub-menu>

      </d-menu>

</template>

What is expected
希望就是在点击某一项子菜单的时候别的子菜单选中状态消失

What is actually happening
实际上我选择一个，别的也不消失，除非有两个子菜单children才会消失

Any additional comments (optional)
文档上面写的是根据key值，但我看到key值其实也不一样啊

你的v-for="item in userApps"导致生成了多个d-menu，可以试试我这种写法。使用template在d-menu中循环就不会出现你那种问题啦
<template>

      <d-menu :multiple="false" mode="vertical" router>
        <template v-for="item in userApps">
          <d-menu-item
            :key="item.redirect"
            v-if="item.children != undefined && item.redirect"
          >
            <template #icon>

            </template>
            {{ item.meta?.title }}
          </d-menu-item>

          <d-sub-menu
            :key="item.path"
            :title="item.meta?.title"
            v-if="item.children != undefined && item.redirect == undefined"
          >
            <template #icon>

            </template>
            <d-menu-item
              v-for="children in item.children"
              :key="`${item.path}/${children.path}`"
            >
              <template #icon>

              </template>
              {{ children.meta?.title }}
            </d-menu-item>
          </d-sub-menu>
        </template>
      </d-menu>

</template>

已解决 @Whbbit1999


text:
feat(root): add better-scripts

This PR improves developers experience(DX) by adding better-script to introduce a new way of npm scripts writing.
Merge and Close #1186 .
Previous
Pain points:

😣 Too many scripts that hard to read and use
🧐 Need a way to reorganize those scripts

package.json
"scripts": {
  "dev": "pnpm --filter vue-devui dev",
  "build": "pnpm --filter vue-devui build",
  "build:lib": "pnpm --filter vue-devui build:lib",
  "build:cli": "pnpm --filter devui-cli build",
  "prepare": "husky install",
  "precommit": "lint-staged",
  "eslint": "eslint \"packages/**/{*.vue,*.js,*.ts,*.jsx,*.tsx}\"",
  "eslint:fix": "eslint --fix \"packages/**/{*.vue,*.js,*.ts,*.jsx,*.tsx}\"",
  "stylelint": "stylelint \"packages/**/{*.scss,*.css}\"",
  "stylelint:fix": "stylelint --fix \"packages/**/{*.scss,*.css}\"",
  "test": "pnpm --filter vue-devui test",
  "coverage": "pnpm --filter vue-devui coverage"
}

Now
package.json
"scripts": {
  "prepare": "husky install",
  "precommit": "lint-staged",
  "scripts": "better-scripts"
}

More
Please check better-scripts docs and give a star ⭐ if you like.

@iamyoki github actions的脚本可否同步改下？🤝
主要包含：
.github/workflows/preview-build.yml
.github/workflows/pull-request.yml

另外，已点亮better-scripts的Star😜
. prepare$ husky install
. prepare: husky - Git hooks installed
. prepare: Done
undefined
 ERR_PNPM_RECURSIVE_EXEC_FIRST_FAIL  Command failed with ENOENT: build
spawn build ENOENT
Error: Process completed with exit code 1.

@kagol Thanks for your star. Github actions updated.

@all-contributors please add @iamyoki for code.

authors: iamyoki kagol


text:
Force DataSourceLoader to load data before it is returned from a controller's action

// GET
using (var db = new MyDbContext())
{
    return this.Request.CreateResponse(DataSourceLoader.Load(db.Table0, loadOptions));
}

This code throws ObjectDisposedException because the MyDbContext instance is disposed of before the inner query is executed.
Related:

https://devexpress.com/issue=T720659

Workarounds: https://github.com/DevExpress/DevExtreme.AspNet.Data/issues/359#issuecomment-505463961

authors: AlekseyMartynov ilyapavlovdx


text:
Cant seem to trigger onValueChange, onCellPrepared, editCellTemplate

When in cell editing mode is there a way to fire a function once the value has changed
I've tried to fire onCellPrepared, editCellTemplate and onValueChanged but nothing seems to log
HTML
       <dx-data-grid id="gridContainer" [dataSource]="cash" [columns]="customcols" [editing]="editing">         </dx-data-grid>
this.editing = {
allowUpdating: true,
mode: 'cell',
allowAdding: false,
allowDeleting: false,
onValueChanged: function (cellElement, cellInfo) {
console.log(cellElement, cellInfo)
}
customcols = [{
dataField: item,
dataType: 'number',
onCellPrepared: function (cellElement) {
console.log(cellElement)
}
// editCellTemplate: function (cellElement, cellInfo) {
// console.log(cellElement, cellInfo);
//},
}];

I solved my issue using  (onEditorPreparing) and (onCellPrepared)
plnkr below
https://plnkr.co/GwgBD1Mp8XF5oLZoYt4k?p
But now changes in my bottom grid flow though to my top even though they are using different datasets but from the same subscription

While you are binding the first grid to the "cash" variable and the second grid to "cash2", these variables reference the same array ("data" that comes from your service). So, when you modify an item of the "cash2" array, the "cash" array is modified as well. See the Plunker that illustrates this behavior with a standard ngFor directive.
To edit the arrays separately, you need to make a copy of the original array. You can do this, for example, like this.cash2 = JSON.parse(JSON.stringify(data));. See the updated Plunker.

authors: GoshaFighten gwsampso

repo: DevExpress/devextreme-angular


text:
Allow taking screenshots of the entire page

Provide the capability to take a screenshot of the entire page, not just the visible area. Such a screenshot should include content not visible on the screen due to overflow.

Hi,

Do you mean taking a screenshot of the whole page, not only of its visible part (e.g., if the page has a large scrollbar height, you'd like to see the whole page in a screenshot)? Do I understand you right?

Yes

I'm looking for the same usibility.
I have found that for now taking fullpage screenshots is available only on firefox.
https://saucelabs.com/blog/Selenium-Tips-Capturing-Screenshots-vs-Scrollbars

@AlexanderMoskovkin did you implement this feature already?

@AlexanderMoskovkin thanks for your answer. Would be a great feature IMO

@AlexanderMoskovkin I'm also interested in this feature. Thanks for looking into it.

@AlexanderMoskovkin  We're looking forward to this feature very much. Thanks for planing it.

Hi @marcin-chwedczuk-meow, thanks for feedback! You can automatically take screenshots on failures using the corresponding option, but it will take only the visible part of a page? of course. Could you please explain, what useful or important things can be in the invisible part of a page? Could you share your experience?

One use-case that comes to my mind is that captured area of the screen does not contain all useful information. Like e.g. let's say we have a page with all invoice data and for some reasons a particular test fails at the bottom of the page. I would appreciate if I see not only part of the page where test failed but also all other invoice data that may be on the screen (my test is concentrating on a business side of the things - so I am not interested that e.g. button is disabled but why it is disabled). So indeed for acceptance tests this may be useful.

Another use case is Visual Validation testing:
You write a Testcafe-driven workflow that visits many pages. On each page, it capture a screenshot of the full page and then sends it to a visual validation tool. This tool looks at the image & compares it to a previous "baseline" image. It then returns a "difference" score (e.g. 0.00 == images identical; 0.01+ - 100.0 == % change). You then write assertions that fail the test above a threshold value.
Examples of visual validation tools include Resemble.js, Applitools, or Galen Framework (no affiliation with these). This makes it easier for people to quickly know & see which pages have changed on their websites (especially when you support many different sites & locales).
There are other automation frameworks that let you capture full-screen screenshots, but I'd rather use TestCafe than have to support two different frameworks only for including this type of testing.
Thanks for considering this feature!

I hope. .resizeWindow can't

I add --window-size='1360,768' in my package.json and it worked for me.
full line
"test-chrome-report": "./node_modules/.bin/cucumber-js  --format json:./reports/report.json --world-parameters \"{\\\"browser\\\": \\\"chrome --window-size='1360,768'\\\"}\""

I'm also experiencing this issue where the entire screenshot of a page is not being taken in desktop, only a portion of it.
@fraalvper It would be great if you could provide more details on this workaround. In the command you have shown, it is referring to tests run on cucumber. Are you using gherkin-testcafe or some tool like that instead of testcafe?

Can anyone suggest a workaround for this until this is done? I tried t.resizeWindow() but this isn't working for me on chrome:headless. I am looking for a workaround for headless mode.

@Pr60, have you tried to use the emulation mode? You can emulate a device with the screen size that is equal to the maximum size of your page.

@bdjos1 Yes im using a suit with testcafe + cucumber. This is my package.json
https://github.com/fralvarop/testcafe-cucumberjs/blob/master/package.json

@AndreyBelym @fralvarop - Screenshot problem got resolved after I implemented both of yours solution but some of my tests started failing because there are multiple scrollbars in my page which did not respond properly because of small page size on my Mac. Could there be another possible way? or do we know a timeline for the implementation of this feature?

@AndreyBelym what is the maximum screen size of our page? It could be 1 page or 10 pages long and would possibly change during the test so I don't know if setting a hardcoded value is really a solution. If I set it to some large value, will that mean I always get a really long screenshot even if the page isn't that large?

@alexschwantes
You can detect dimensions of your page using document.clientWidht and document.clientHeight in ClientFunction. After that you can resize your browser by the resize method and then call thetakeScreenshot method. Please note that is better to do it in Emulation and Headless mode.
@Pr60 @alexschwantes
We know how this feature is important for users, so we added it in our Roadmap for 2019
Since this feature is highly requested we consider it as one of our prior tasks

@AlexKamaev
this is an issue we're looking at right now and trying to devise a workaround. We have a mix of large and small pages, most outside the visible area. I've had no success with document.clientWidth or height, they seem undefined when used with the ClientFunction. Did you mean something like ClientFunction(() => window.document.body.clientHeight)? The problem with clientWidth or clientHeight is that it's still the size of the visible window, even in Headless mode, and not the full page.
I've found a better solution that works is to use the scrollHeight on the body element. This produces full page screenshots.
const pageHeight = ClientFunction(() => window.document.body.scrollHeight)
t.resizeWindow(1200, await pageHeight() );

However, one problem with this method is that it works only when the pages are ordered from large to small. The canvas seems 'stretched' when ordered the other (see #3648 )

@byrne-greg
Thanks, for this page I have document.body.clientHeight equal to document.body.scrollHeight, but it does not matter, in any case, you got it right. I am happy to hear that this workaround works for you.

However, one problem with this method is that it works only when the pages are ordered from small to large during test execution. The page seems 'stretched' when ordered the other way around

This behavior is very strange, we'll discuss it in the ticket you provided

+1 for taking entire window/browser screenshot.

authors: AlexKamaev AlexanderMoskovkin AndreyBelym Grawl HenningThiemann Pr60 adamorlowskipoland alexschwantes bdjos1 byrne-greg fraalvper fralvarop jantonypdx jiteshsojitra marcin-chwedczuk-meow moffsugita rquellh zhaoace

repo: DevExpress/testcafe


text:
Fixture is not defined

reporting a bug
Cannot run any test on TestCafe. I've defined my login page model and created a basic fixture to validate successful login. But fixture isn't recognize while running test on yarn.
What is the current behavior?
The REPL reports that the fixture function is not defined
What is the expected behavior?
See the test run
Dev environment

operating system: Windows 10
testcafe version: 0.19.2
node.js version: 8.11.1
yarn version: 1.6.0

Hi @Kavignon,
Could you please provide the test code? TestCafe looks for a fixture definition in files passed to the test runner. Otherwise, the fixture is not defined. To create a sequence of actions, you can use testController .

I will close the issue because no activity has been encountered for a long time.
Feel free to reopen the issue if the problem persists and you can provide an example to reproduce it.

authors: Kavignon kirovboris miherlosev


text:
How to remove hostname and port (added by testcafe) from URL?

What is the current behavior?
Failing to open an internal company page because testcafe adds "http://192.168.15.12:51713/8wtjCRmaq/" before the real URL.. http://192.168.15.12:51713/8wtjCRmaq/https://www.google.com/?gws_rd=ssl**
What is the expected behavior?
Testcafe should navigate to google.com  page
How would you reproduce the current behavior (if this is a bug)?
Test case not to write "http://hostname/port/xx" in the URL field along with google.com page.
Provide the test code and the tested page URL (if applicable)
Tested page URL:
Google.com
Test code
Basic code.
import {selector} from 'testcafe';
fixture MyFixture
.meta(fixtureID,1)
.meta({author:Dev, CreationDate:20 May 2020})
.meta({ severity: 'low', testedWebVersion: '1.0' })
.page http://www.google.com/;
test ('google description', async t=> {
await t
.navigateTo('`http://www.example.com/')
.setNativeDialogHandler(() => true)
.click('#populate')
.click('#submit-button');
});
Specify your
operating system: Win 10
testcafe version: 1.8.5
node.js version: 13.13.0
Chrome:81

Rewriting url's is one of core TestCafe concepts. You cannot remove it. Please refer to the following article: https://devexpress.github.io/testcafe/documentation/how-it-works/#reverse-proxy
Probably you cannot navigate to the target page because there is a proxy server in your local network. If so, please try the approach we described here: https://devexpress.github.io/testcafe/documentation/reference/command-line-interface.html#--proxy-host
Feel free to reopen this issue if you have further questions.

I am using wifi and the Proxy server is off on my machine.

So you mean the application will have 2 URLs: Testcafe proxy URL+Application url and application will work as usual?

Yes, you are right. For the https://example.com the full URL will be http://proxy-url:port/example.com.
You need to add round brackets for the getUrl function. See the following fixed code example:
import { ClientFunction } from 'testcafe';

fixture `Fixture`
	.page('https://example.com');

const getUrl = ClientFunction(() => window.location.href);

test('test', async t => {
	await t.expect(getUrl()).eql('https://example.com/');
});