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stringlengths 20
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| labels
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classes |
|---|---|
Title: About uint16 support
Body: Pytorch 2.3 is introducing unsigned integer dtypes like `uint16`, `uint32` and `uint64` in https://github.com/pytorch/pytorch/pull/116594.
Quoting Ed:
> The dtypes are very useless right now (not even fill works), but it makes torch.uint16, uint32 and uint64 available as a dtype.
I tried `uint16` on some of the transforms and the following would work:
```py
x = torch.randint(0, 256, size=(1, 3, 10, 10), dtype=torch.uint16)
transforms = T.Compose(
[
T.Pad(2),
T.Resize(5),
T.CenterCrop(3),
# T.RandomHorizontalFlip(p=1),
# T.ColorJitter(2, 2, 2, .1),
T.ToDtype(torch.float32, scale=True),
]
)
transforms(x)
```
but stuff like flip or colorjitter won't work. In general, it's safe to assume that uint16 doesn't really work on eager.
--------
### What to do about `F.to_tensor()` and `F.pil_to_tensor()`.
Up until 2.3, passing a unit16 PIL image (mode = "I;16") to those would produce:
- `to_tensor()`: an `int16` tensor as ouput for. This is completely wrong and a bug: the range of `int16` is smaller than `uint16`, so the resulting tensor is incorrect and has tons of negative value (coming from overflow).
- `pil_to_tensor()`: an error - this is OK.
Now with 2.3 (or more precisely with the nightlies/RC):
- `to_tensor()`: still outputs an int16 tensor which is still incorrect
- `pil_to_tensor()` outputs a uint16 tensor which is correct - but that tensor won't work with a lot of the transforms.
**Proposed fix**
- Keep `pil_to_tensor()` as-is, just write a few additional tests w.r.t. uint16 support
- Make `to_tensor()` return uint16 tensor instead of int16. This is a bug fix. Users may get loud errors down the line when they're using that uint16 on transforms (because uint16 is generally not well supported), but a loud error is much better than a silent error, which is what users were currently getting
- Document in https://pytorch.org/vision/main/transforms.html#supported-input-types-and-conventions that uint16, uint32 and uint64 aren't officially supported by the torchvision transforms - most users should stick to uint8 or float.
--------
Dirty notebook to play with:
<details>
```py
%
%load_ext autoreload
%autoreload 2
import numpy as np
import torchvision.transforms.v2 as T
import torchvision.transforms.v2.functional as F
from PIL import Image
import torch
torch.__version__
#%%
x = torch.randint(100, (512, 512), dtype=torch.int16)
#%%
x_pil = F.to_pil_image(x)
x_pil.mode # I;16
#%%
F.pil_to_tensor(x_pil).dtype # torch.uint16
# %%
F.to_tensor(x_pil).dtype # torch.int16
# %%
x = np.random.randint(0, np.iinfo(np.uint16).max, (10, 10), dtype=np.uint16)
x_pil = Image.fromarray(x, mode="I;16")
x_pil.mode # I;16
# %%
F.pil_to_tensor(x_pil).dtype # torch.uint16
# %%
torch.testing.assert_close(torch.from_numpy(x)[None], F.pil_to_tensor(x_pil))
# %%
F.to_tensor(x_pil).dtype # torch.int16
# %%
torch.testing.assert_close(torch.from_numpy(x)[None].float(), F.to_tensor(x_pil).float())
# %%
x = torch.randint(0, 256, size=(1, 3, 10, 10), dtype=torch.uint16)
transforms = T.Compose(
[
T.Pad(2),
T.Resize(5),
T.CenterCrop(3),
# T.RandomHorizontalFlip(p=1),
# T.ColorJitter(2, 2, 2, .1),
T.ToDtype(torch.float32, scale=True),
]
)
transforms(x)
#
```
</details> | 1medium
|
Title: Color API use is not discoverable
Body: cc @thibaudh
We have received repeated questions about how to use color bindings and where the palettes are. I'm thinking: make more prominent in the homepage docs links, cross-link in the ReadTheDocs bind call docs, and cross-link in the relevant tutorials.
| 1medium
|
Title: Standard headers properties to all endpoints in one project
Body: Hi,
We have a custom header for our project that should be on all endpoints,
Is there a way to configure it in one place (Not on every "extend_scheme")?
I tried looking for a configuration that adds headers by default to all endpoints but failed to find one. | 1medium
|
Title: How to set_epoch with interleave_datasets?
Body: Let's say I have dataset A which has 100k examples, and dataset B which has 100m examples.
I want to train on an interleaved dataset of A+B, with stopping_strategy='all_exhausted' so dataset B doesn't repeat any examples. But every time A is exhausted I want it to be reshuffled (eg. calling set_epoch)
Of course I want to interleave as IterableDatasets / streaming mode so B doesn't have to get tokenized completely at the start.
How could I achieve this? I was thinking something like, if I wrap dataset A in some new IterableDataset with from_generator() and manually call set_epoch before interleaving it? But I'm not sure how to keep the number of shards in that dataset...
Something like
```
dataset_a = load_dataset(...)
dataset_b = load_dataset(...)
def epoch_shuffled_dataset(ds):
# How to make this maintain the number of shards in ds??
for epoch in itertools.count():
ds.set_epoch(epoch)
yield from iter(ds)
shuffled_dataset_a = IterableDataset.from_generator(epoch_shuffled_dataset, gen_kwargs={'ds': dataset_a})
interleaved = interleave_datasets([shuffled_dataset_a, dataset_b], probs, stopping_strategy='all_exhausted')
``` | 1medium
|
Title: API key should be made optional in `LLMEvaluator` and co
Body: Now that we support using the evaluator(s) with locally hosted models (thorough the `api_base_url` API parameter), the `api_key` init parameter should be made optional.
| 1medium
|
Title: Install DeepSpeed fail with setuptools-77.0.3
Body: ```bash
root@csi67c88jasm4-0:/workspace/LLaMA-Factory# pip list
Package Version Editable project location
--------------------------------- ------------- ---------------------------------
accelerate 1.2.1
aiofiles 23.2.1
aiohappyeyeballs 2.6.1
aiohttp 3.11.14
aiosignal 1.3.2
airportsdata 20250224
annotated-types 0.7.0
anyio 4.9.0
astor 0.8.1
async-timeout 5.0.1
attrs 25.3.0
audioread 3.0.1
av 14.2.0
blake3 1.0.4
cachetools 5.5.2
certifi 2025.1.31
cffi 1.17.1
charset-normalizer 3.4.1
click 8.1.8
cloudpickle 3.1.1
compressed-tensors 0.9.2
contourpy 1.3.1
cupy-cuda12x 13.4.1
cycler 0.12.1
datasets 3.2.0
dbus-python 1.2.18
decorator 5.2.1
depyf 0.18.0
dill 0.3.8
diskcache 5.6.3
distro 1.7.0
dnspython 2.7.0
docstring_parser 0.16
einops 0.8.1
email_validator 2.2.0
exceptiongroup 1.2.2
fastapi 0.115.11
fastapi-cli 0.0.7
fastrlock 0.8.3
ffmpy 0.5.0
filelock 3.18.0
fire 0.7.0
fonttools 4.56.0
frozenlist 1.5.0
fsspec 2024.9.0
gguf 0.10.0
gradio 5.21.0
gradio_client 1.7.2
groovy 0.1.2
h11 0.14.0
httpcore 1.0.7
httptools 0.6.4
httpx 0.28.1
huggingface-hub 0.29.3
idna 3.10
importlib_metadata 8.6.1
interegular 0.3.3
jieba 0.42.1
Jinja2 3.1.6
jiter 0.9.0
joblib 1.4.2
jsonschema 4.23.0
jsonschema-specifications 2024.10.1
kiwisolver 1.4.8
lark 1.2.2
lazy_loader 0.4
librosa 0.11.0
llamafactory 0.9.2 /workspace/LLM_test/LLaMA-Factory
llvmlite 0.43.0
lm-format-enforcer 0.10.11
markdown-it-py 3.0.0
MarkupSafe 2.1.5
matplotlib 3.10.1
mdurl 0.1.2
mistral_common 1.5.4
mpmath 1.3.0
msgpack 1.1.0
msgspec 0.19.0
multidict 6.2.0
multiprocess 0.70.16
nest-asyncio 1.6.0
networkx 3.4.2
ninja 1.11.1.3
nltk 3.9.1
numba 0.60.0
numpy 1.26.4
nvidia-cublas-cu12 12.4.5.8
nvidia-cuda-cupti-cu12 12.4.127
nvidia-cuda-nvrtc-cu12 12.4.127
nvidia-cuda-runtime-cu12 12.4.127
nvidia-cudnn-cu12 9.1.0.70
nvidia-cufft-cu12 11.2.1.3
nvidia-curand-cu12 10.3.5.147
nvidia-cusolver-cu12 11.6.1.9
nvidia-cusparse-cu12 12.3.1.170
nvidia-cusparselt-cu12 0.6.2
nvidia-nccl-cu12 2.21.5
nvidia-nvjitlink-cu12 12.4.127
nvidia-nvtx-cu12 12.4.127
openai 1.67.0
opencv-python-headless 4.11.0.86
orjson 3.10.15
outlines 0.1.11
outlines_core 0.1.26
packaging 24.2
pandas 2.2.3
partial-json-parser 0.2.1.1.post5
peft 0.12.0
pillow 11.1.0
pip 25.0.1
platformdirs 4.3.7
pooch 1.8.2
prometheus_client 0.21.1
prometheus-fastapi-instrumentator 7.1.0
propcache 0.3.0
protobuf 6.30.1
psutil 7.0.0
py-cpuinfo 9.0.0
pyarrow 19.0.1
pycountry 24.6.1
pycparser 2.22
pydantic 2.10.6
pydantic_core 2.27.2
pydub 0.25.1
Pygments 2.19.1
PyGObject 3.42.1
pyparsing 3.2.1
python-dateutil 2.9.0.post0
python-dotenv 1.0.1
python-json-logger 3.3.0
python-multipart 0.0.20
pytz 2025.1
PyYAML 6.0.2
pyzmq 26.3.0
ray 2.44.0
referencing 0.36.2
regex 2024.11.6
requests 2.32.3
rich 13.9.4
rich-toolkit 0.13.2
rouge-chinese 1.0.3
rpds-py 0.23.1
ruff 0.11.1
safehttpx 0.1.6
safetensors 0.5.3
scikit-learn 1.6.1
scipy 1.15.2
semantic-version 2.10.0
sentencepiece 0.2.0
setuptools 77.0.3
shellingham 1.5.4
shtab 1.7.1
six 1.17.0
sniffio 1.3.1
soundfile 0.13.1
soxr 0.5.0.post1
sse-starlette 2.2.1
ssh-import-id 5.11
starlette 0.46.1
sympy 1.13.1
termcolor 2.5.0
threadpoolctl 3.6.0
tiktoken 0.9.0
tokenizers 0.21.0
tomlkit 0.13.2
torch 2.6.0
torchaudio 2.6.0
torchvision 0.21.0
tqdm 4.67.1
transformers 4.49.0
triton 3.2.0
trl 0.9.6
typer 0.15.2
typing_extensions 4.12.2
tyro 0.8.14
tzdata 2025.1
urllib3 2.3.0
uvicorn 0.34.0
uvloop 0.21.0
vllm 0.8.1
watchfiles 1.0.4
websockets 15.0.1
wheel 0.45.1
xformers 0.0.29.post2
xgrammar 0.1.16
xxhash 3.5.0
yarl 1.18.3
zipp 3.21.0
```
When I install deepspeed with `pip install deepspeed`
I got this:
```error report
TypeError: Command.__init__() got an unexpected keyword argument 'use_ninja'
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for deepspeed
Running setup.py clean for deepspeed
Failed to build deepspeed
```
Then I downgrade setuptools with `pip install setuptools==58.0.4` and succeed to install deepspeed.
I will check if it works as well in practice as it seems.
| 1medium
|
Title: Improve speed for box blur (mean filter)
Body: ## 🚀 Feature
<!-- A clear and concise description of the feature proposal -->
Originally from this: https://github.com/kornia/kornia/issues/720#issuecomment-881859300
In theory box blur (mean filter) can be implemented in O(n) time.
Two solutions
- Use 2 separable 1D kernels (like `gaussian_blur2d()`) -> O(nk) time, should be simple. Only launch 2 pytorch calls.
- Use intergral image (like [here](https://github.com/wuhuikai/DeepGuidedFilter/blob/master/GuidedFilteringLayer/GuidedFilter_PyTorch/guided_filter_pytorch/box_filter.py)) -> O(n) time. It seems like there will be quite a lot of overhead for small kernels
## Motivation
<!-- A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too -->
## Pitch
<!-- A clear and concise description of what you want to happen. -->
## Alternatives
<!-- A clear and concise description of any alternative solutions or features you've considered, if any. -->
## Additional context
<!-- Add any other context or screenshots about the feature request here. -->
______________________________________________________________________
#### Consider also to contribute to Kornia universe projects :)
<sub>
- [**Tutorials**](https://github.com/kornia/tutorials): our repository containing the tutorials.
</sub>
| 2hard
|
Title: pandera 0.12.0 does not support Python 3.7
Body: When trying to import pandera 0.12.0 in python 3.7, I get the following error message:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "...\lib\site-packages\pandera\__init__.py", line 50, in <module>
from . import errors, pandas_accessor, typing
File "...\lib\site-packages\pandera\pandas_accessor.py", line 7, in <module>
from . import schemas
File "...\lib\site-packages\pandera\schemas.py", line 13, in <module>
from typing import (
ImportError: cannot import name 'Literal' from 'typing' (...\lib\typing.py)
A quick search shows that Literal does not exist in typing in python 3.7 (https://stackoverflow.com/questions/61206437/importerror-cannot-import-name-literal-from-typing)
A clear and concise description of what the bug is.
- [X] I have checked that this issue has not already been reported.
- [X] I have confirmed this bug exists on the latest version of pandera.
- [ ] (optional) I have confirmed this bug exists on the master branch of pandera.
**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug.
#### Code Sample, a copy-pastable example
```python
import pandera
```
#### Expected behavior
A clear and concise description of what you expected to happen.
No error.
#### Desktop (please complete the following information):
- OS: Windows 10
- Browser [e.g. chrome, safari] N/A
- Version: pandera 0.12.0, python 3.7
#### Screenshots
If applicable, add screenshots to help explain your problem.
#### Additional context
Add any other context about the problem here.
| 1medium
|
Title: 希望能在WeixinMP构造方法中增加proxies代理设置参数
Body: 希望能在WeixinMP构造方法中增加proxies代理设置参数
```python
def __init__(self, app_id, app_secret, ac_path=None, jt_path=None, ac_callback=None, jt_callback=None, proxies=None):
"""
:param :app_id 微信app id
:param :app_secret 微信app secret
:param :ac_path access token 保存路径
:param :jt_path js ticket 保存路径
:param :ac_callback ac_callback
:param :jt_callback jt_callback
"""
self.app_id = app_id
self.app_secret = app_secret
self.session = requests.Session()
self.session.proxies = proxies # 为requests设置代理
if ac_path is None:
ac_path = os.path.join(DEFAULT_DIR, ".access_token")
if jt_path is None:
jt_path = os.path.join(DEFAULT_DIR, ".jsapi_ticket")
self.ac_path = ac_path
self.jt_path = jt_path
self.ac_callback = ac_callback
self.jt_callback = jt_callback
```
| 1medium
|
Title: S3 custom domain should be based on bucket name
Body: I override the class `S3Boto3Storage` to create my own custom storage like below
```python
class MediaStorage(S3Boto3Storage):
bucket_name = 'my-media-bucket'
```
I was expecting the file url generated by my custom storage `MediaStorage` will honor the bucket name and give the correct file url when I do the following:
```python
media_storage = MediaStorage()
media_storage.save(additional_path, file_obj)
file_url = media_storage.url(additional_path)
```
However, the generated `file_url` gives me a 404. When I look into the url, it did not use the bucket name of `MediaStorage` in domain name. After I looked at the implementation of the class `S3Boto3Storage`, the url is indeed not using `bucket_name` to generate url, and instead it always uses `settings.AWS_S3_CUSTOM_DOMAIN`, which I already set for my static file storage.
I can see that the file is uploaded on S3, and the url S3 generated for the file is like `https://my-media-bucket.s3.amazonaws.com/path/to/the/file/`. I was expecting when I override `bucket_name`, the storage should use the bucket name to generate the url.
I'm wondering is there any reason not to use bucket name to generate the url, or set the custom domain?
I'm thinking of in class `S3Boto3Storage`, `def __init__`, let `custom_domain` honor `bucket_name` during the initialization. There're two ways I can think of:
1. Set `custom_domain` to `self.custom_domain = '{}.s3.amazonaws.com'.format(self.bucket_name)`
2. Replace the lowest level subdomain of `custom_domain` by `bucket_name`, if custom_domain is set.
If any of these sounds good, I can create a PR, but just want to make sure if there's any consideration for the current behavior or concern about the change beforehand. Thank you! | 1medium
|
Title: 每个用户使用单独的词典
Body: <!--
注意事项和版本号必填，否则不回复。若希望尽快得到回复，请按模板认真填写，谢谢合作。
-->
## 注意事项
请确认下列注意事项：
* 我已仔细阅读下列文档，都没有找到答案：
- [首页文档](https://github.com/hankcs/HanLP)
- [wiki](https://github.com/hankcs/HanLP/wiki)
- [常见问题](https://github.com/hankcs/HanLP/wiki/FAQ)
* 我已经通过[Google](https://www.google.com/#newwindow=1&q=HanLP)和[issue区检索功能](https://github.com/hankcs/HanLP/issues)搜索了我的问题，也没有找到答案。
* 我明白开源社区是出于兴趣爱好聚集起来的自由社区，不承担任何责任或义务。我会礼貌发言，向每一个帮助我的人表示感谢。
* [ ] 我在此括号内输入x打钩，代表上述事项确认完毕。
## 版本号
<!-- 发行版请注明jar文件名去掉拓展名的部分；GitHub仓库版请注明master还是portable分支 -->
当前最新版本号是：
我使用的版本是：
<!--以上属于必填项，以下可自由发挥-->
## 我的问题
<!-- 请详细描述问题，越详细越可能得到解决 -->
## 复现问题
<!-- 你是如何操作导致产生问题的？比如修改了代码？修改了词典或模型？-->
### 步骤
1. 首先……
2. 然后……
3. 接着……
### 触发代码
```
public void testIssue1234() throws Exception
{
CustomDictionary.add("用户词语");
System.out.println(StandardTokenizer.segment("触发问题的句子"));
}
```
### 期望输出
<!-- 你希望输出什么样的正确结果？-->
```
期望输出
```
### 实际输出
<!-- HanLP实际输出了什么？产生了什么效果？错在哪里？-->
```
实际输出
```
## 其他信息
<!-- 任何可能有用的信息，包括截图、日志、配置文件、相关issue等等。-->
| 2hard
|
Title: Running query in superset sqllab with shillelagh driver returns Unsupported table error
Body: **Describe the bug**
Using shillelagh with superset. Running query on internal hosted json api url returns error.
`shillelagh error: Unsupported table: https://server.com/app/api/v1.0/storage/list`
Running the query on public API works. For example: "https://date.nager.at/api/v3/publicholidays/2023/US". So that gives me the confident that shillelagh is configured correctly on superset.
Both internal and public api shows header content-type: application/json; charset=utf-8
**To Reproduce**
Steps to reproduce the behavior:
1. from superset sqllab, run:
`SELECT * FROM "https://server.com/app/api/v1.0/storage/list"; ` <-- does not work
2. from superset sqllab, run:
`SELECT * FROM "https://date.nager.at/api/v3/publicholidays/2023/US"; ` <-- works
**Expected behavior**
Expect query from internal API url works by showing the table
**Screenshots**
If applicable, add screenshots to help explain your problem.
**Desktop (please complete the following information):**
- OS: Server OS is CentOS, client is Windows OS with Chrome browser
- Browser [e.g. chrome, safari]: Google Chrome
- Version [e.g. 22]:
**Additional context**
The internal API return content like below:
```
[
{
"id": "656d951ca902d21d40bdf0f8",
"path": "\\\\10.11.10.101\\datavol\\raw_file\\OX02C10\\P8P456\\OX02C10ABA_SS_60C_EM0508_JTT\\10A4\\CP0",
"remarks": "For _NC algorithm updates",
"creationDate": "2023-12-04T09:00:12.473Z",
"obsoleteDate": "0001-01-01T00:00:00Z",
"creator": "a.gong",
"creatorId": null
},
{
"id": "656e97c58c3d9373cb8eb15c",
"path": "\\\\10.11.10.101\\datavol\\raw_file\\OV50K40\\P8P672\\OV50K40AAA_FS_60C_EM0101\\03G6\\CP0",
"remarks": "migrated retention",
"creationDate": "2023-12-05T03:23:49.26Z",
"obsoleteDate": "0001-01-01T00:00:00Z",
"creator": "m.zou",
"creatorId": null
},
{
"id": "656eb0eaa902d21d40bdf0f9",
"path": "\\\\172.33.10.210\\oscStorage\\datavolOsc\\raw_file_eng\\OV50H40\\PL9155\\OV50H40ABA_FS_60C_EM1306\\04C6\\CP0",
"remarks": "pattern detection analysis",
"creationDate": "2023-12-05T05:11:06.979Z",
"obsoleteDate": "0001-01-01T00:00:00Z",
"creator": "k.mao",
"creatorId": null
}
]
```
| 1medium
|
Title: 同，后台添加节点报500，附上debug
Body: 找了一圈，发现就那一个和我一样，，但是没有看到解决方法，所以自己再发一下，
希望大佬看一下，还有上次我忘记在哪个群见到你了，，，唉，要不然直接QQ给你讲好了
Request Method: | POST
-- | --
http://myss.co/admin/ssserver/node/add/
2.0.5
IntegrityError
(1048, "Column 'password' cannot be null")
/root/.pyenv/versions/3.6.3/envs/venv-sspanel/lib/python3.6/site-packages/django/db/backends/mysql/base.py in execute, line 76
/root/.pyenv/versions/3.6.3/envs/venv-sspanel/bin/uwsgi
3.6.3
['.', '', '/root/.pyenv/versions/3.6.3/lib/python36.zip', '/root/.pyenv/versions/3.6.3/lib/python3.6', '/root/.pyenv/versions/3.6.3/lib/python3.6/lib-dynload', '/root/.pyenv/versions/3.6.3/envs/venv-sspanel/lib/python3.6/site-packages']
星期二, 5 六月 2018 12:57:36 +0800
Variable | Value
-- | --
args | [1, None, None, 'CN', 0, 1, 0, '1', None, '1', 'aes-128-ctr', 1.0, 'auth_chain_a', None, 'http_simple', None, 0, 1073741824, '1.0GB', 0, '0B', 1, '谜之屋']
query | ('INSERT INTO `ss_node` (`node_id`, `port`, `password`, `country`, ' '`custom_method`, `show`, `node_type`, `name`, `info`, `server`, `method`, ' '`traffic_rate`, `protocol`, `protocol_param`, `obfs`, `obfs_param`, `level`, ' '`total_traffic`, `human_total_traffic`, `used_traffic`, ' '`human_used_traffic`, `order`, `group`) VALUES (%s, %s, %s, %s, %s, %s, %s, ' '%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)')
self | <django.db.backends.mysql.base.CursorWrapper object at 0x7ff530e41048>
等等.....
我也不晓得该截那些，，，笑哭，，小白一枚。。。 | 1medium
|
Title: Can't upload anything to bucket
Body: I read the doc and after connected, I can't upload anything in the storage bucket.
````
url= "XXXXX"
key = "XXXXXXXXX
supabase: Client = create_client(url, key)
my_file = os.path.dirname(os.path.abspath("__file__")) + "/file.jpg"
storage.upload(path=supabase.storage_url, file=my_file)
````
It doesn't work and don't print any error.
| 1medium
|
Title: [Proposal] docs: gym -> gymnasium
Body: ### Proposal
A clear and concise description of the proposal.
### Motivation
Please outline the motivation for the proposal.
Is your feature request related to a problem? e.g.,"I'm always frustrated when [...]".
If this is related to another GitHub issue, please link here too.
### Pitch
A clear and concise description of what you want to happen.
### Alternatives
A clear and concise description of any alternative solutions or features you've considered, if any.
### Additional context
Add any other context or screenshots about the feature request here.
### Checklist
- [ ] I have checked that there is no similar [issue](https://github.com/Farama-Foundation/PettingZoo/issues) in the repo (**required**)
| 1medium
|
Title: 运行toolbox后进行录音，随后卡死
Body: 运行toolbox，使用https://github.com/babysor/MockingBird/wiki/Quick-Start-(Newbie) 中提到的模型，录音后程序卡死，
尝试选择不同的vocoder，程序也卡死
| 1medium
|
Title: Install on Macbook m1 chip
Body: conda install pyqt
conda install matplotlib
pip install labelme
**That works for me!**
| 3misc
|
Title: Date argument is returned as String type in info.field_asts
Body: ```python
import graphene
import datetime
class Patron(graphene.ObjectType):
id = graphene.ID()
name = graphene.String()
age = graphene.Int()
dob = graphene.types.datetime.Date()
class Query(graphene.ObjectType):
patron = graphene.Field(Patron, birth=graphene.types.datetime.Date())
def resolve_patron(self, info, birth):
print(info.field_asts[0])
return Patron(id=1, name="Syrus", age=27, dob=datetime.date(2018, 1, 1))
schema = graphene.Schema(query=Query)
query = """
query something{
patron(birth: "2018-01-01") {
id
name
age
dob
}
}
"""
if __name__ == "__main__":
result = schema.execute(query)
print(result.errors)
print(result.data["patron"])`
```
Running this python code prints following `info.field_asts`
```bash
$ python3.7 simple_example.py
Field(alias=None, name=Name(value='patron'), arguments=[Argument(name=Name(value='birth'), value=StringValue(value='2018-01-01'))], directives=[], selection_set=SelectionSet(selections=[Field(alias=None, name=Name(value='id'), arguments=[], directives=[], selection_set=None), Field(alias=None, name=Name(value='name'), arguments=[], directives=[], selection_set=None), Field(alias=None, name=Name(value='age'), arguments=[], directives=[], selection_set=None), Field(alias=None, name=Name(value='dob'), arguments=[], directives=[], selection_set=None)]))
```
**`Argument(name=Name(value='birth'), value=StringValue(value='2018-01-01'))`**
If you see closely, the argument birth comes back as `StringValue` but I expected a `DateValue`. Is this a bug or am I missing something?
| 1medium
|
Title: Web UI language not changing
Body: Hi,
I have installed LibreTranslate with docker image `libretranslate/libretranslate:latest`, but when I change the web ui language with icon on top-right, the language of the web ui does not change. I think this is a bug. | 1medium
|
Title: Downloading detection model too slow
Body: Hi, I when I run a code in Windows, it display "Downloading detection model, please wait. This may take several minutes depending upon your network connection."
Then it keep downloading for a long time.
even though I plug a VPN , its progress is very slow
I just use "pip install easyocr" to install
And the code is here:
#
import easyocr
#
reader = easyocr.Reader(['en'])
#
result = reader.readtext('1.jpg')
#
result
| 1medium
|
Title: [Bug]: Cannot change the run name through UI
Body: ### Describe the bug
I tried changing the name of my run through the UI.
But the figure does not change accordingly, still showing the original name
And the name on the left panel is back
when I reload the webpage
| 1medium
|
Title: ERROR - qlib.workflow - [utils.py:38] - An exception has been raised[ValueError: can't find a freq from [Freq(day)] that can resample to 1min!].
Body:
## To Reproduce
df = D.features([code], ['$high', '$low', '$close', '$open','$volume','$money'],
start_time='%s 09:31:00'%str(start_time)[:10],
end_time='%s 15:00:00'%str(end_time)[:10],
freq='1min', disk_cache=0)
## Screenshot
<img width="1062" alt="image" src="https://user-images.githubusercontent.com/41324537/155052775-e0cb1ca4-ba25-4b97-9e5a-dfba5ccc4da7.png">
## Environment
**Note**: User could run `cd scripts && python collect_info.py all` under project directory to get system information
and paste them here directly.
- Qlib version:Newest
- Python version:3.7.9
- OS (`Windows`, `Linux`, `MacOS`):MacOS
- Commit number (optional, please provide it if you are using the dev version):
| 1medium
|
Title: [BUG] App restarts without error message.
Body: 
Recently my app restarts when certain callbacks are called without any error message. I am running the same code that runs in successfully in production, but I updated dependencies. Any one else observing this behavior?
It's hard to debug this without info about what goes wrong.
```
dependencies:
- _libgcc_mutex=0.1
- _openmp_mutex=4.5
- blas=1.0
- bottleneck=1.3.5
- bzip2=1.0.8
- ca-certificates=2023.5.7
- click=8.1.3
- colorama=0.4.6
- ghp-import=2.1.0
- importlib-metadata=6.6.0
- jinja2=3.1.2
- ld_impl_linux-64=2.40
- libblas=3.9.0
- libcblas=3.9.0
- libexpat=2.5.0
- libffi=3.4.2
- libgcc-ng=12.2.0
- libgfortran-ng=12.2.0
- libgfortran5=12.2.0
- libgomp=12.2.0
- liblapack=3.9.0
- libnsl=2.0.0
- libopenblas=0.3.21
- libsqlite=3.40.0
- libstdcxx-ng=12.2.0
- libuuid=2.38.1
- libzlib=1.2.13
- markdown=3.3.7
- mergedeep=1.3.4
- mkdocs=1.4.3
- ncurses=6.3
- numexpr=2.8.4
- numpy=1.24.2
- openssl=3.1.1
- pip=23.0.1
- python=3.11.3
- python-dateutil=2.8.2
- python-tzdata=2023.3
- python_abi=3.11
- pytz=2023.3
- pyyaml=6.0
- pyyaml-env-tag=0.1
- readline=8.2
- setuptools=67.6.1
- six=1.16.0
- tk=8.6.12
- typing_extensions=4.6.3
- tzdata=2023c
- watchdog=3.0.0
- wheel=0.40.0
- xz=5.2.6
- yaml=0.2.5
- zipp=3.15.0
- pip:
- adjusttext==0.8
- aiohttp==3.8.4
- aiosignal==1.3.1
- alabaster==0.7.13
- alembic==1.10.3
- ansi2html==1.8.0
- asttokens==2.2.1
- async-timeout==4.0.2
- attrs==22.2.0
- babel==2.12.1
- backcall==0.2.0
- beautifulsoup4==4.12.2
- black==23.3.0
- blosc2==2.0.0
- brotlipy==0.7.0
- cachelib==0.9.0
- certifi==2022.12.7
- cffi==1.15.1
- charset-normalizer==3.1.0
- cloudpickle==2.2.1
- cmaes==0.9.1
- colorlog==6.7.0
- colorlover==0.3.0
- comm==0.1.3
- contourpy==1.0.7
- coverage==7.2.3
- coverage-badge==1.1.0
- cycler==0.11.0
- cython==0.29.34
- dash==2.11.1
- dash-bootstrap-components==1.4.1
- dash-core-components==2.0.0
- dash-extensions==0.1.3
- dash-html-components==2.0.0
- dash-table==5.0.0
- dash-tabulator==0.4.2
- dash-uploader==0.7.0a1
- dask==2023.7.0
- debugpy==1.6.7
- decorator==5.1.1
- dill==0.3.6
- diskcache==5.6.1
- dnspython==2.3.0
- docutils==0.18.1
- editorconfig==0.12.3
- email-validator==2.0.0.post2
- entrypoints==0.4
- et-xmlfile==1.1.0
- executing==1.2.0
- fastjsonschema==2.16.3
- flask==2.2.5
- flask-caching==1.10.1
- flask-login==0.6.2
- flask-migrate==4.0.4
- flask-sqlalchemy==3.0.5
- flask-wtf==1.1.1
- fonttools==4.39.3
- frozenlist==1.3.3
- fsspec==2023.6.0
- greenlet==2.0.2
- h5py==3.9.0
- hdf5plugin==4.1.3
- idna==3.4
- imagesize==1.4.1
- iniconfig==2.0.0
- ipyfilechooser==0.6.0
- ipykernel==6.22.0
- ipython==8.12.0
- ipywidgets==8.0.7
- itsdangerous==2.1.2
- jedi==0.17.2
- joblib==1.2.0
- jsbeautifier==1.14.8
- jsonschema==4.18.0
- jsonschema-specifications==2023.6.1
- jupyter-client==8.1.0
- jupyter-core==5.3.0
- jupyterlab-widgets==3.0.7
- kiwisolver==1.4.4
- locket==1.0.0
- lxml==4.9.3
- mako==1.2.4
- markdown-it-py==3.0.0
- markupsafe==2.1.2
- matplotlib==3.7.2
- matplotlib-inline==0.1.6
- matplotlib-venn==0.11.9
- mdit-py-plugins==0.4.0
- mdurl==0.1.2
- missingno==0.5.2
- mizani==0.9.0
- mkdocs-material==9.1.15
- mkdocs-material-extensions==1.1.1
- molmass==2023.4.10
- more-itertools==8.14.0
- ms-mint==0.2.3
- msgpack==1.0.5
- multidict==6.0.4
- multiprocess==0.70.14
- mypy-extensions==1.0.0
- myst-parser==2.0.0
- nbformat==5.9.1
- nest-asyncio==1.5.6
- openpyxl==3.1.2
- optuna==3.1.1
- packaging==21.3
- pandas==2.0.3
- parso==0.7.1
- partd==1.4.0
- pathspec==0.11.1
- patsy==0.5.3
- pexpect==4.8.0
- pickleshare==0.7.5
- pillow==10.0.0
- platformdirs==3.2.0
- plotly==5.15.0
- plotnine==0.10.1
- pluggy==1.0.0
- prompt-toolkit==3.0.38
- psims==1.2.5
- psutil==5.9.4
- psycopg2-binary==2.9.6
- ptyprocess==0.7.0
- pure-eval==0.2.2
- py-cpuinfo==9.0.0
- pyarrow==12.0.1
- pycparser==2.21
- pygments==2.15.1
- pymdown-extensions==10.0.1
- pymzml==2.5.2
- pyparsing==3.0.9
- pyrsistent==0.19.3
- pyteomics==4.6
- pytest==7.4.0
- pytest-cov==4.0.0
- pyzmq==25.0.2
- referencing==0.29.1
- regex==2023.3.23
- requests==2.28.2
- retrying==1.3.4
- rpds-py==0.8.10
- scikit-learn==1.3.0
- scipy==1.10.1
- seaborn==0.12.2
- sklearn==0.0.post4
- snowballstemmer==2.2.0
- soupsieve==2.4.1
- sphinx==6.2.1
- sphinx-rtd-theme==1.2.2
- sphinxcontrib-applehelp==1.0.4
- sphinxcontrib-devhelp==1.0.2
- sphinxcontrib-htmlhelp==2.0.1
- sphinxcontrib-jquery==4.1
- sphinxcontrib-jsmath==1.0.1
- sphinxcontrib-qthelp==1.0.3
- sphinxcontrib-serializinghtml==1.1.5
- sqlalchemy==2.0.9
- stack-data==0.6.2
- statsmodels==0.13.5
- tables==3.8.0
- tabulate==0.9.0
- tenacity==8.2.2
- threadpoolctl==3.1.0
- toolz==0.12.0
- tornado==6.2
- tqdm==4.65.0
- traitlets==5.9.0
- typing-extensions==4.5.0
- urllib3==1.26.16
- waitress==2.1.2
- wcwidth==0.2.6
- werkzeug==2.2.3
- wget==3.2
- widgetsnbextension==4.0.7
- wtforms==3.0.1
- xlsxwriter==3.1.2
- yarl==1.9.1
```
The app also takes notably more time to start. | 2hard
|
Title: Use Github actions for the CI
Body: | 1medium
|
Title: 绘制PR曲线
Body: 作者您好，我想借鉴您的ssd代码，现在有个难题就是您的论文中好像没有绘制P曲线或R曲线或者PR曲线，请问我该在validation.py中操作还是在plot_curve.py中操作呢？
或者不绘制曲线，单纯得到Recall和Precision的值也可以，我该怎么改呢？
感谢感谢 | 1medium
|
Title: Train using GPU, predict using CPU
Body: Hi everyone!
I'm new in deep learning, so sorry I ask a lot of questions. I opened this issue: https://github.com/deepmipt/DeepPavlov/issues/1212 and now I can use GPU to train BERT models. I'm building chatbot system and for this reason I have to load 4 trained models: intent classification, two ner models, and bert embedder. But I have several problems with it:
1) I want to train models one by one, but after first training, training process is still exist (nvidia-smi shows this). I trained 3 models this way: train model, then restart Jupyter's kernel (this clear training process from GPU) and then train another models the same way. But it is not flexible solution. I want to use .py file where all models will be trained. (when I use .py file also this problem)
2) Loading trained models using GPU to Jupyter Notebook make Jupyter's kernel dead. I try to load them using .py file, also have error. I think, that may be not enough memory, but It's strange. When I use CPU to train, there is no problem with memory, I can load all this models and use them. May be good way is to train model using GPU, and then make predictions using CPU, is it possible?
I feel that smth wrong with memory usage of GPU, but lack of experience in deep learning doesn't afford me to solve this by myself.
(p.s. возможно будет эффективней общаться на русском:) )
| 1medium
|
Title: consider splitting memory allocation and computation functions in the c_rewrite branch
Body: e.g. see: https://github.com/kwgoodman/bottleneck/blob/6a4a315f7c5426b8071903697e5e65e27d0c905d/bottleneck/src/move2_template.c#L27
If the split happens now, it will make it easy to parallelize these functions later. Also, it makes it easy to benchmark the computation part only, as you can avoid repeated memory allocation.
| 1medium
|
Title: Several Problems when using Keras Image Augmentation Layers to Augment Images and Masks for a Semantic Segmentation Dataset
Body: In the following, I will try to tell you a bit of my “story” about how I read through the various layers and (partly outdated) tutorials on image augmentation for semantic segmentation, what problems and bugs I encountered and how I was able to solve them, at least for my use case. I hope that I am reporting these issue(s) in the right place and that I can help the developers responsible with my “story”.
I am currently using the latest versions of Keras (3.8) and Tensorflow (2.18.0) on an Ubuntu 23.10 machine, but have also observed the issues with other setups.
My goal was to use some of the Image Augmentation Layers from Keras to augment the images AND masks in my semantic segmentation dataset. To do this I followed the [segmentation tutorial from Tensarflow](https://www.tensorflow.org/tutorials/images/segmentation) where a custom `Augment` class is used for augmentation.
This code works fine because the `Augment` class uses only a single augmentation layer. But, for my custom code I wanted to use more augmentations. Therefore, I "combined" several of the augmentation layers trying both the normal [Pipeline layer](https://keras.io/api/layers/preprocessing_layers/image_augmentation/pipeline/) but also my preferred [RandomAugmentationPipeline layer from keras_cv](https://github.com/keras-team/keras-cv/blob/master/keras_cv/src/layers/preprocessing/random_augmentation_pipeline.py).
I provide an example implementation in [this gist](https://colab.research.google.com/gist/RabJon/9f27d600301dcf6a734e8b663c802c7f/modification_of_segmentation.ipynb). The gist is a modification of the aforementioned [image segmentation tutorial from Tensorflow](https://www.tensorflow.org/tutorials/images/segmentation) where I intended to only change the `Augment` class slightly to fit my use case and to remove all the unnecessary parts. Unfortunately, I also had to touch the code that loads the dataset, because the dataset version used in the notebook was no longer supported. But, this is another issue.
Anyway, at the bottom of the notebook you can see some example images and masks from the data pipeline and if you inspect the in more detail you can see that for some examples images and masks do not match because they were augmented differently, which is obviously not the expected behaviour. You can also see that these mismatches already happen from the first epoch on. On my system with my custom use case (where I was using the RandomAugmentationPipeline layer) it happend only from the second epoch onward, which made debugging much more difficult.
I first assumed that it was one specific augmentation layer that caused the problems, but after trying them one-by-one I found out that it is the combination of layers that makes the problems. So, I started to think about possible solutions and I also tried to use the Sequential model from keras to combine the layers instead of the Pipeline layer, but the result remained the same.
I found that the random seed is the only parameter which can be used to "control and sync" the augmentation of the images and masks, but I already had ensured that I always used the same seed. So, I started digging into the source code of the different augmentation layers and I found that most of the ones that I was using implement a `transform_segmentation_masks()` method which is however not used if the layers are called as described in the tutorial. This is because, in order to enforce the call of this method, images and masks must be passed to the same augmentation layer object as a dictionary with the keys “images” and “segmentation_masks” instead of using two different augmentation layer objects for images and masks. However, I had not seen this type of call using a dictionary in any tutorial, neither for Tensorflow nor for Keras. Nevertheless, I decided to change my code to make use of the `transform_segmentation_masks()` method, as I hoped that if such a method already existed, it would also process the images and masks correctly and thus avoid the mismatches.
Unfortunately, this was not directly the case, because some of the augmentation layers changed the masks to float32 data types although the input was uint8. Even with layers such as “RandomFlip”, which should not change or interpolate the data at all, but only shift it. So, I had to wrap all layers again in a custom layer, which castes the data back to the input data type before returning it:
```python
class ApplyAndCastAugmenter(BaseImageAugmentationLayer):
def __init__(self, other_augmenter, **kwargs):
super().__init__(**kwargs)
self.other_augmenter = other_augmenter
def call(self, inputs):
output_dtypes = {}
is_dict = isinstance(inputs, dict)
if is_dict:
for key, value in inputs.items():
output_dtypes[key] = value.dtype
else:
output_dtypes = inputs.dtype
outputs = self.other_augmenter(inputs)
if is_dict:
for key in outputs.keys():
outputs[key] = keras.ops.cast(outputs[key], output_dtypes[key])
else:
outputs = keras.ops.cast(outputs, output_dtypes)
return outputs
```
With this (admittedly unpleasant) work-around, I was able to fix all the remaining problems. Images and masks finally matched perfectly after augmentation and this was immediately noticeable in a significant performance improvement in the training of my segmentation model.
For the future I would now like to see that wrapping with my custom `ApplyAndCastAugmenter` is no longer necessary but is handled directly by the `transform_segmentation_masks()` method and it would also be good to have a proper tutorial on image augmentation for semantic segmentation or to update the old tutorials. | 2hard
|
Title: poetry - PEP 735
Body: Aguardando novidades sobre a implementação da PEP 735. Isso alterará todos os diretórios (pyproject.toml) de código além do projeto principal.
Monitorar PR: https://github.com/python-poetry/poetry/pull/10130 | 1medium
|
Title: Should `pa.check_types` always do schema validation?
Body: #### Question about pandera
The behavior of `pa.check_types` is a little counter-intuitive because of this check:
```
if (
arg_value.pandera.schema is None
# don't re-validate a dataframe that contains the same exact
# schema
or arg_value.pandera.schema != schema # <<<<<<< not sure about this
):
try:
arg_value = schema.validate(
arg_value,
head,
tail,
sample,
random_state,
lazy,
inplace,
)
```
https://github.com/unionai-oss/pandera/blob/57d826956b88f89c18e794c728b7f5547ab4ecf8/pandera/decorators.py#L652
This allows errors like:
```python
def test_check_types_example_case():
class Schema(pa.DataFrameModel):
bid: pa.Int64
ask: pa.Int64
@pa.check_types
def calc_spread(df: DataFrame[Schema]) -> DataFrame[Schema]:
df["spread"] = df["ask"] - df["bid"]
return df
n = 42
rng = np.random.default_rng()
bids = rng.integers(0, 100, size=n, dtype=np.int64)
# This will call Schema.validate():
df = DataFrame[Schema](data={
"bid": bids,
"ask": bids + rng.integers(0, 500, size=n, dtype=np.int64),
})
# everything is as it should be...
print(df.dtypes)
# oops! schema silently violated
df["ask"] = df["ask"].astype(np.float64) # oops!
# scheme validation now fails:
with pytest.raises(pa.errors.SchemaError):
Schema.validate(df)
print(df.dtypes)
# I was expecting pa.check_types to catch the schema error
df = calc_spread(df)
print(df.dtypes)
```
I can resolve this by adding:
```
@pa.check_input(Schema.to_schema()) # check_output would also catch it in this case
@pa.check_types
def calc_spread(df: DataFrame[Schema]) -> DataFrame[Schema]:
# ...
```
Is this behavior intended? Or maybe the cached `arg_value.pandera.schema` should be getting invalidated when a column changes? | 1medium
|
Title: Add llama.cpp support to LLM
Body: Create a llama.cpp subclass for the LLM interface. When a GGUF extension is detected, it should automatically download the file from the HF hub and load it. The experience should be as seamless as other LLM models loaded through transformers. | 2hard
|
Title: Unittest failed after upgraded pydantic to 2.9.2
Body: **Describe the bug**
After upgraded pydantic to the latest version, unittest failed with the following message:
```
FAILED tests/contrib/test_pydantic.py::TestPydantic::test_address_schema - AssertionError: {'$de[312 chars]': {'$ref': '#/$defs/tortoise__contrib__pydant[2748 chars]ect'} != {'$de[312 chars]': {'allOf...
FAILED tests/contrib/test_pydantic.py::TestPydantic::test_event_schema - AssertionError: {'$de[2178 chars]': {'$ref': '#/$defs/tortoise__contrib__pydant[1071 chars]ect'} != {'$de[2178 chars]': {'all...
FAILED tests/contrib/test_pydantic.py::TestPydantic::test_eventlist_schema - AssertionError: {'$de[312 chars]': {'$ref': '#/$defs/tortoise__contrib__pydant[3079 chars]ray'} != {'$de[312 chars]': {'allOf...
FAILED tests/contrib/test_pydantic.py::TestPydantic::test_team_schema - AssertionError: {'$de[312 chars]': {'$ref': '#/$defs/tortoise__contrib__pydant[2809 chars]ect'} != {'$de[312 chars]': {'allOf...
4 failed, 1120 passed, 67 skipped, 4 xfailed, 1187 warnings in 113.37s (0:01:53)
make: *** [Makefile:44: test] Error 1
```
**To Reproduce**
```bash
git clone [email protected]:tortoise/tortoise-orm
cd tortoise-orm
poetry shell
make deps
pip install --upgrade pydantic
make test_sqlite
```
**Expected behavior**
Support pydantic2.9
| 1medium
|
Title: filterform still queries for last field of Basemodel
Body: ```
class FilterForm(pydantic.BaseModel):
city: str | None = Field(json_schema_extra={'search_url': '/api/forms/search', 'placeholder': 'Filter by City...'})
country: str | None = Field(json_schema_extra={'search_url': '/api/forms/search', 'placeholder': 'Filter by Country...'})
@router.get('/cities', response_model=FastUI, response_model_exclude_none=True)
def cities_view(page: int = 1, country: str | None = None, city: str | None = None) -> list[AnyComponent]:
print(city,country)
```
If I change this section of the demo , annotating city and country as optional. Then the selection in webserver queries for country still when clearing the form. Shouldn't go back to http://127.0.0.1:8000/table/cities instead ?
<img width="1355" alt="Screenshot 2024-03-01 at 11 10 54 PM" src="https://github.com/pydantic/FastUI/assets/30285349/50901223-60e2-4b02-af8d-36061e476aab">
<img width="1353" alt="Screenshot 2024-03-01 at 11 11 42 PM" src="https://github.com/pydantic/FastUI/assets/30285349/7e058395-5b6f-43eb-a8da-2d15f469616e">
| 1medium
|
Title: [FEEDBACK] Change "All data returned from APIs must be JSON serializable."
Body: Sometimes in russian Telegram chats I read "Django was sexy in 2008-2012, but it looks legacy and outdated in 2019 now...". Thank you for share your opinion, I completely agree with your suggestions. This is the best way to answer them "No, Django is modern framework for modern web. And there is the Guide how to cook Django. How to make Django Great Again".
Okay...
About APIs. Check this project. https://github.com/gluk-w/django-grpc/tree/master/tests Author tried to implement gRPC server with django project. Also https://github.com/grpc/grpc-web is stable now... So in my opinion line "All data returned from APIs must be JSON serializable." is incorrect. It's depends on API types... | 3misc
|
Title: Incredibly high map, are there potential bugs in evaluation codes?
Body: I have trained a video-based model which has a outstanding performance:
My training script is:
```shell
python main.py --app video\
-s dukemtmcvidreid\
-t dukemtmcvidreid\
-j 8 \
--height 256 \
--width 128 \
--optim amsgrad \
--label-smooth \
--lr 0.0003 \
--max-epoch 60 \
--stepsize 20 40 \
--fixbase-epoch 10 \
--open-layers classifier \
--save-dir log/pcb-Duke-Video-combined-freeze\
--root ../../dataset/DukeMTMC-VideoReID\
--batch-size 16\
--gpu-devices 0,1\
-a pcb_p6\
--train-sampler RandomIdentitySampler\
--loss triplet
```
And the performance imformation logged :
```
##### Evaluating dukemtmcvidreid (source) #####
Extracting features from query set ...
Done, obtained 702-by-12288 matrix
Extracting features from gallery set ...
Done, obtained 2636-by-12288 matrix
Speed: 0.0620 sec/batch
Computing CMC and mAP ...
** Results **
mAP: 90.0%
CMC curve
Rank-1 : 89.3%
Rank-5 : 97.4%
Rank-10 : 98.9%
Rank-20 : 99.3%
```
It's almost **SOTA**, I'm wondering whether there could be some bugs in evaluation.
Also, is it regular that mAP is higher than rank-1? | 2hard
|
Title: [BUG] Dashboard link on 404 page not working
Body: ### Description
The link back to the dashboard on the 404 page does not work. This happens since version 2.14.0, 2.13.5 was still fine.
### Steps to reproduce
1. Go to the 404 page /404
2. Click on the link that is supossed to take you back to the dashboard
3. Nothing happens
### Webserver logs
```bash
No related logs.
```
### Browser logs
```bash
```
### Paperless-ngx version
2.14.4
### Host OS
various
### Installation method
Docker - official image
### System status
```json
```
### Browser
Firefox / Google Chrome
### Configuration changes
_No response_
### Please confirm the following
- [x] I believe this issue is a bug that affects all users of Paperless-ngx, not something specific to my installation.
- [x] This issue is not about the OCR or archive creation of a specific file(s). Otherwise, please see above regarding OCR tools.
- [x] I have already searched for relevant existing issues and discussions before opening this report.
- [x] I have updated the title field above with a concise description. | 1medium
|
Title: Axis enhancements
Body: * revert axis checkbox
* some axis transformation (log, ln, exp...)
* `dx` and `dy` axis start and stop (like ranges) boxes?
* double axis in the same plot? | 1medium
|
Title: Generating ProfileReport from pickle files does not work (methods ProfileReport.dumps() and ProfileReport.loads())
Body: ### Current Behaviour
- Used all defaut values
- Generating ProfileReport from pd.DataFrame (Works) with ProfileReport(df) (**Works**)
- Saving ProfileReport locally as pickle file (Works) with ProfileReport.dumps() (**Works**)
- Loading ProfileReport from local pickle file with ProfileReport.loads() (**Works**)
- Getting the error when calling ProfileReport.to_file('report.html') function: ValueError: Can not describe a `lazy` ProfileReport without a DataFrame.
### Expected Behaviour
Expected a ProfileReport to be generated from loaded pickle file when invoking profile.to_file('report.html').
I expecte it to be possible to generate the ProfileReport from the loaded pickle file report without the original pd.DataFrame.
### Data Description
df = pd.DataFrame([['a', 'b'], ['c', 'd']],
index=['row 1', 'row 2'],
columns=['col 1', 'col 2'])
### Code that reproduces the bug
```Python
import pandas as pd
from pandas_profiling import ProfileReport
import pickle
# create dummy data as pd.DataFrame
df = pd.DataFrame([['a', 'b'], ['c', 'd']],
index=['row 1', 'row 2'],
columns=['col 1', 'col 2'])
# generate report from pd.DataFrame
profile = ProfileReport(df)
# save report locally as pickle file
_bytes = profile.dumps()
with open('report.pickle', 'wb') as f:
pickle.dump(_bytes, f, pickle.HIGHEST_PROTOCOL)
# create empty report object
profile = ProfileReport()
# load report pickle file from local
data = pickle.load(open('report.pickle', "rb"))
profile.loads(data)
#! generate report from loaded pickle file
profile.to_file('report.html')
```
### pandas-profiling version
v3.2.0
### Dependencies
```Text
pandas==1.3.5
pandas-profiling==3.2.0
```
### OS
Windows 10
### Checklist
- [X] There is not yet another bug report for this issue in the [issue tracker](https://github.com/ydataai/pandas-profiling/issues)
- [X] The problem is reproducible from this bug report. [This guide](http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) can help to craft a minimal bug report.
- [X] The issue has not been resolved by the entries listed under [Common Issues](https://pandas-profiling.ydata.ai/docs/master/pages/support_contrib/common_issues.html). | 1medium
|
Title: Functions not working due invalid URL formatting
Body: Hi all,
Calling functions using supabase-py for me allways results in the following response:
`{'data': b'Function not found', 'error': None}`
I believe this is due to the improper URL format in the `FunctionClient`:
https://github.com/supabase-community/supabase-py/blob/5c752443277de0a4a8dfd7d0d113f0d177efc81f/supabase/client.py#LL74C64-L74C64
Overwriting the `functions_url` in the following format seems to work for me: `"{}/rest/v1/rpc".format(url)` | 1medium
|
Title: jinja_loader type bug
Body: I have
```Python
from jinja2 import ChoiceLoader, FileSystemLoader
app.jinja_loader = ChoiceLoader([
FileSystemLoader(project_dir / 'templates/'),
app.jinja_loader,
])
```
There is a mypy error
```
error: Incompatible types in assignment (expression has type "ChoiceLoader", variable has type "FileSystemLoader | None") [assignment]
error: List item 1 has incompatible type "FileSystemLoader | None"; expected "BaseLoader" [list-item]
```
There shouldn't be an error.
Environment:
- Python version: 3.12.1
- Flask version: 3.0.1
| 1medium
|
Title: Tabulator with row_content : not toggling (collapse/expand) properly
Body: <!--
Thanks for contacting us! Please read and follow these instructions carefully, then you can delete this introductory text. Note that the issue tracker is NOT the place for usage questions and technical assistance; post those at [Discourse](https://discourse.holoviz.org) instead. Issues without the required information below may be closed immediately.
-->
### ALL software version info
```plaintext
Panel: 1.5.1
Python: 3.12.6
Quarto: 1.5.57
Jupyter: 5.7.2
```
</details>
### Description of expected behavior and the observed behavior
**Desired behaviors**
* Row contents - BUG - the row contents are not redrawn after first collapse, for example when toggled.
* Row contents - prefer that they be collapsed on start. Current default is expanded.
**Observed behaviors**
* row contents are expanded on start
( row contents are not displayed when triangle is toggled.
Here is a larger example of the issue in the wild. Here is a tabulator with row_content:
- [https://vcu-ssg.github.io/ssg-quarto-soc-analysis/31_explore.html](https://vcu-ssg.github.io/ssg-quarto-soc-analysis/31_explore.html)
I’d like the row_content to be collapsed on loading. An older version of panel had this default. Since upgrading the code my row_content is expanded on loading.
### Complete, minimal, self-contained example code that reproduces the issue
```python
import pandas as pd
import panel as pn
pn.extension('tabulator')
# sample dataframe
data = {'name': ['Alice', 'Bob', 'Charlie'],
'Value1': [10, 20, 30],
'Value2': [3, 2, 5],
}
df = pd.DataFrame(data)
# function to return row contents
def content_fn( row ):
return pn.pane.Markdown(
f'# Row contents\n name: {row["name"]}',
sizing_mode='stretch_width'
)
# tabulator object with row_contents
sample_table = pn.widgets.Tabulator(
df, height=350,
layout='fit_columns',
sizing_mode='stretch_width',
row_content=content_fn,
embed_content=True,
expanded = []
)
sample_table
```
| 1medium
|
Title: Focus changes from the control to the entire browser when working with Google Chrome, Yandex Browser.
Body: ## Problem
I'm trying to get information about a control by mouse click. In Mozilla Firefox I get full information about the cotrol, but in Google Chrome and Yandex Browser I get only information about the window. When using inspect.exe, the cursor can't point to the control either, but the tree structure has this control. Adding the flag --force-renderer-accessibility to Сhrome doesn't change anything.
## Short Example of Code to Demonstrate the Problem
```
from ctypes.wintypes import tagPOINT
import pywinauto
x, y = 100, 100 # cursor coordinates
elem = pywinauto.uia_defines.IUIA().iuia.ElementFromPoint(tagPOINT(x, y))
element = pywinauto.uia_element_info.UIAElementInfo(elem)
wrapper = pywinauto.controls.uiawrapper.UIAWrapper(element)
```
## Expected Behavior
The wrapper variable stores the wrapper of the control, located at specific coordinates.
If you hover the cursor over a control in inspect.exe, the control is highlighted.
## Actual Behavior
The wrapper variable stores the control's wrapper, located at specific coordinates for Mozilla Firefox. The wrapper variable stores the dialog window control wrapper for Google Chrome and Yandex Browser.
If you put the cursor on the control in inspect.exe, the control is highlighted in Mozilla Firefox. If you hover your cursor over a control in inspect.exe, the dialog window in Google Chrome and Yandex Browser is highlighted.
## Steps to Reproduce the Problem
1. Open Google Chrome, Mozilla Firefox
2. Open in browsers google.com
3. Open inspect.exe, and place the cursor on the Google icon in browsers.
4. Different areas will be highlighted and different data will be in inspect.exe in different browsers. Move the cursor around the screen and hover over the Google icon again several times. In this case, the behavior will be different.
## Specifications
- Pywinauto version: 0.6.8
- Python version and bitness: 3.8.5 and 64
- Platform and OS: Windows10
| 2hard
|
Title: HPO: gpu_metrics file does not exist!
Body: **Describe the issue**:
I run`nnictl create --config exp.yaml --port 9090` and cheak `nvidia-smi`, find its running without using GPU.
So I run`nnictl create --config exp.yaml --port 9090 - f` and get error below:
I have followed tips in https://github.com/microsoft/nni/issues/4226
`METRIC_OUTPUT_DIR=${scriptFolder} python3 -m nni.tools.gpu_tool.gpu_metrics_collector` works under my path.
**Environment**:
- NNI version: 2.6
- Training service (local|remote|pai|aml|etc): local
- Client OS: Ubuntu 18.04
- Server OS (for remote mode only): \
- Python version: py3.7
- PyTorch/TensorFlow version: pytorch 1.10.2
- Is conda/virtualenv/venv used?: Y
- Is running in Docker?: N
**Configuration**:
- Experiment config (remember to remove secrets!):
- Search space:
**Log message**:
- nnimanager.log:
Dont exist
- dispatcher.log:
[2022-04-20 20:12:10] INFO (nni.experiment/MainThread) Creating experiment, Experiment ID: 30pecn7s
[2022-04-20 20:12:10] INFO (nni.experiment/MainThread) Starting web server...
[2022-04-20 20:12:11] WARNING (nni.experiment/MainThread) Timeout, retry...
[2022-04-20 20:12:12] INFO (nni.experiment/MainThread) Setting up...
[2022-04-20 20:12:12] INFO (nni.experiment/MainThread) Web UI URLs: http://127.0.0.1:9090 http://172.17.7.99:9090 http://172.18.0.1:9090 http://172.19.0.1:9090 http://172.20.0.1:9090
[2022-04-20 20:12:12] INFO (nni.tools.nnictl.launcher/MainThread) To stop experiment run "nnictl stop 30pecn7s" or "nnictl stop --all"
[2022-04-20 20:12:12] INFO (nni.tools.nnictl.launcher/MainThread) Reference: https://nni.readthedocs.io/en/stable/Tutorial/Nnictl.html
[2022-04-20 20:12:12] INFO (nni.experiment/MainThread) Stopping experiment, please wait...
[2022-04-20 20:12:12] INFO (nni.tuner.tpe/MainThread) Using random seed 1504156515
[2022-04-20 20:12:12] INFO (nni.runtime.msg_dispatcher_base/MainThread) Dispatcher started
[2022-04-20 20:12:12] INFO (nni.runtime.msg_dispatcher_base/MainThread) Dispatcher exiting...
[2022-04-20 20:12:15] INFO (nni.runtime.msg_dispatcher_base/MainThread) Dispatcher terminiated
- nnictl stdout and stderr:
Dont exist
**How to reproduce it?**:
nnictl create --config exp.yaml --port 9090 - f
| 1medium
|
Title: Moderator Panel
Body: It would be nice to have an Moderator Panel. We could just rename the current "Admin Settings" to something like "Management" and display the settings depending on the permissions --> If a user has Admin permissions he can see everything but if he only has Moderator permissions he can only see the Moderation Settings/Dashboard/whatever
| 1medium
|
Title: [Q] MultiThreadMapData and AugmentImageComponents?
Body: I read the tutorial "Efficient DataFlow", which was very helpful.
I have a question. The tutorial includes two cases.
**multiprocess**
```
ds0 = dataset.ILSVRC12('/path/to/ILSVRC12', 'train', shuffle=True)
ds1 = AugmentImageComponent(ds0, lots_of_augmentors)
ds = PrefetchDataZMQ(ds1, nr_proc=25)
ds = BatchData(ds, 256)
```
**multithread**
```
ds0 = dataset.ILSVRC12('/path/to/ILSVRC12', 'train', shuffle=True)
augmentor = AugmentorList(lots_of_augmentors)
ds1 = MultiThreadMapData(ds0, nr_thread=25,
map_func=lambda dp: [augmentor.augment(dp[0]), dp[1]], buffer_size=1000)
# ds1 = PrefetchDataZMQ(ds1, nr_proc=1)
ds = BatchData(ds1, 256)
```
{`AugmentImageComponent`, `PrefetchDataZMQ`} vs. {`AugmentorList`, `MultiThreadMapData`}.
So, if I have to use `AugmentImageComponents` (because it has `index=(0, 1)` argument so I can apply same augmentations to images and labels equally), then is there no way to use `MultiThreadMapData`?
An example I can think of is something like `map_func=[augmentor.augment(dp[0]), augmentor.augment(dp[1])]`, but then, I imagine two different augment will apply to dp[0] and dp[1] respectively (due to random factors).
I'm curious if one can use a `multithread` way when using `AugmentImageComponent(ds, augmentors, (0, 1))`.
| 1medium
|
Title: Autocomplete path portion in URL bar
Body: Currently, URL bar supports autocomplete for base URLs (https://domain.io) and variables. Please support path segments also.
For example, I'm playing with [Gmail API](https://developers.google.com/gmail/api/reference/rest), which has a URL like this:
```
https://gmail.googleapis.com/gmail/v1/users/{userId}/messages
```
I want that after I type "https://gmail.googleapis.com/", Posting suggests "gmail", after I accept this suggestion and type "/", Posting continue to suggest "v1" and so on.
The path portions are from the collection. | 1medium
|
Title: Load full native fastText Facebook model is partial
Body: #### Problem description
Hidden vectors are bad. I'm using the gensim.models.fasttext.load_facebook_model function to load the .bin file, but the syn1 fails loading. Also trainables.syn1neg is full of zeros.
'FastTextTrainables' object has no attribute 'syn1'
#### Steps/code/corpus to reproduce
Simply using `ft = gensim.models.fasttext.load_facebook_model(fname)` on Facebook's model.
Then `ft.syn1` or `ft.trainables.syn1neg` which returns the zero array.
#### Versions
Please provide the output of:
Windows-2012ServerR2-6.3.9600-SP0
Python 3.7.1 (default, Dec 10 2018, 22:54:23) [MSC v.1915 64 bit (AMD64)]
Bits 64
NumPy 1.18.3
SciPy 1.4.1
gensim 3.8.3
FAST_VERSION 0
| 1medium
|
Title: Update exception message for `insert` function
Body: | 1medium
|
Title: Support for HTTP 308
Body: Currently, support for HTTP 308 Permanent Redirect is missing.
From https://httpstatuses.com/308 :
> The target resource has been assigned a new permanent URI and any future references to this resource ought to use one of the enclosed URIs.
>
> Clients with link editing capabilities ought to automatically re-link references to the effective request URI to one or more of the new references sent by the server, where possible.
>
> The server SHOULD generate a Location header field2 in the response containing a preferred URI reference for the new permanent URI. The user agent MAY use the Location field value for automatic redirection. The server's response payload usually contains a short hypertext note with a hyperlink to the new URI(s).
>
> A 308 response is cacheable by default; i.e., unless otherwise indicated by the method definition or explicit cache controls3.
>
> Note: This status code is similar to 301 Moved Permanently, except that it does not allow changing the request method from POST to GET.
Relevant RFC: https://tools.ietf.org/html/rfc7538#section-3 | 1medium
|
Title: compare_intra() keeps giving 'index X is out of bounds for axis 0 with size Y' error
Body: Tried `compare_intra()` on a road accident data set to compare urban vs rural samples. However, it keeps giving the above error.
`report = sv.compare_intra(df, df["UrbanRural"] == 1, ["Urban", "Rural"], 'Is Fatal')`
Removed from `df` up to four fields that the error is reported on, but then the next field throws the error. Unfortunately, the data set is private so can't share, but could this be an issue with the code itself? | 1medium
|
Title: Concurrent editing notifications - backend additions to identify concurrent edits
Body: Part of [RFC 95 - concurrent editing notifications](https://github.com/wagtail/rfcs/pull/95)
As per the RFC section 'Notifying users on changes', add support to the 'ping' endpoint for passing a revision ID so that any users who have created revisions newer than that one are annotated as part of the response, including users who do not have an active editing session open.
(TBC: does this backend code need to know about the list of dismissed revision IDs, or is this entirely a front-end detail? If the former, that should be covered as part of this issue.)
### Working on this
To be implemented by the Wagtail product team at Torchbox - we are not seeking external contributors for this. | 1medium
|
Title: [Question]How to set width & height parameter when you use pygwalker on flask app?
Body: hello.I use pygwalker on flask app. but display size is a little big, so I would like to resize it.
Though I tried width & height parameter to pyg.walk function, it didn't work. Is there any way to resize it?
Code)
df = pd.read_sql(sql,engine)
# Get Pygwalker as html
html_str = pyg.walk(df, return_html=True, show_cloud_tool=False, dark='dark', width=800,height=400,scrolling=True)
return templates.TemplateResponse("pygwalker.html",{"request": request, "html_str": html_str}) | 1medium
|
Title: Unexpected Negative Coordinates in YOLOv8 Annotations after Transformations
Body: **Description:**
I am encountering an issue while applying transformations to YOLOv8 annotations (object detection format) using albumentations. Below is a minimal reproducible example
```
import cv2
import albumentations as A
image = cv2.imread("....")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
bboxes = [[0.231627, 0.014903, 0.047738, 0.029807]]
category_ids = [0]
category_id_to_name = {0: 'part'}
transform = A.Compose(
[A.HorizontalFlip(p=0.5)], # Horizontal flip transformation
bbox_params=A.BboxParams(format='yolo', label_fields=['category_ids'])
)
transformed = transform(image=image, bboxes=bboxes, category_ids=category_ids)
```
**Error:**
When applying different transformations (even an empty one), I consistently receive the following error
```
ValueError: Expected y_min for bbox [ 2.0775801e-01 -5.0012022e-07 2.5549600e-01 2.9806498e-02 0.0000000e+00] to be in the range [0.0, 1.0], got -5.00120222568512e-07.
```
**Observations:**
Input image shape (672, 504, 3)
The original bounding box coordinates are well within the valid range [0.0, 1.0].
Drawing the bounding boxes on the original image using a custom function shows correct placement and dimensions.
The error consistently produces the same invalid numbers:
```
[2.0775801e-01, -5.0012022e-07, 2.5549600e-01, 2.9806498e-02, 0.0000000e+00]
```
**Custom Function for Verification:**
Below is the custom function I used to verify the correctness of the bounding boxes by visualizing them on the image:
```
import cv2
def draw_bboxes(image_path, annotations_path):
image = cv2.imread(image_path)
height, width, _ = image.shape
with open(annotations_path, 'r') as f:
annotations = f.readlines()
_, x_center, y_center, bbox_width, bbox_height = map(float, annotation.strip().split())
x_center = int(x_center * width)
y_center = int(y_center * height)
bbox_width = int(bbox_width * width)
bbox_height = int(bbox_height * height)
x1 = int(x_center - bbox_width / 2)
y1 = int(y_center - bbox_height / 2)
x2 = int(x_center + bbox_width / 2)
y2 = int(y_center + bbox_height / 2)
cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), 2) # Green, line thickness = 2
# Save the result
cv2.imwrite('output_image.jpg', image)
```
**Notes:**
The bounding boxes drawn using this function align correctly with the objects in the image.
The issue seems to be related to how albumentations processes the YOLO format during transformations.
**Questions:**
Where do the negative y_min values originate, given that the original annotations are valid?
Is this a bug in the library, or is there a mistake in the way I define or pass the annotations?
Any help in resolving this would be greatly appreciated!
| 1medium
|
Title: Make requests over Tor. Following error. OSError: 'tor' isn't available on your system. Maybe it's not in your PATH?
Body: <!--
######################################################################
WARNING!
IGNORING THE FOLLOWING TEMPLATE WILL RESULT IN ISSUE CLOSED AS INCOMPLETE.
######################################################################
-->
## Checklist
<!--
Put x into all boxes (like this [x]) once you have completed what they say.
Make sure complete everything in the checklist.
-->
- [x] I'm asking a question regarding Sherlock
- [x] My question is not a tech support question.
**We are not your tech support**.
If you have questions related to `pip`, `git`, or something that is not related to Sherlock, please ask them on [Stack Overflow](https://stackoverflow.com/) or [r/learnpython](https://www.reddit.com/r/learnpython/)
## Question
Hi, I've started to use Sherlock and I encounter the following message while trying to make requests over Tor with the argument --tor:
raise OSError("'%s' isn't available on your system. Maybe it's not in your PATH?" % tor_cmd)
OSError: 'tor' isn't available on your system. Maybe it's not in your PATH?
I'm running Sherlock on Windows 11 and the Python version used is 3.12.
| 1medium
|
Title: Test on multiple GPu
Body: I have been digging through the issues and could not find the answer. Does this code work for multiple GPUs when testing? specifically the video code in the sample folder. I am trying to see how to increase the speed of that. | 1medium
|
Title: Tutorial Documentation Issue with --debug
Body: # Recap
[Run Application](https://flask.palletsprojects.com/en/2.2.x/tutorial/factory/#run-the-application) section says you should run: `flask --app flaskr run --debug`
This provides this error:
```
(Discord) C:\Users\gradu\code\flask-tutorial>flask --app flaskr run --debug
Usage: flask run [OPTIONS]
Try 'flask run --help' for help.
Error: No such option: --debug (Possible options: --debugger, --no-debugger)
```
# Replication
Follow the tutorial.
# Resolution
The following should be run for it to work since `--debug` should be before `run`:
`flask --app flaskr --debug run`
Environment:
- Python version: 3.10
- Flask version: 2.2.2
| 0easy
|
Title: Is it possible to add multiple images in a folium popup?
Body: I tried to add an image to the marker popup in folium and was successful. Following is the code snippet:
```
encoded = base64.b64encode(open('test.png', 'rb').read())
html = ''' <img src="data:image/png;base64,{}">'''.format
iframe = IFrame(html(encoded.decode('UTF-8')), width=400, height=350)
popup = folium.Popup(iframe, max_width=400)
folium.Marker(location,tooltip=html1, popup=popup,icon=folium.Icon(color='red'')).add_to(feature_group)
```
Is there a way to add multiple images in the popup like this??
| 1medium
|
Title: Key Error (trying to fetch out of range data)
Body: File ~/jupyter_dir/lib/python3.9/site-packages/pandas/core/indexes/base.py:3623, in Index.get_loc(self, key, method, tolerance)
3621 return self._engine.get_loc(casted_key)
3622 except KeyError as err:
-> 3623 raise KeyError(key) from err
3624 except TypeError:
3625 # If we have a listlike key, _check_indexing_error will raise
3626 # InvalidIndexError. Otherwise we fall through and re-raise
3627 # the TypeError.
3628 self._check_indexing_error(key)
KeyError: 'Date'
INTC 1900-01-01 through 1980-03-16, for example.
There is no data for that period. We should get an empty string and catch the error on a try-except routine. Pandas try to read nonexistent data and crashes.
Not a big issue but when trying to release the internet from unnecessary traffic it should count.
In my case, I keep some data for research and only try to fetch whatever data is not in the range already on file.
Best,
Laurentino | 1medium
|
Title: 获取帐号太频繁了,可以调整拉取时间吗?
Body: <img width="1265" alt="image" src="https://github.com/vvbbnn00/WARP-Clash-API/assets/19187969/f888f5ce-56e7-4379-aee4-bf678d059c14">
| 0easy
|
Title: value_counts with NaN sometimes raises ValueError: No objects to concatenate
Body: <!-- Please include a self-contained copy-pastable example that generates the issue if possible.
Please be concise with code posted. See guidelines below on how to provide a good bug report:
- Craft Minimal Bug Reports http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports
- Minimal Complete Verifiable Examples https://stackoverflow.com/help/mcve
Bug reports that follow these guidelines are easier to diagnose, and so are often handled much more quickly.
-->
**Describe the issue**:
Context: We rely on Dask for processing large amounts of time series data collected at our production machines in a proprietary file format. Thanks for making this possible due to the flexibility of Dask! We do a first selection of data by a time range. If a channel/signal is not available for the whole time period, part of the time range is filled with NaNs. Since version 2024.3.0 an error is raised if we try to calculate `value_counts` of such a data structure. See the MCVE below.
**Minimal Complete Verifiable Example**:
```python
import dask.dataframe as dd
import numpy as np
import pandas as pd
size = 500_000
na_size = 400_000
npartitions = 10
df = pd.DataFrame(
{
'A': np.random.randint(0, 2, size=size, dtype=bool),
'B': np.append(np.nan * np.zeros(na_size), np.random.randn(size - na_size)),
}
)
ddf = dd.from_pandas(df, npartitions=npartitions)
ddf.groupby('A')['B'].value_counts().compute()
```
raises the following error
```python
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[3], line 12
5 df = pd.DataFrame(
6 {
7 'A': np.random.randint(0, 2, size=size, dtype=bool),
8 'B': np.append(np.nan * np.zeros(na_size), np.random.randn(size - na_size)),
9 }
10 )
11 ddf = dd.from_pandas(df, npartitions=npartitions)
---> 12 ddf.groupby('A')['B'].value_counts().compute()
File ~\Python\Lib\site-packages\dask_expr\_collection.py:453, in FrameBase.compute(self, fuse, **kwargs)
451 out = out.repartition(npartitions=1)
452 out = out.optimize(fuse=fuse)
--> 453 return DaskMethodsMixin.compute(out, **kwargs)
File ~\Python\Lib\site-packages\dask\base.py:375, in DaskMethodsMixin.compute(self, **kwargs)
351 def compute(self, **kwargs):
352 """Compute this dask collection
353
354 This turns a lazy Dask collection into its in-memory equivalent.
(...)
373 dask.compute
374 """
--> 375 (result,) = compute(self, traverse=False, **kwargs)
376 return result
File ~\Python\Lib\site-packages\dask\base.py:661, in compute(traverse, optimize_graph, scheduler, get, *args, **kwargs)
658 postcomputes.append(x.__dask_postcompute__())
660 with shorten_traceback():
--> 661 results = schedule(dsk, keys, **kwargs)
663 return repack([f(r, *a) for r, (f, a) in zip(results, postcomputes)])
File ~\Python\Lib\site-packages\dask_expr\_groupby.py:292, in SingleAggregation.aggregate(cls, inputs, **kwargs)
290 @classmethod
291 def aggregate(cls, inputs, **kwargs):
--> 292 return _groupby_aggregate(_concat(inputs), **kwargs)
File ~\Python\Lib\site-packages\dask\dataframe\groupby.py:436, in _groupby_aggregate(df, aggfunc, levels, dropna, sort, observed, **kwargs)
433 # we emit a warning earlier in stack about default numeric_only being deprecated,
434 # so there's no need to propagate the warning that pandas emits as well
435 with check_numeric_only_deprecation():
--> 436 return aggfunc(grouped, **kwargs)
File ~\Python\Lib\site-packages\dask\dataframe\groupby.py:3219, in _value_counts_aggregate(series_gb)
3217 def _value_counts_aggregate(series_gb):
3218 data = {k: v.groupby(level=-1).sum() for k, v in series_gb}
-> 3219 res = pd.concat(data, names=series_gb.obj.index.names)
3220 typed_levels = {
3221 i: res.index.levels[i].astype(series_gb.obj.index.levels[i].dtype)
3222 for i in range(len(res.index.levels))
3223 }
3224 res.index = res.index.set_levels(
3225 typed_levels.values(), level=typed_levels.keys(), verify_integrity=False
3226 )
File ~\Python\Lib\site-packages\pandas\core\reshape\concat.py:507, in _Concatenator._clean_keys_and_objs(self, objs, keys)
504 objs_list = list(objs)
506 if len(objs_list) == 0:
--> 507 raise ValueError("No objects to concatenate")
509 if keys is None:
510 objs_list = list(com.not_none(*objs_list))
ValueError: No objects to concatenate
```
Now comes the fun part, if you adjust `size`, `na_size` and `npartitions` you may get working code (for example `size = 500_000`, `na_size = 400_000` and `npartitions = 9`).
**Anything else we need to know?**:
At I first I thought it may be related to the new default of [query-planning](https://docs.dask.org/en/stable/changelog.html#query-planning), however setting
```python
import dask
dask.config.set({'dataframe.query-planning': False})
```
leads to the same error with a different stacktrace.
**Environment**:
- Dask version: 2024.3.1 (dask-expr 1.0.4)
- Python version: 3.11.8
- Operating System: Windows 10 (22H2)
- Install method (conda, pip, source): conda
| 1medium
|
Title: Best way to decode (translate) a hundred thousand files?
Body: I'm currently looping through each text file and running `t2t-decoder` to translate into my target language.
It appears loading my model per each file is taking a lot more time than necessary. What is the best way to go about this?
I have considered batching all my text files into one big text file for inference, but it doesn't seem to be a good idea as some of the text files need to fail appropriately as they contain errors from pre-processing (long story, don't ask me why.) | 1medium
|
Title: Fix deprecations in SoftCosineSimilarity
Body: When running our CI test suite, I see an array of deprecation warnings:
https://dev.azure.com/rare-technologies/gensim-ci/_build/results?buildId=287&view=logs&jobId=f9575ddc-dec8-54e6-9d26-abb8bdd9bed7&j=f9575ddc-dec8-54e6-9d26-abb8bdd9bed7&t=180156a9-2bf9-537d-c84a-ef9e808c0367
Some are from gensim, some from scipy:
<img width="1440" alt="Screen Shot 2020-09-09 at 09 16 00" src="https://user-images.githubusercontent.com/610412/92567026-635b8f00-f27d-11ea-9d5f-c56f3ba7c08b.png">
@Witiko could you please have a look? Is it something your existing PR already addresses?
If not, can you please fix those? Thanks. | 1medium
|
Title: Regarding training images
Body: Hi,
Please let me the format of the training images. can we have the training images in .jpg format?
Thanks
| 0easy
|
Title: Avoid Status Spam/Visual Noise
Body: Regardless of how anyone may feel about the subjectivity of this "feature", previously, setting an empty motd URL kept this sort of status spam from appearing during startup. However, at some point after updating to a newer version of visidata, this status message is now very pronounced and distracting, and disabling the MOTD doesn't disable the status display either.
This, coupled with the default help status overlay (which also was not a default behavior previously), leads to a very distracting user experience. Thankfully, it's possible to disable the help overlay at least with config overrides, but there doesn't seem to be a way to disable the default status upon startup.
I suggest removing this line, or add a "quiet" option to skip this status display:
https://github.com/saulpw/visidata/blob/develop/visidata/main.py#L180
<img width="1040" alt="Screenshot 2024-01-31 at 14 43 52" src="https://github.com/saulpw/visidata/assets/108159757/a9379613-5b2f-4998-b869-49e93e9d118c">
I would like for visidata to "stay out of my way" as it did in previous versions. | 0easy
|
Title: Proposing a PR to fix a few small typos
Body: # Issue Type
[x] Bug (Typo)
# Steps to Replicate and Expected Behaviour
- Examine bowtie/tests/test_components.py and observe `instatiation`, however expect to see `instantiation`.
- Examine bowtie/tests/test_tags.py and observe `instantation`, however expect to see `instantiation`.
# Notes
Semi-automated issue generated by
https://github.com/timgates42/meticulous/blob/master/docs/NOTE.md
To avoid wasting CI processing resources a branch with the fix has been
prepared but a pull request has not yet been created. A pull request fixing
the issue can be prepared from the link below, feel free to create it or
request @timgates42 create the PR. Alternatively if the fix is undesired please
close the issue with a small comment about the reasoning.
https://github.com/timgates42/bowtie/pull/new/bugfix_typos
Thanks.
| 0easy
|
Title: Hook for replacing user avatar logic
Body: ### Is your proposal related to a problem?
From @jhrr on https://github.com/wagtail/wagtail/issues/12274#issuecomment-2514281305:
> Sorry to mildly necrobump this thread, but there is one issue with cutomizing the Wagtail `UserProfile` that we've encountered, and that is being able to replace or alias the `avatar` field on the `UserProfile` with some other avatar on some other model so it could use that instead.
For example, we have a custom `User` model and a `Profile` model, and we keep the avatar on the `Profile`. We'd prefer to just be able to use the avatar from the `Profile` everywhere, across all sub-domains/contexts, including inside the CMS itself. Is there any way we can achieve this currently?
### Describe the solution you'd like
As per https://github.com/wagtail/wagtail/issues/12274#issuecomment-2517181624 - introduce a new `get_avatar_url` hook which is called by the `{% avatar_url %}` tag in wagtailadmin_tags.py, passing the user object and requested size. If any hook returns a non-empty response, return that in preference to the standard avatar URL logic.
The change would be [to this function](https://github.com/wagtail/wagtail/blob/23275a4cef4d36bb311abe315eb9ddfc43868e8b/wagtail/admin/templatetags/wagtailadmin_tags.py#L652-L659), and would look something like (untested):
```python
@register.simple_tag
def avatar_url(user, size=50, gravatar_only=False):
"""
A template tag that receives a user and size and return
the appropriate avatar url for that user.
Example usage: {% avatar_url request.user 50 %}
"""
for hook_fn in hooks.get_hooks("get_avatar_url"):
url = hook_fn(user, size)
if url:
return url
# followed by the existing code
```
This would need to be accompanied by unit tests - I'd suggest adding a hook function in `wagtail/test/testapp/wagtail_hooks.py` that returns a custom URL when some specific condition is true, such as the username containing "fred", and adding tests to confirm that both the custom code and the default avatar logic come into play at the appropriate times. The hook will also need to be documented in the [hooks reference docs](https://github.com/wagtail/wagtail/blob/main/docs/reference/hooks.md), and ideally we'd mention it in the [customising admin templates](https://github.com/wagtail/wagtail/blob/main/docs/advanced_topics/customization/admin_templates.md) docs too - this _could_ be a detailed howto, but I expect we can come up with a simple self-contained example snippet on the hooks reference page that's not too long, and just link to that.
### Describe alternatives you've considered
On #12274 we considered:
* making it possible to customise the value returned for `avatar` on `UserProfile` (too invasive)
* overriding the wagtailadmin/shared/avatar.html template (too much copy-and-paste, and locks us in to the current template tag approach)
* overriding `avatar_url` in the wagtailadmin_tags tag registry (likewise, locks us in to the template tag approach)
### Working on this
@jhrr has first dibs on this, but anyone else is welcome to pick it up :-) | 1medium
|
Title: refresh data with zoom in/zoom out
Body: when I changed to st_folium everytime I zoom in/zoom out all data uploads again and it takes a lot of time. When I use folium_static I don't have this problem. | 1medium
|
Title: How can we support text_to_instance running in parallel?
Body: Though we have a multi-process data-loader, we use for loops in [predictor](https://github.com/allenai/allennlp/blob/main/allennlp/predictors/predictor.py#L299).
It will make the process hard and slow when we have many inputs( especially when this predictor as a server).
I mean can we use or add some method to make this call(`text_to_instance`) support multi-process ?
@epwalsh @dirkgr maybe you could help ?
| 1medium
|
Title: Website detects undetected bot
Body: I have been trying to log in on a website but it keeps detecting me. This is what I do:
I create a driver and open google with it. From there I log in manually to my google account so that I have cookies on the profile. Next step I go to the website where I want to log in, but whenever i try logging in it detects me as a bot. Besides creating the chrome instance everything is manually.
I tried doing the exact same on my actual chrome and there I can log in without a problem. I also changed my IP and I still encounter detection. It seems like it knows i spun up an instance with seleniumbase but how is that possible?
Things i tried:
Remove the user_dir folder,
Change user-agent,
Change IP,
Created new google profile with cookies,
Checked recaptcha score which was 0.9.
If anyone has any suggestions I would love to hear them! | 2hard
|
Title: Invalid SQLAlchemy Model
Body: Hey guys I hope someone can help me on this, i'm really stuck....
database automaping:
```
engine = create_engine(" blablabla", convert_unicode=True)
db_session = scoped_session(sessionmaker(autocommit=False,
autoflush=False,
bind=engine))
Base = automap_base()
Base.prepare(engine, reflect=True, generate_relationship=_gen_relationship, name_for_scalar_relationship = name_for_scalar_relationship, classname_for_table=camelize_classname, name_for_collection_relationship=pluralize_collection)
Buyer = Base.classes.Buyer
Base.query = db_session.query_property()
```
schema :
```
import graphene
import json
from graphene import relay
from graphene_sqlalchemy import SQLAlchemyConnectionField, SQLAlchemyObjectType
from database import Buyer as BuyerModel
class Buyer(SQLAlchemyObjectType):
class Meta:
model = BuyerModel
interfaces = (relay.Node, )
class Query(graphene.ObjectType):
node = relay.Node.Field()
all_buyers = SQLAlchemyConnectionField(Buyer)
schema = graphene.Schema(query=Query)
```
Produces the error :
`AssertionError: You need to pass a valid SQLAlchemy Model in Buyer.Meta, received "<class 'sqlalchemy.ext.automap.Buyer'>".`
| 1medium
|
Title: v3.2.3 GitHub release missing binary and package
Body: ## Checklist
- [x] I've searched for similar issues.
---
The 3.2.3 release https://github.com/httpie/cli/releases/tag/3.2.3 is missing the binary and .deb package | 1medium
|
Title: render_item method in env.py is not being called
Body: There's an issue with the `sqlalchemy_utils` package. Special field types like UUID, Password from this library are not being recognized by alembic. There was a workaround in earlier versions of `flask-migrate` and that is to write custom `render_item` method in `env.py`:
```python
def render_item(type_, obj, autogen_context):
"""Apply custom rendering for selected items."""
if type_ == 'type' and isinstance(obj, sqlalchemy_utils.types.uuid.UUIDType):
# add import for this type
autogen_context.imports.add("import sqlalchemy_utils")
autogen_context.imports.add("import uuid")
return "sqlalchemy_utils.types.uuid.UUIDType(), default=uuid.uuid4"
# default rendering for other objects
return False
```
This was resulting in versions being correct, e.g. sqlalchemy_utils imported and `Column type` is correct.
I upgraded to the latest `flask-migrate` from `2.3.1` and this workaround no longer works. Seems like the `render_item` method is not called at all.
| 1medium
|
Title: consider moving to poetry from flit
Body: Advantages:
1. Automatic python dependency management.
2. Consolidate requirements into one file.
Disadvantages:
1. Learn a new tool, (leaving flit).
https://github.com/sdispater/poetry | 1medium
|
Title: Leafmap and streamlit cannot use interactive vector tools to obtain coordinate values
Body: <!-- Please search existing issues to avoid creating duplicates. -->
### Environment Information
- leafmap version:0.18.8
- Python version:3.9.16
- Operating System:windows
### Description
Leafmap and streamlit cannot use the interactive vector tool to obtain coordinate values. The possible reason is that the program will refresh and run as soon as the button is clicked.
### What I Did
```
import os
import streamlit as st
import leafmap as leafmap
st.set_page_config(layout="wide")
Map = leafmap.Map()
col1, col2 = st.columns([4, 1])
with col2:
addButton3=st.button('get range')
if addButton3:
bbox=Map.user_roi_bounds()
st.markdown("{}".format(bbox))
with col1:
Map.to_streamlit(height=750)
```
In addition, when using the 'leafmap' library in streamlit, the Map.add_raster function cannot load the local raster normally, but at the same time, the add_raster function of 'leafmap.foliumap' can be displayed normally in the streamlit map. What is the reason?
| 1medium
|
Title: Example for list response is either a list containing only the first element or a list inside a list in redoc
Body: **Describe the bug**
When I specify a list as an example (in a raw schema) in redoc only the first item in the given list is displayed as the only item in the example.
If I wrap the example list into a list, the example is displayed as a list of list.
In swagger I always get a single element list with garbled data.
**To Reproduce**
Note that this endpoint returns a list of (subset of) icinga (nagios) check items which are converted into the format expected by icinga checks. The data is provided by code that runs these checks and returns the appropriate status code and description.
So this is my application code (I elided some earlier definitions, but you get the idea, I think):
~~~python
STATUS_INFO_NAME_SCHEMA = dict(
type='string',
pattern=r'sis\.[a-z_]+',
description='Name des Checks (beginnt immer mit `"sis."`)'
)
STATUS_INFO_VALUE_SCHEMA = dict(
type='string',
enum=('ok', 'warning', 'error', 'critical'),
description='Wert oder Schwere ("Severity") des Status.'
)
STATUS_INFO_TYPE_SCHEMA = dict(
type='string',
enum=('direct',),
description='Nagios/Icinga Typ (immer `"direct"`)'
)
STATUS_INFO_DESCRIPTION_SCHEMA = dict(
type='string',
description='Menschenlesbare Beschreibung des Resultats'
)
INFO_NAMES = [
'info',
'configuration',
'database',
'fingerprints',
'mail_host',
'django_compatibility',
'request_stats'
]
INFO_NAME_SCHEMA = dict(
type='string',
enum=tuple(
f'sis.{name}'
for name
in INFO_NAMES),
description='Name des Checks (beginnt immer mit `"sis."`)'
)
INFO_NAGIOS_SCHEMA = dict(
type='string',
pattern=(
f'({" | ".join(INFO_NAME_SCHEMA["enum"])})'
f' ({" | ".join(STATUS_INFO_TYPE_SCHEMA["enum"])})'
f' ({" | ".join(STATUS_INFO_VALUE_SCHEMA["enum"])})'
r' - .+'),
description='Vollständige Ausgabe des Nagios/Icinga-Checks'
)
INFO_EXAMPLE = [
OrderedDict([
("name", "sis.info"),
("value", "ok"),
("type", "direct"),
("description", "SIS version devel"),
("nagios", "sis.info direct ok - SIS version devel")]
),
OrderedDict([
("name", "sis.configuration"),
("value", "critical"),
("type", "direct"),
("description", "Config option 'errors-to' missing"),
("nagios", "sis.configuration direct critical - Config option 'errors-to' missing")] # noqa
),
OrderedDict([
("name", "sis.database"),
("value", "ok"),
("type", "direct"),
("description", "Database readable"),
("nagios", "sis.database direct ok - Database readable")]
),
OrderedDict([
("name", "sis.fingerprints"),
("value", "ok"),
("type", "direct"),
("description", "No duplicate fingerprints"),
("nagios", "sis.fingerprints direct ok - No duplicate fingerprints")]
),
OrderedDict([
("name", "sis.mail_host"),
("value", "ok"),
("type", "direct"),
("description", "SMTP server reachable"),
("nagios", "sis.mail_host direct ok - SMTP server reachable")]
),
OrderedDict([
("name", "sis.django_compatibility"),
("value", "ok"),
("type", "direct"),
("description", "SIS is compatible with the current version of Django"), # noqa
("nagios", "sis.django_compatibility direct ok - SIS is compatible with the current version of Django")] # noqa
),
OrderedDict([
("name", "sis.request_stats"),
("value", "ok"),
("type", "direct"),
("description", "No request durations over 100 seconds in the last 5 days"), # noqa
("nagios", "sis.request_stats direct ok - No request durations over 100 seconds in the last 5 days")] # noqa
)
]
INFO_ITEM_SCHEMA = dict(
type='object',
properties=OrderedDict({
'name': INFO_NAME_SCHEMA,
'value': STATUS_INFO_VALUE_SCHEMA,
'type': STATUS_INFO_TYPE_SCHEMA,
'description': STATUS_INFO_DESCRIPTION_SCHEMA,
'nagios': INFO_NAGIOS_SCHEMA,
}),
examples=[INFO_EXAMPLE])
class InfoViewSet(viewsets.ViewSet):
"""[Allgemeine Info über den SIS-Server](/doc/#tag/info)
Erlaubt zu Debugging-Zwecken eine Untermenge der
Statusinformationen ohne Autorisierung.
"""
resource = "info"
permission_classes = [permissions.IsAuthenticated]
@extend_schema(
responses={200: INFO_ITEM_SCHEMA})
def list(self, request):
"""List SIS Information
[/api/info/](/api/info/)
Liefert einen Status von einigen Selbsttest aus.
"""
return Response([c.as_dict() for c in check.info()])
~~~
In this form, the example data will be given in redoc as a list of list.
~~~json
[
[
{
"name": "sis.info",
"value": "ok",
"type": "direct",
"description": "SIS version devel",
"nagios": "sis.info direct ok - SIS version devel"
},
{
"name": "sis.configuration",
"value": "critical",
"type": "direct",
"description": "Config option 'errors-to' missing",
"nagios": "sis.configuration direct critical - Config option 'errors-to' missing"
},
{
"name": "sis.database",
"value": "ok",
"type": "direct",
"description": "Database readable",
"nagios": "sis.database direct ok - Database readable"
},
{
"name": "sis.fingerprints",
"value": "ok",
"type": "direct",
"description": "No duplicate fingerprints",
"nagios": "sis.fingerprints direct ok - No duplicate fingerprints"
},
{
"name": "sis.mail_host",
"value": "ok",
"type": "direct",
"description": "SMTP server reachable",
"nagios": "sis.mail_host direct ok - SMTP server reachable"
},
{
"name": "sis.django_compatibility",
"value": "ok",
"type": "direct",
"description": "SIS is compatible with the current version of Django",
"nagios": "sis.django_compatibility direct ok - SIS is compatible with the current version of Django"
},
{
"name": "sis.request_stats",
"value": "ok",
"type": "direct",
"description": "No request durations over 100 seconds in the last 5 days",
"nagios": "sis.request_stats direct ok - No request durations over 100 seconds in the last 5 days"
}
]
]
~~~
In swagger, the example looks garbled and only has the first item:
~~~json
[
{
"name": "sis.info",
"value": "ok",
"type": "direct",
"description": "string",
"nagios": " sisVrequest_stats direct critical - I{P{2prRCQe68zEfoE,tD"
}
]
~~~
If I change the raw schema to
~~~python
INFO_ITEM_SCHEMA = dict(
type='object',
properties=OrderedDict({
'name': INFO_NAME_SCHEMA,
'value': STATUS_INFO_VALUE_SCHEMA,
'type': STATUS_INFO_TYPE_SCHEMA,
'description': STATUS_INFO_DESCRIPTION_SCHEMA,
'nagios': INFO_NAGIOS_SCHEMA,
}),
examples=INFO_EXAMPLE)
~~~
The example in redoc changes to:
~~~json
[
{
"name": "sis.info",
"value": "ok",
"type": "direct",
"description": "SIS version devel",
"nagios": "sis.info direct ok - SIS version devel"
}
]
~~~
i.e. just the first item. And swagger displays:
~~~json
[
{
"name": "sis.info",
"value": "ok",
"type": "direct",
"description": "string",
"nagios": " sisdfingerprints direct ok - >[U7/ Nw?D+jZQ%=og65=v^v>!Z+^!r)(WY/O46iRA0_4=H"
}
]
~~~
**Expected behavior**
I expect to be able to give a full **list** as an example to a list response and see exactly that list as an example.
I've seen https://drf-spectacular.readthedocs.io/en/stable/faq.html#my-viewset-list-does-not-return-a-list-but-a-single-object as well as https://github.com/tfranzel/drf-spectacular/issues/990 and I've tried to come up with a way to apply the hints in https://drf-spectacular.readthedocs.io/en/stable/faq.html#how-do-i-wrap-my-responses-my-endpoints-are-wrapped-in-a-generic-envelope to an on-the-fly serializer via https://drf-spectacular.readthedocs.io/en/stable/drf_spectacular.html#drf_spectacular.utils.inline_serializer but it seems easier to write a serializer for the data returned by `check.info()`.
That seems wasteful since it already does return an array and I feel it should not be that hard.
Also why does **something** (redoc?) apparently wrap the first element of a list into a list if it's not a list **AND** keep a nested list as is? That garbled swagger output is also very weird. | 1medium
|
Title: extra_requires[dev] does not exist
Body: Issue: running `pip install -e .[dev]` works, but doesn't install the dev requirements. this is due to the missing extra, dev
```
pip install .[dev]
Processing /Users/jchittum/dev01/flask-restplus
flask-restplus 0.12.2.dev0 does not provide the extra 'dev'
```
clearly missing in setup.py
```
extras_require={
'test': tests_require,
'doc': doc_require,
},
```
Also, develop.pip contains an entry that is not compatible with installing in this fashion (even with the very useful parser in setup.py)
```
-e .[test]
invoke==0.21.0
flake8==3.5.0
readme-renderer==17.2
tox==2.9.1
```
| 1medium
|
Title: [BUG] Time Series Predictor Model Fit Causing Kernel to Die (Jupyter Notebook)
Body: **Bug Report Checklist**
<!-- Please ensure at least one of the following to help the developers troubleshoot the problem: -->
- [ ] I provided code that demonstrates a minimal reproducible example. <!-- Ideal, especially via source install -->
- [ ] I confirmed bug exists on the latest mainline of AutoGluon via source install. <!-- Preferred -->
- [ ] I confirmed bug exists on the latest stable version of AutoGluon. <!-- Unnecessary if prior items are checked -->
**Describe the bug**
<!-- A clear and concise description of what the bug is. -->
I am running a Jupyter Notebook on an M2 Macbook Pro with 24 GB of RAM where I am using Autogluon to run a Time Series Predictor model. I am having issues when fitting the model, due to my kernel dying. I have tried 3 things:
1. Uninstalling Autogluon and reinstalling it using the provided method on the Autogluon website.
2. Decreasing the size of the data set
3. Excluding certain hyperparameters in the fit
With doing these my kernel still continues to crash, but when running on colleagues computer it runs with no issues. I am trying to understand why this is an issue on my computer.
**Expected behavior**
<!-- A clear and concise description of what you expected to happen. -->
What is expected when fitting the Autogluon Time Series Predictor Model is a verbose output showing the system information about Autogluon, Python and your computer. Followed by information of the model you are trying to fit, then the information of the fit (Validation score, runtime information).
**Installed Versions**
<!-- Please run the following code snippet: -->
<details>
Here are my systems info for running the following code:
AutoGluon Version: 1.1.1
Python Version: 3.9.16
Operating System: Darwin
Platform Machine: arm64
CPU Count: 8
GPU Count: 0
Memory Avail: 12.45 GB / 24.00 GB (51.9%)
Disk Space Avail: 645.40 GB / 926.35 GB (69.7%)
Below is the Code that I am trying to run:
```python
# Fit the predictor with the specified models
predictor.fit(
training_data,
hyperparameters={
'SeasonalNaive': {}, # Seasonal Naive
'CrostonSBA': {}, # Croston's method with SBA
'NPTS': {}, # Non-Parametric Time Series
'AutoETS': {}, # Auto Exponential Smoothing
'DynamicOptimizedTheta': {}, # Dynamic Optimized Theta
'AutoARIMA': {}, # Auto ARIMA
'RecursiveTabular': {}, # Recursive Tabular Model
'DirectTabular': {}, # Direct Tabular Model
'DeepAR': {}, # DeepAR
'TemporalFusionTransformer': {}, # Temporal Fusion Transformer
'PatchTST': {} # Patch Transformer
},
num_val_windows=1,
time_limit=3600
)
```
Note: With the Data Set not being too large the notebook suggested to decrease the num_val_windows from 5 to 1, but still no improvement. Also the same with decreasing the time_limit.
</details>
| 1medium
|
Title: %autoreload fails since 8.17.0
Body: May be related to #14145
```python
In [1]: %load_ext autoreload
In [2]: %autoreload 3
Error in callback <bound method AutoreloadMagics.pre_run_cell of <IPython.extensions.autoreload.AutoreloadMagics object at 0x7f8877f5ad90>> (for pre_run_cell):
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
TypeError: AutoreloadMagics.pre_run_cell() takes 1 positional argument but 2 were given
```
This caused no issues until including version IPython 8.16.1
I use IPython from command line on Fedora Linux 38, Python 3.11.6 built from source, IPython installed from PyPI. | 1medium
|
Title: dirty-equals causes a infinite-loop if it is used with `functools.singledispatch`
Body: The following code causes a infinite-loop inside `functools.singledispatch`
``` python
from dirty_equals import IsStr
from functools import singledispatch
@singledispatch
def dispatch(a):
print("generic", a)
s = IsStr()
dispatch(s)
```
It looks like this is caused by the fact that `IsStr == IsStr` is `False`.
I already created a pull-request, please let me know if this can be fixed this way.
| 1medium
|
Title: [Feature]cookiejar
Body: requests cookies.py
```
def get_cookie_header(jar, request):
"""
Produce an appropriate Cookie header string to be sent with `request`, or None.
:rtype: str
"""
r = MockRequest(request)
jar.add_cookie_header(r)
return r.get_new_headers().get("Cookie")
```
它会根据请求的url 自动加载jar路径cookie, 底层使用的是http.cookiejar的 add_cookie_header
我希望这个库可以支持一下这个功能
| 1medium
|
Title: Question about seemingly unused file
Body: what does the file 'samplelib/SampleGeneratorFaceCelebAMaskHQ.py' do? | 1medium
|
Title: Enhancement: Support for dictionaries with Pydantic models as value, e.g. dict[str, PydanticClass]
Body: ### Summary
As mentioned on the Discord channel, below code will result in a Pydantic validation error. If I turn households into lists (as well as in the defaults dict as in the HousHolds class) it all works!! But using dict[str, HouseHold] as typehint, does not work out.
As a maintainer `g...` pointed out: This is something we just don't handle in polyfactory currently. When given a dictionary for households, then it's assumed that the value you've given is what should be used. That means we end up not creating instances of HouseHold, but just pass in the raw dictionary and pydantic complains.
Currently we do support Sequence[SomeModel, but no other type is supported. I think supporting things like Mapping[str, SomeModel] is going to be somewhat complex though I'm not a 100% sure.
The code:
```
from pydantic import Field, BaseModel
from uuid import UUID, uuid4
from typing_extensions import TypedDict
from typing import Union
from datetime import date, datetime
from polyfactory.factories.pydantic_factory import ModelFactory
from polyfactory.factories import TypedDictFactory
class RelativeDict(TypedDict):
household_id:str
familymember_id:str
class FamilyMember(BaseModel):
familymember_id: str
name: str
hobbies: list[str]
age: Union[float, int]
birthday: Union[datetime, date]
relatives: list[RelativeDict]
class HouseHold(BaseModel):
household_id: str
name: str
familymembers: list[FamilyMember]
class HouseHolds(BaseModel):
id: UUID = Field(default_factory=uuid4)
households: dict[str, HouseHold]
class RelativeDictFactory(TypedDictFactory[RelativeDict]):
...
class FamilyMemberFactory(ModelFactory[FamilyMember]):
relatives = list[RelativeDictFactory]
class HouseHoldFactory(ModelFactory[HouseHold]):
familymembers = list[FamilyMemberFactory]
class HouseHoldsFactory(ModelFactory[HouseHolds]):
...
defaults = {
"households": {
"beck": {
"household_id": "beck",
"name": "Family Beck",
"familymembers": [
{
"familymember_id": "linda",
"relatives": [
{
"household_id": "grant",
"familymember_id": "dad"
}
]
},
{"familymember_id": "erik"}
]
}
,"grant":{
"household_id": "grant",
"name": "Family Grant",
"familymembers": [
{"familymember_id": "dad"},
{"familymember_id": "mother"}
]
}
}
}
test = HouseHoldsFactory.build(**defaults)
print(test)
```
Just running the build method without any defaults works, but as you can see, the `relatives` key of the `FamilyMember` class should only contain a Household id and a Familymember id that exist (ie. an id of a Household that contains a Familymember wiht a certain id). So there are dependencies, that's the reason of this whole dictionary.
It does saves us from having to provide all the other fields, as in practice these classes could contain a ton of extra fields, which don't have dependencies, so Polyfactory could easily fake those for us.
### Basic Example
Maintainer `g...` provided a basic solution for now:
```
defaults = {
"households": {
"beck": HouseHold(...)
,"grant": HouseHold(...)
}
```
So that should work out or I could override the build method, was his suggestion.
What I think would be ideal is:
```
class HouseHoldsFactory(ModelFactory[HouseHolds]):
households: dict[str, HouseHoldFactory]
```
So the `ModelFactory` knows when it hits the key households, it actually knows how to handle that object (the keys should be a string and the values should be parsed with that `HouseHoldFactory` class).
### Drawbacks and Impact
_No response_
### Unresolved questions
_No response_ | 1medium
|
Title: publish_subscribe.py - Method missing?
Body: The example does not make use of the method Provider.unsubscribe().
I expected that there would be a "wrapping" method like Subscriber.unsubscribe() for removing itself from the Provider (compare: Subscriber.subscribe() for appending itself to the Provider)?
[See draft for publish_subscribe.py tests, line 24](https://github.com/fkromer/python-patterns/blob/test_publish_subscribe/test_publish_subscribe.py)
| 1medium
|
Title: image2image.py on win10: AttributeError: Can't pickle local object 'get_data.<locals>.<lambda>'
Body: Hello,
I am rather new to tensorflow and to python, but I am trying to learn, because I could use the image2image translation network. Well I played around with other pix2pix variants before, and I got some of them working, though rather slow. Then I read, that your tensorpack code is trimmed for speed, so I am trying this now, especially your image2image.py.
So I cloned your tensorpack.git and all the dependencies. Then I tried to train a small selfmade sample training set made out of 512x256 pixel Images, that I put together in the form B to A, in the folder ´train´in the main directory. The code starts, and asks if I want to keep the model. Then the error happens, and it tells me somewhere:
AttributeError: Can't pickle local object 'get_data.<locals>.<lambda>'
(1) The command line I used: image2image.py --data train --mode BtoA
Log:
2020-03-26 23:19:47.581961: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll
[0326 23:19:49 @logger.py:128] WRN Log directory train_log\Image2Image exists! Use 'd' to delete it.
[0326 23:19:49 @logger.py:131] WRN If you're resuming from a previous run, you can choose to keep it.
Press any other key to exit.
Select Action: k (keep) / d (delete) / q (quit):d
[0326 23:19:51 @logger.py:92] Argv: I:\pix2pix basic\Image2Image.py --data train --mode BtoA
[0326 23:19:51 @parallel.py:207] WRN MultiProcessRunner does support Windows. However, Windows requires more strict picklability on processes, which may lead of failure on some of the code.
Traceback (most recent call last):
File "I:\pix2pix basic\Image2Image.py", line 215, in <module>
data = QueueInput(get_data())
File "I:\pix2pix basic\Image2Image.py", line 172, in get_data
ds = MultiProcessRunner(ds, 100, 1)
File "I:\pix2pix basic\tensorpack\dataflow\parallel.py", line 226, in __init__
start_proc_mask_signal(self.procs)
File "I:\pix2pix basic\tensorpack\utils\concurrency.py", line 239, in start_proc_mask_signal
p.start()
File "C:\Program Files\Python37\lib\multiprocessing\process.py", line 112, in start
self._popen = self._Popen(self)
File "C:\Program Files\Python37\lib\multiprocessing\context.py", line 223, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "C:\Program Files\Python37\lib\multiprocessing\context.py", line 322, in _Popen
return Popen(process_obj)
File "C:\Program Files\Python37\lib\multiprocessing\popen_spawn_win32.py", line 89, in __init__
reduction.dump(process_obj, to_child)
File "C:\Program Files\Python37\lib\multiprocessing\reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
AttributeError: Can't pickle local object 'get_data.<locals>.<lambda>'
I:\pix2pix basic>2020-03-26 23:19:51.635938: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Program Files\Python37\lib\multiprocessing\spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "C:\Program Files\Python37\lib\multiprocessing\spawn.py", line 115, in _main
self = reduction.pickle.load(from_parent)
EOFError: Ran out of input
Here´s the log.txt:
[32m[0326 23:42:54 @logger.py:92][0m Argv: I:\pix2pix basic\Image2Image.py --data train --mode BtoA
[32m[0326 23:42:54 @parallel.py:207][0m [5m[31mWRN[0m MultiProcessRunner does support Windows. However, Windows requires more strict picklability on processes, which may lead of failure on some of the code.
2020-03-26 23:40:51.195079: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll
-------------------- ---------------------------------------------------------------------------------
sys.platform win32
Python 3.7.6 (tags/v3.7.6:43364a7ae0, Dec 19 2019, 00:42:30) [MSC v.1916 64 bit (AMD64)]
Tensorpack 0.10
Numpy 1.18.1
TensorFlow 1.15.0/v1.15.0-rc3-22-g590d6eef7e
TF Compiler Version MSVC 191627032
TF CUDA support True
TF MKL support False
TF XLA support False
Nvidia Driver
CUDA
CUDNN
NCCL
CUDA_VISIBLE_DEVICES None
GPU AMD Ryzen 2600X
Free RAM 22.20/31.95 GB
CPU Count 12
cv2 4.1.2
msgpack 1.0.0
python-prctl False
-------------------- ---------------------------------------------------------------------------------
Don´t know why it says "CUDA_VISIBLE_DEVICES None", I have a Geforce 1080 installed, which is (normally) recognized, and which I successfully used in other small neural network experiments.
+ You can install tensorpack master by `pip install -U git+https://github.com/tensorpack/tensorpack.git`
and see if your issue is already solved.
+ If you're not using tensorpack under a normal command line shell (e.g.,
using an IDE or jupyter notebook), please retry under a normal command line shell.
Tried the above, sadly, didin´t change my error. Oh, and may I ask a question? Is your image2image implementation somehow limited in image-dimensions? As I understand it, the original pix2pix code only accepts 256x256 pixel images, so I chose those for my training set.
Thank you very much for your time.
| 1medium
|
Title: Exception error when exporting to HTML
Body: Hello,
I tried exporting my map to HTML, the code is same in the documentation.
`embed_minimal_html('export.html', views=[fig])`
It works fine on my computer, but my friend has encountered the following exception error.
<img width="982" alt="error" src="https://user-images.githubusercontent.com/34975332/54467860-d38d6f80-47c2-11e9-80ae-b05dc33c9a67.png">
I am afraid we forget to do something before running the code.
What can I do to fix this?
Thank you for your help!
Best,
Iris | 1medium
|
Title: tl.files.load_and_assign_npz() does not return 'False'
Body: ### New Issue Checklist
- [x] I have read the [Contribution Guidelines](https://github.com/tensorlayer/tensorlayer/blob/master/CONTRIBUTING.md)
- [x] I searched for [existing GitHub issues](https://github.com/tensorlayer/tensorlayer/issues)
### Issue Description
tl.files.load_and_assign_npz() does not return 'False' when the model is not exist.
I tried to add 'return False' to the code and it works well.
tensorlayer/files/utils.py
```python
def load_and_assign_npz(sess=None, name=None, network=None):
if not os.path.exists(name):
logging.error("file {} doesn't exist.".format(name))
return False
```
### Reproducible Code
- Which OS are you using ?
Ubuntu 16.04 LTS
```python
if tl.files.load_and_assign_npz(sess=sess, name=checkpoint_path + 'g_{}.npz'.format(tl.global_flag['mode']), network=net_g) is False:
tl.files.load_and_assign_npz(sess=sess, name=checkpoint_path + 'g_{}_init.npz'.format(tl.global_flag['mode']), network=net_g)
``` | 1medium
|
Title: Standalone HTML representation of animation not loading
Body: I've tried running the hydrogen example notebook through nbconvert to get a "static" representation. The produced html file will generally load, but the plot will not.
Browser console (same error in firefox)
[k3d.log](https://github.com/K3D-tools/K3D-jupyter/files/6765093/k3d.log)
I've added the converted html too (zipped since GitHub doesn't allow html attachments:
[k3d.html.zip](https://github.com/K3D-tools/K3D-jupyter/files/6765102/k3d.html.zip)
| 1medium
|
Title: vkQueueSubmit failed -4
Body: realesrgan-ncnn-vulkan-20210901-windows.zip
Win10 Home,
GF 920M
Run with
realesrgan-ncnn-vulkan.exe -i 01.JPG -o 01-up.jpg
Tried with different files, same error(s). Got black plain jpg file on output. | 1medium
|
Title: how to implemente get_document_topics()
Body: Hi all,
I use the method of AuthorTopicModel() build a model, then i want to get the topic of each document or the document belongs to which topic(assume that i have 10 topics) and the tutorial tell me that the method of get_document_topics() are not be implemented, so how to implemente it.
thanks in advance
yi zhao
| 1medium
|
Title: Improper converter check
Body: Can someone please check this [converter check](https://github.com/microsoft/hummingbird/blob/2705c42585ac1d3ee69e65a93e5f59d7d42f44d5/hummingbird/ml/_topology.py#L205) in topology.py?
Shouldn't it be a `if converter is None:` ?
Or am I just missing something in the flow?
| 1medium
|
Title: [Suggestion] Remove print statements in `handler.py`
Body: ## Context
I have gone thru some effort to reduce my CloudWatch logs. By default Zappa was logging a lot of things that I didn't use to CloudWatch which was costing me $100s / mo.
The best solution I found to suppressing logs was this code in `app.py`:
```
logging.getLogger('boto3').setLevel(logging.ERROR)
logging.getLogger('botocore').setLevel(logging.ERROR)
logging.getLogger('s3transfer').setLevel(logging.ERROR)
logging.getLogger('urllib3').setLevel(logging.ERROR)
logging.getLogger('requests').setLevel(logging.ERROR)
logging.getLogger('zappa').setLevel(logging.INFO)
logging.getLogger('chardet.charsetprober').setLevel(logging.INFO)
```
Now the majority of my logs are due to `print()` statements in `handler.py` which cannot be suppressed in my application code.
## Possible Fix
I would like to reduce logging from `handler.py`.
Here are three options:
1. Remove `print()` statements as on the line https://github.com/Miserlou/Zappa/blob/master/zappa/handler.py#L383
2. Change the `print()` statements to user a `logger`
3. Put the `print()` statements behind a `enable_logging` flag | 1medium
|
Title: emotion voice cloning
Body: I use the method mentioned in thie rep https://github.com/innnky/emotional-vits to try to implement emotion voice cloning, I finetuned the pretrained synthsizer on a small dataset that contains about 24 speakers, each with 100 audio, and these 100 pieces of audio are divided by into roughly five or four categories, with the same tetx in each category but with different emotions. I inference with the finetuned synthesizer and the pretrained encoder and vocoder, but it's not working very well, if anyone know what the problem is or how it should be trained? | 1medium
|
Title: How to open a file *.html?
Body: Hello,
I am trying to access to a file but it is giving me an error:
`ERROR - Invalid URL: "/output/html/my_html.html"!`
How could I do that? Thank you | 1medium
|
Title: Consider allowing PythonCallable initialization with dotted paths
Body: We're currently working on exporting DAGs to Airflow but PythonCallables won't be supported for the first version. When we convert a Ploomber DAG to Airflow, we need to instantiate the Ploomber DAG, if it has PythonCallables, the actual functions have to be imported, which implies that all imported packages in those functions must be installed in the Airflow host.
This is highly problematic, as it can pollute the python installation where Airflow is running and it requires all dependencies to be installed before parsing the DAG.
A possible solution would be to let PythonCallables be initialized with dotted paths, so they don't have to import the source function until the task is executed. | 1medium
|
Title: When Serving static files: "TypeError: 'NoneType' object is not iterable"
Body: Using the example code:
```import responder
api = responder.API()
@api.route("/{greeting}")
async def greet_world(req, resp, *, greeting):
resp.text = f"{greeting}, world!"
if __name__ == '__main__':
api.run()
```
I dropped a couple files in the `static/` directory, and while it does successfully serve the files, I get these messages back in the log whenever it loads my index.html file.
```
INFO: ('127.0.0.1', 46560) - "GET /index.html HTTP/1.1" 302
INFO: ('127.0.0.1', 46560) - "GET / HTTP/1.1" 304
ERROR: Exception in ASGI application
Traceback (most recent call last):
File "/home/cig/PycharmProjects/PythonSandbox/venv/lib64/python3.6/site-packages/uvicorn/protocols/http/httptools_impl.py", line 387, in run_asgi
result = await asgi(self.receive, self.send)
File "/home/cig/PycharmProjects/PythonSandbox/venv/lib64/python3.6/site-packages/uvicorn/middleware/message_logger.py", line 59, in __call__
await self.inner(self.receive, self.send)
File "/home/cig/PycharmProjects/PythonSandbox/venv/lib64/python3.6/site-packages/asgiref/wsgi.py", line 41, in __call__
await self.run_wsgi_app(message)
File "/home/cig/PycharmProjects/PythonSandbox/venv/lib64/python3.6/site-packages/asgiref/sync.py", line 108, in __call__
return await asyncio.wait_for(future, timeout=None)
File "/usr/lib64/python3.6/asyncio/tasks.py", line 339, in wait_for
return (yield from fut)
File "/usr/lib64/python3.6/concurrent/futures/thread.py", line 56, in run
result = self.fn(*self.args, **self.kwargs)
File "/home/cig/PycharmProjects/PythonSandbox/venv/lib64/python3.6/site-packages/asgiref/sync.py", line 123, in thread_handler
return self.func(*args, **kwargs)
File "/home/cig/PycharmProjects/PythonSandbox/venv/lib64/python3.6/site-packages/asgiref/wsgi.py", line 118, in run_wsgi_app
for output in self.wsgi_application(environ, self.start_response):
TypeError: 'NoneType' object is not iterable
INFO: ('127.0.0.1', 46564) - "GET /static/chat.js HTTP/1.1" 500
ERROR: Exception in ASGI application
Traceback (most recent call last):
File "/home/cig/PycharmProjects/PythonSandbox/venv/lib64/python3.6/site-packages/uvicorn/protocols/http/httptools_impl.py", line 387, in run_asgi
result = await asgi(self.receive, self.send)
File "/home/cig/PycharmProjects/PythonSandbox/venv/lib64/python3.6/site-packages/uvicorn/middleware/message_logger.py", line 59, in __call__
await self.inner(self.receive, self.send)
File "/home/cig/PycharmProjects/PythonSandbox/venv/lib64/python3.6/site-packages/asgiref/wsgi.py", line 41, in __call__
await self.run_wsgi_app(message)
File "/home/cig/PycharmProjects/PythonSandbox/venv/lib64/python3.6/site-packages/asgiref/sync.py", line 108, in __call__
return await asyncio.wait_for(future, timeout=None)
File "/usr/lib64/python3.6/asyncio/tasks.py", line 339, in wait_for
return (yield from fut)
File "/usr/lib64/python3.6/concurrent/futures/thread.py", line 56, in run
result = self.fn(*self.args, **self.kwargs)
File "/home/cig/PycharmProjects/PythonSandbox/venv/lib64/python3.6/site-packages/asgiref/sync.py", line 123, in thread_handler
return self.func(*args, **kwargs)
File "/home/cig/PycharmProjects/PythonSandbox/venv/lib64/python3.6/site-packages/asgiref/wsgi.py", line 118, in run_wsgi_app
for output in self.wsgi_application(environ, self.start_response):
TypeError: 'NoneType' object is not iterable
INFO: ('127.0.0.1', 46568) - "GET /static/chat.js HTTP/1.1" 500
```
To reproduce:
1. Check out my [sandbox](https://github.com/chrisbrake/PythonSandbox)
2. Install `responder`
3. Go into the `toDo` module
4. Run `app.py`
5. Go to http://127.0.0.1:5042/static/ | 2hard
|
Title: Is it possible to use templates without {"mask"}?
Body: I am using OpenPrompt on UnifiedQA (https://github.com/allenai/unifiedqa), which generates the answer using T5 without a mask token.
I tried a template without {"mask"}, but this is not allowed in OpenPrompt.
I use the template {"placeholder":"text_a"} \n {"placeholder":"text_b"} {"mask"}, following the instruction in UnifiedQA, but the generation results are most empty.
Is there a way to use T5 generation (instead of mask filling) in OpenPrompt?
like this:
| 2hard
|
Title: How to log all of the query sql string with args ?
Body: I tried set `echo=True` and `logging.getLogger("databases").setLevel(logging.DEBUG)`, but both are not working.
Are there have some method to log sql string and args except `print(query)` ? I want something global.
Thank you ! | 1medium
|
Title: Issue with connection to database with postgres (asyncpg)
Body: There is simplified example extracted from /databases/backends/postgres.py:connect()
```python
import asyncio
import asyncpg
from databases import DatabaseURL
async def run():
url = 'postgresql://user:password@localhost/dbname'
pool = await asyncpg.create_pool(str(DatabaseURL(url)))
loop = asyncio.get_event_loop()
loop.run_until_complete(run())
```
I am getting error `socket.gaierror: [Errno -3] Temporary failure in name resolution` and that's fair since according to https://magicstack.github.io/asyncpg/current/api/index.html#connection-pools accepts **dsn** (str) – Connection arguments specified using as a single string in the following format: `postgres://user:pass@host:port/database?option=value` like that.
So we need to add some note to docs maybe or handle that situation via advanced parsing maybe.. | 1medium
|
Title: Dose there any better way to write timezone aware datetime field without using the SQLAlchemy ?
Body: ### First Check
- [X] I added a very descriptive title to this issue.
- [X] I used the GitHub search to find a similar issue and didn't find it.
- [X] I searched the SQLModel documentation, with the integrated search.
- [X] I already searched in Google "How to X in SQLModel" and didn't find any information.
- [X] I already read and followed all the tutorial in the docs and didn't find an answer.
- [X] I already checked if it is not related to SQLModel but to [Pydantic](https://github.com/samuelcolvin/pydantic).
- [X] I already checked if it is not related to SQLModel but to [SQLAlchemy](https://github.com/sqlalchemy/sqlalchemy).
### Commit to Help
- [X] I commit to help with one of those options 👆
### Example Code
```python
class UserBase(SQLModel):
username: str = Field(index=True, unique=True)
email: EmailStr = Field(unique=True, index=True) # this field should be unique for table and this field is required
fullname: str | None = None
created_at: datetime = Field(default_factory=datetime.utcnow)
updated_at: datetime = Field(default_factory=datetime.utcnow)
```
### Description
I write my `created_at` and `updated_at` fields like this, however, this did not work, because of the time awareness,
```
SQLModel user: fullname='string' created_at=datetime.datetime(2023, 1, 26, 18, 19, 32, 961000, tzinfo=datetime.timezone.utc) updated_at=datetime.datetime(2023, 1, 26, 18, 19, 32, 961000, tzinfo=datetime.timezone.utc) id=None is_staff=False is_admin=False username='string' email='[email protected]' password='string'
(sqlalchemy.dialects.postgresql.asyncpg.Error) <class 'asyncpg.exceptions.DataError'>: invalid input for query argument $4: datetime.datetime(2023, 1, 26, 18, 19, 3... (can't subtract offset-naive and offset-aware datetimes)
```
After checking Github, i found this solution:
```python
class AuthUser(sqlmodel.SQLModel, table=True):
__tablename__ = 'auth_user'
id: Optional[int] = sqlmodel.Field(default=None, primary_key=True)
password: str = sqlmodel.Field(max_length=128)
last_login: datetime.datetime = Field(sa_column=sa.Column(sa.DateTime(timezone=True), nullable=False))
```
It is written with mixing SQLModel stuff and the SALAlchemy, I know SQLModel is SQLAlchemy under the hood but this feels strange, cause i want to face SQLModel ONLY.
Is there any better way of handling this?
Let's say when SQLModel create tables, it will check the payding field `created_at`, if it is timezone aware datetime then it will set it as `sa_column=sa.Column(sa.DateTime(timezone=True)` so that we do not need to mix them both,
### Operating System
macOS
### Operating System Details
_No response_
### SQLModel Version
0.0.8
### Python Version
3.10.2
### Additional Context
_No response_ | 1medium
|
Title: Create CAM for network trained on timeseries
Body: Would it be possible to handle networks trained for timeseries classification? Example CNN below:
```
class Cnn1d(nn.Module):
def __init__(self, outputs=2):
super(Cnn1d, self).__init__()
self.outputs = outputs
self.conv1 = nn.Conv1d(1, 16, 3)
self.bn1 = nn.BatchNorm1d(16)
self.pool1 = nn.MaxPool1d(2)
self.conv2 = nn.Conv1d(16, 32, 3)
self.bn2 = nn.BatchNorm1d(32)
self.pool2 = nn.MaxPool1d(2)
self.conv3 = nn.Conv1d(32, 64, 3)
self.bn3 = nn.BatchNorm1d(64)
self.pool3 = nn.MaxPool1d(2)
self.conv4 = nn.Conv1d(64, 128, 3)
self.bn4 = nn.BatchNorm1d(128)
self.pool4 = nn.MaxPool1d(2)
self.dropout = nn.Dropout()
self.avgPool = nn.AvgPool1d(127)
self.fc1 = nn.Linear(256, self.outputs)
def forward(self, x):
x = self.conv1(x)
x = F.relu(self.bn1(x))
x = self.pool1(x)
x = self.conv2(x)
x = F.relu(self.bn2(x))
x = self.pool2(x)
x = self.conv3(x)
x = F.relu(self.bn3(x))
x = self.pool3(x)
x = self.conv4(x)
x = F.relu(self.bn4(x))
x = self.pool4(x)
x = self.dropout(x)
x = self.avgPool(x)
x = x.view(x.shape[0], -1)
x = self.fc1(x)
return x
``` | 2hard
|
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