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ymcui/Chinese-BERT-wwm | nlp | 192 | 如何获得词向量？ | 请问是简单的词语中的字向量相加取平均还是简单相加得到的？ | closed | 2021-07-30T08:32:28Z | 2021-08-07T10:51:16Z | https://github.com/ymcui/Chinese-BERT-wwm/issues/192 | [
"stale"
] | c9412600 | 2 |
taverntesting/tavern | pytest | 631 | AttributeError: module 'pykwalify.core' has no attribute 'yaml' | Hello, it looks like pykwalify has had a new release (1.8) which is breaking my tavern build with the following error:
```
ve/lib/python3.8/site-packages/tavern/schemas/files.py:16: in <module>
core.yaml.safe_load = functools.partial(yaml.load, Loader=IncludeLoader)
E AttributeError: module 'pykwalify.core' has no attribute 'yaml'
```
It looks like pykwalify has dropped/changed their default yaml parser which may be the culprit?
Could we maybe get tavern pinned to pykwalify 1.7 until it's fixed?
https://github.com/Grokzen/pykwalify/releases
https://github.com/taverntesting/tavern/blob/master/setup.cfg#L34 | closed | 2020-12-31T03:54:13Z | 2022-08-17T07:00:42Z | https://github.com/taverntesting/tavern/issues/631 | [] | erichaus | 6 |
feature-engine/feature_engine | scikit-learn | 693 | future warnings | List of future pandas warnings
```
c:\users\sole\documents\repositories\feature_engine\feature_engine\creation\math_features.py:212: FutureWarning: The provided callable <function sum at 0x0000016E1CA6D090> is currently using Series.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass 'sum' instead.
```
| closed | 2023-09-12T10:58:44Z | 2024-07-16T06:02:11Z | https://github.com/feature-engine/feature_engine/issues/693 | [] | solegalli | 5 |
mwaskom/seaborn | matplotlib | 2,908 | Unexpected behaviour of ticks in next gen Seaborn |
Hello,
Just found a potential bug in the next gen version of **seaborn**.
Expected behaviour: create a histogram with y-axis limits that go from 0 to 50.
Actual behaviour: limits of y-axis do not change.
Other possible reasons for this: I may be confused between the syntax for ticks and for axes limits here, but the ticks do not appear to change either.
Reprex:
```python
import pandas as pd
import seaborn.objects as so
diamonds = pd.read_csv(
"https://github.com/mwaskom/seaborn-data/raw/master/diamonds.csv"
)
(
so.Plot(diamonds, x="y")
.add(so.Bar(), so.Hist(binwidth=0.5))
.scale(y=so.Continuous(trans=None).tick(at=[0, 50]))
)
```
System info:
Python implementation: CPython
Python version : 3.9.12
IPython version : 8.3.0
Compiler : Clang 12.0.1
OS : Darwin
Release : 19.6.0
Machine : x86_64
Processor : i386
CPU cores : 4
Architecture: 64bit
seaborn : 0.12.0b1
matplotlib : 3.5.2
matplotlib_inline: 0.1.3
sys : 3.9.12 | packaged by conda-forge | (main, Mar 24 2022, 23:23:20)
[Clang 12.0.1 ]
pandas : 1.4.2
Thanks for all the work on this amazing package! | closed | 2022-07-14T13:17:42Z | 2022-07-24T00:25:25Z | https://github.com/mwaskom/seaborn/issues/2908 | [
"bug",
"objects-plot"
] | aeturrell | 1 |
jpadilla/django-rest-framework-jwt | django | 461 | Unable to log in with the authentication information provided | when i post data, i always get this msg: Unable to log in with the authentication information provided
```
class CustomBackend(ModelBackend):
def authenticate(self, username=None, password=None, **kwargs):
try:
user = User.objects.get(Q(username=username)|Q(mobile=username))
if user.check_password(password):
return user
except Exception as e:
return None
```
i'm follow offical website config, but don't know why not post data to API? | open | 2018-12-05T04:42:21Z | 2018-12-05T04:44:37Z | https://github.com/jpadilla/django-rest-framework-jwt/issues/461 | [] | labike | 0 |
anselal/antminer-monitor | dash | 191 | Request to add graphs | great product. add graphics, please. | open | 2021-12-23T12:11:02Z | 2021-12-23T12:11:02Z | https://github.com/anselal/antminer-monitor/issues/191 | [] | iacsvrn | 0 |
automl/auto-sklearn | scikit-learn | 1,324 | I have confusion with final ensemble model. | My constractor:
`automl = autosklearn.classification.AutoSklearnClassifier(
time_left_for_this_task=3600,
memory_limit=3072,
ensemble_size=10,
ensemble_nbest=10,
max_models_on_disc=10,
delete_tmp_folder_after_terminate=False,
)`
Below is my model's sprint_statistics
Number of target algorithm runs: 17\n
Number of successful target algorithm runs: 4\n
Number of crashed target algorithm runs: 2\n
Number of target algorithms that exceeded the time limit: 9\n
Number of target algorithms that exceeded the memory limit: 2\n'
output of `show_model()` :
`"[(1.000000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'random_forest', 'data_preprocessing:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessing:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessing:numerical_transformer:imputation:strategy': 'mean', 'data_preprocessing:numerical_transformer:rescaling:__choice__': 'robust_scaler', 'feature_preprocessor:__choice__': 'select_rates_classification', 'classifier:random_forest:bootstrap': 'False', 'classifier:random_forest:criterion': 'gini', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.23832696118792362, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 1, 'classifier:random_forest:min_samples_split': 16, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, 'data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.035949803138524174, 'data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_max': 0.7356350569414665, 'data_preprocessing:numerical_transformer:rescaling:robust_scaler:q_min': 0.2902106911441806, 'feature_preprocessor:select_rates_classification:alpha': 0.3610975428987517, 'feature_preprocessor:select_rates_classification:score_func': 'mutual_info_classif'},\ndataset_properties={\n 'task': 2,\n 'sparse': False,\n 'multilabel': False,\n 'multiclass': True,\n 'target_type': 'classification',\n 'signed': False})),\n]"`
My question is why I got only one algorithm/configuration in final ensemble model if my successful target algorithms are 4 and ensemble size are 10? My final model should be ensemble of 4 configurations, right? | closed | 2021-11-26T12:48:35Z | 2021-11-29T12:49:41Z | https://github.com/automl/auto-sklearn/issues/1324 | [] | shabir1 | 2 |
gunthercox/ChatterBot | machine-learning | 1,904 | ImportError: cannot import name 'StatementMixin' | 
| closed | 2020-01-30T09:27:39Z | 2025-02-23T23:38:53Z | https://github.com/gunthercox/ChatterBot/issues/1904 | [] | JoinChang | 1 |
ClimbsRocks/auto_ml | scikit-learn | 270 | eventually, train on the entire dataset even when warm_starting, or early_stopping | right now we're losing 10-15% of our data for our validation stuff, to find the best number of rounds/iterations/estimators/etc.
eventually, it would be ideal to train with early stopping to find the ideal number of iterations, then use that (plus some small amount, say 5% of however many num_iteration we have to account for the fact that we'll have a slightly larger dataset now) to train a new model with all the data.
so, we'd end up training the model twice: once to find the ideal num_iteration, once with the full dataset using the ideal num_iteration.
make this a param to .train(). not sure of the name yet. | open | 2017-07-05T16:26:36Z | 2017-07-05T16:26:36Z | https://github.com/ClimbsRocks/auto_ml/issues/270 | [] | ClimbsRocks | 0 |
twopirllc/pandas-ta | pandas | 764 | Indicators using moving averages variable does not pass kwargs to ma(mamode, close, length=length) | **Which version are you running? The lastest version is on Github. Pip is for major releases.**
```python 3.11
import pandas_ta as ta
print(ta.version) 2.1.4
```
**Do you have _TA Lib_ also installed in your environment?**
```sh
$ pip list
```
Yes
**Have you tried the _development_ version? Did it resolve the issue?**
```sh
$ pip install -U git+https://github.com/twopirllc/pandas-ta.git@development
```
No
**Describe the bug**
Many indicators e.g. Keltner Channel is using internally ma(mamode, close, length) but it should have
ma(mamode, close, length, kwargs)
Many moving averages e.g linreg have extra kwargs that are not passed further
So for example passing kwargs to kc Class is not passing those kwargs to be used when calculating moving average.
**To Reproduce**
Provide sample code.
```python
if mamode == 'linreg':
kwargs={'degrees': True, 'slope': True, 'tsf': True}
print(kwargs)
kc_var = kc(close=df['close'], high=df['high'], low=df['low'], length=p_length, mamode=mamode, scalar=p_scalar, kwargs=kwargs)
```
**Expected behavior**
A clear and concise description of what you expected to happen.
When calling should have kwargs passed into it e.g. ma(mamode, close, length, kwargs)
Inside kc
```python
# Calculate Result
use_tr = kwargs.pop("tr", True)
if use_tr:
range_ = true_range(high, low, close)
else:
range_ = high_low_range(high, low)
#SHOULD BE BELOW
basis = ma(mamode, close, length=length, kwargs)
band = ma(mamode, range_, length=length, kwargs)
```
**Screenshots**
If applicable, add screenshots to help explain your problem.
**Additional context**
Add any other context about the problem here.
Thanks for using Pandas TA!
| open | 2024-02-11T22:15:15Z | 2024-02-19T01:35:43Z | https://github.com/twopirllc/pandas-ta/issues/764 | [
"enhancement",
"help wanted",
"good first issue"
] | PiotrJuzwiak | 1 |
wkentaro/labelme | computer-vision | 659 | [BUG] label with angle brackets <> causes display errors | **Describe the bug**
When the label contains **<>**, the label list will show, for example `<NONCAR><font color="#c8c8c8"> 〇 </font>`. Meanwhile, in the _polygon labels_ panel, only the color circle shows but not the name <NONCAR>.
**To Reproduce**
Steps to reproduce the behavior:
1. create a polygon and label it as **<text>**
**Expected behavior**
like other normal labels
**Desktop:**
- OS: MacOS 10.15.4
- Labelme Version 4.2.10
| closed | 2020-05-18T02:24:03Z | 2022-06-25T16:05:38Z | https://github.com/wkentaro/labelme/issues/659 | [
"issue::bug"
] | wxupjack | 0 |
JaidedAI/EasyOCR | pytorch | 1,103 | Japanese Kanji character | I found that the Japanese character "絆" is missing in the file "ja_char.txt".And I cannot recognize this Japanese Kanji character using the Japanese model. | open | 2023-08-02T15:20:34Z | 2023-08-02T15:20:34Z | https://github.com/JaidedAI/EasyOCR/issues/1103 | [] | 98andpeople | 0 |
autogluon/autogluon | computer-vision | 4,328 | Using custom model.hf_text.checkpoint_name | ## Description
In Autogluon multimodal, you can specify a text model on Huggingface (say for the sake of the example roberta-base). If I fine-tuned roberta-base using the Transformers library but did not publish to Huggingface, can I still train on that backbone by specifying the path in the model.hf_text.checkpoint_name hyperparameter?
| open | 2024-07-18T00:47:39Z | 2024-07-18T00:47:39Z | https://github.com/autogluon/autogluon/issues/4328 | [
"enhancement"
] | zkalson | 0 |
Kinto/kinto | api | 2,984 | Either don't send email notification or only selectively notify about changes to nimbus-desktop-experiments | Every change to `main-workspace/nimbus-desktop-experiments` is currently notified to each reviewer by email.
These don't seem to carry much value, as they are generated as part of the process of actually releasing an experiment - the reviewer will go and approve them straight away, and they shouldn't need to know that they've approved them.
Whilst reviewers could use email filters to filter these out, I think the fact that we're adding lots of reviewers means we should consider if it is really necessary to send these notifications out in the first place - or maybe, only a subset of reviewers need them. | closed | 2022-04-19T15:57:24Z | 2022-04-19T16:05:23Z | https://github.com/Kinto/kinto/issues/2984 | [] | Standard8 | 0 |
matplotlib/matplotlib | data-visualization | 29,004 | Decide how to document SecondaryAxes | ### Documentation Link
_No response_
### Problem
A reminder/placeholder/replacement for #28953 .
### Suggested improvement
_No response_ | open | 2024-10-21T18:05:04Z | 2024-10-21T18:05:04Z | https://github.com/matplotlib/matplotlib/issues/29004 | [] | timhoffm | 0 |
recommenders-team/recommenders | deep-learning | 2,205 | [FEATURE] Add embeding ranker in PyTorch | ### Description
<!--- Describe your expected feature in detail -->
https://www.tensorflow.org/recommenders/examples/basic_ranking, but using PyTorch instead of TF
### Expected behavior with the suggested feature
<!--- For example: -->
<!--- *Adding algorithm xxx will help people understand more about xxx use case scenarios. -->
### Willingness to contribute
<!--- Go over all the following points, and put an `x` in the box that apply. -->
- [ ] Yes, I can contribute for this issue independently.
- [ ] Yes, I can contribute for this issue with guidance from Recommenders community.
- [ ] No, I cannot contribute at this time.
### Other Comments
| open | 2025-01-16T09:17:38Z | 2025-01-24T22:04:07Z | https://github.com/recommenders-team/recommenders/issues/2205 | [
"enhancement"
] | miguelgfierro | 7 |
3b1b/manim | python | 1,714 | The animation window didn't show up | I typed 'manimgl' and pressed the key 'Enter', this is what happened to my terminal.
My terminal just waiting, with the cursor flashing continously.
'Maybe it is loading now, I just to wait for some time', I thought, but through an hour (or longer), nothing happened, and the terminal just waiting, waiting for me to type some string, like a text edit software.
===Environment Config========
System: Ubuntu 21.10, x64
Python: 3.9.7
Manim: 1.3.0
===Problem Description========
It should pop up the animation window and the terminal should enter interactive mode, but in this case the animation window didn't show up.
===What I have done==========
I have installed the manimgl (using the command '`pip install manimgl`'), and other softwares (like TeXLive, FFmpeg) also have been installed. | closed | 2022-01-25T12:36:58Z | 2022-01-26T17:18:54Z | https://github.com/3b1b/manim/issues/1714 | [] | Acpear | 0 |
psf/black | python | 4,594 | long string: black miscalculates line lengths (without string_processing) | **Describe the bug**
In some cases the current black version (25.1.0) reformats a string that is too long. It seems to miscalculate the resulting line length.
**To Reproduce**
```python
class A:
def foo(self):
return (
"This is a very loooooooooooooooooooooooooooooooooooooooooooooong string"
)
```
Note: The long line is exactly 86 chars wide. The bug does not occur when I add one character.
```sh
$ black file.py
```
reformats to:
```python
class A:
def foo(self):
return "This is a very loooooooooooooooooooooooooooooooooooooooooooooong string"
```
With the long line being 89 chars long.
I would expect it to leave the code as is in this case.
**Environment**
- Black's version: 25.1.0
- OS and Python version: Gentoo, Python 3.11.11
**Additional context**
The problem occurred first with in a project using `black -l100` producing a line of length 105.
| closed | 2025-02-28T11:37:29Z | 2025-02-28T16:42:25Z | https://github.com/psf/black/issues/4594 | [
"T: bug"
] | torotil | 1 |
django-import-export/django-import-export | django | 1,155 | How to raise message when import file # format file action import? | <!--- Welcome to django-import-export -->
<!--- Your issue may already be reported!
Please search on the [issue tracker](../) before creating one. -->
<!--- Additionally, you can either search or ask you question on [Stack Overflow tagged with `django-import-export`](https://stackoverflow.com/questions/tagged/django-import-export) -->
<!--- If you would still like to create a question on github, please do so and remember that the more details you give, the more likely your question will be answered. -->
| closed | 2020-06-16T03:51:11Z | 2021-06-22T09:21:40Z | https://github.com/django-import-export/django-import-export/issues/1155 | [
"question"
] | sa-thangbn | 4 |
tensorflow/tensor2tensor | deep-learning | 1,683 | ValueError: The passed save_path is not a valid checkpoint | Hello.
I'm having problems resuming training from a checkpoint on Google Colab. The only way I've found is to delete all checkpoints and start again, which of course isn't a good idea after training for hours.
I'm using TF and t2t version 1.14.0 and Ubuntu 18.04 on normal runtime because of a problem using Colab's GPU I've reported [here](https://github.com/tensorflow/tensorflow/issues/32017).
This is the code :
```
!t2t-trainer \
--tmp_dir='/content/gdrive/My Drive/TCC/T2T LibriSpeech/tmp/' \
--problem='librispeech_clean_small' \
--model='transformer' \
--eval_steps=3 \
--hparams_set='transformer_librispeech' \
--data_dir='/content/gdrive/My Drive/TCC/T2T LibriSpeech/data/' \
--output_dir='/content/gdrive/My Drive/TCC/T2T LibriSpeech/output/'
```
And this is the prompt output:
```
WARNING: Logging before flag parsing goes to stderr.
W0828 15:46:33.243587 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/expert_utils.py:68: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.
W0828 15:46:34.237717 139684777486208 lazy_loader.py:50]
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
* https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
* https://github.com/tensorflow/addons
* https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.
W0828 15:46:36.218165 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/adafactor.py:27: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
W0828 15:46:36.218689 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/multistep_optimizer.py:32: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.
W0828 15:46:36.231890 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/mesh_tensorflow/ops.py:4237: The name tf.train.CheckpointSaverListener is deprecated. Please use tf.estimator.CheckpointSaverListener instead.
W0828 15:46:36.232139 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/mesh_tensorflow/ops.py:4260: The name tf.train.SessionRunHook is deprecated. Please use tf.estimator.SessionRunHook instead.
W0828 15:46:36.251127 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/models/research/neural_stack.py:38: The name tf.nn.rnn_cell.RNNCell is deprecated. Please use tf.compat.v1.nn.rnn_cell.RNNCell instead.
W0828 15:46:36.288087 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/rl/gym_utils.py:235: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.
W0828 15:46:36.311170 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/trainer_lib.py:111: The name tf.OptimizerOptions is deprecated. Please use tf.compat.v1.OptimizerOptions instead.
W0828 15:46:36.326797 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensorflow_gan/python/contrib_utils.py:305: The name tf.estimator.tpu.TPUEstimator is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimator instead.
W0828 15:46:36.327040 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensorflow_gan/python/contrib_utils.py:310: The name tf.estimator.tpu.TPUEstimatorSpec is deprecated. Please use tf.compat.v1.estimator.tpu.TPUEstimatorSpec instead.
W0828 15:46:37.165019 139684777486208 deprecation_wrapper.py:119] From /usr/local/bin/t2t-trainer:32: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.
W0828 15:46:37.165243 139684777486208 deprecation_wrapper.py:119] From /usr/local/bin/t2t-trainer:32: The name tf.logging.INFO is deprecated. Please use tf.compat.v1.logging.INFO instead.
W0828 15:46:37.165358 139684777486208 deprecation_wrapper.py:119] From /usr/local/bin/t2t-trainer:33: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.
W0828 15:46:37.166135 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/hparams_lib.py:49: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead.
I0828 15:46:37.167073 139684777486208 hparams_lib.py:64] Loading hparams from existing json /content/gdrive/My Drive/TCC/T2T LibriSpeech/output/hparams.json
W0828 15:46:37.167232 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/hparams_lib.py:65: The name tf.gfile.Open is deprecated. Please use tf.io.gfile.GFile instead.
W0828 15:46:37.169995 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/trainer_lib.py:839: The name tf.set_random_seed is deprecated. Please use tf.compat.v1.set_random_seed instead.
W0828 15:46:37.170993 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/trainer_lib.py:123: The name tf.GraphOptions is deprecated. Please use tf.compat.v1.GraphOptions instead.
W0828 15:46:37.171175 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/trainer_lib.py:129: The name tf.GPUOptions is deprecated. Please use tf.compat.v1.GPUOptions instead.
W0828 15:46:37.171345 139684777486208 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/trainer_lib.py:242: RunConfig.__init__ (from tensorflow.contrib.learn.python.learn.estimators.run_config) is deprecated and will be removed in a future version.
Instructions for updating:
When switching to tf.estimator.Estimator, use tf.estimator.RunConfig instead.
I0828 15:46:37.171534 139684777486208 trainer_lib.py:265] Configuring DataParallelism to replicate the model.
I0828 15:46:37.171617 139684777486208 devices.py:76] schedule=continuous_train_and_eval
I0828 15:46:37.171699 139684777486208 devices.py:77] worker_gpu=1
I0828 15:46:37.171761 139684777486208 devices.py:78] sync=False
W0828 15:46:37.171855 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/devices.py:139: The name tf.logging.warn is deprecated. Please use tf.compat.v1.logging.warn instead.
W0828 15:46:37.171929 139684777486208 devices.py:141] Schedule=continuous_train_and_eval. Assuming that training is running on a single machine.
I0828 15:46:37.172624 139684777486208 devices.py:170] datashard_devices: ['gpu:0']
I0828 15:46:37.172721 139684777486208 devices.py:171] caching_devices: None
I0828 15:46:37.173149 139684777486208 devices.py:172] ps_devices: ['gpu:0']
I0828 15:46:37.173902 139684777486208 estimator.py:209] Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f0aa908abe0>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_train_distribute': None, '_eval_distribute': None, '_experimental_max_worker_delay_secs': None, '_device_fn': None, '_tf_config': gpu_options {
per_process_gpu_memory_fraction: 1.0
}
, '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': None, '_log_step_count_steps': 100, '_protocol': None, '_session_config': gpu_options {
per_process_gpu_memory_fraction: 0.95
}
allow_soft_placement: true
graph_options {
optimizer_options {
global_jit_level: OFF
}
}
isolate_session_state: true
, '_save_checkpoints_steps': 1000, '_keep_checkpoint_max': 20, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': '/content/gdrive/My Drive/TCC/T2T LibriSpeech/output/', 'use_tpu': False, 't2t_device_info': {'num_async_replicas': 1}, 'data_parallelism': <tensor2tensor.utils.expert_utils.Parallelism object at 0x7f0aa908af28>}
W0828 15:46:37.174193 139684777486208 model_fn.py:630] Estimator's model_fn (<function T2TModel.make_estimator_model_fn.<locals>.wrapping_model_fn at 0x7f0aa9087ae8>) includes params argument, but params are not passed to Estimator.
W0828 15:46:37.174434 139684777486208 trainer_lib.py:783] ValidationMonitor only works with --schedule=train_and_evaluate
I0828 15:46:37.185815 139684777486208 estimator_training.py:186] Not using Distribute Coordinator.
I0828 15:46:37.186260 139684777486208 training.py:612] Running training and evaluation locally (non-distributed).
I0828 15:46:37.186565 139684777486208 training.py:700] Start train and evaluate loop. The evaluate will happen after every checkpoint. Checkpoint frequency is determined based on RunConfig arguments: save_checkpoints_steps 1000 or save_checkpoints_secs None.
E0828 15:46:37.192399 139684777486208 checkpoint_management.py:348] Couldn't match files for checkpoint /content/gdrive/My Drive/TCC/T2T LibriSpeech/output/model.ckpt-13000
W0828 15:46:37.197599 139684777486208 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts.
I0828 15:46:37.208258 139684777486208 problem.py:644] Reading data files from /content/gdrive/My Drive/TCC/T2T LibriSpeech/data/librispeech_clean_small-train*
I0828 15:46:37.229276 139684777486208 problem.py:670] partition: 0 num_data_files: 100
W0828 15:46:37.232276 139684777486208 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/data_generators/problem.py:680: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.experimental.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_determinstic`.
W0828 15:46:37.275019 139684777486208 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/layers/common_audio.py:92: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0828 15:46:37.562360 139684777486208 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/layers/common_audio.py:115: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.cast` instead.
W0828 15:46:37.750620 139684777486208 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/data_reader.py:275: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and:
`tf.data.TFRecordDataset(path)`
W0828 15:46:38.267626 139684777486208 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/data_reader.py:395: DatasetV1.output_shapes (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.data.get_output_shapes(dataset)`.
W0828 15:46:38.267972 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/data_reader.py:398: The name tf.logging.warning is deprecated. Please use tf.compat.v1.logging.warning instead.
W0828 15:46:38.268058 139684777486208 data_reader.py:399] Shapes are not fully defined. Assuming batch_size means tokens.
W0828 15:46:38.323740 139684777486208 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/data/experimental/ops/grouping.py:193: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
W0828 15:46:38.372743 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/data_reader.py:231: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.
I0828 15:46:38.437698 139684777486208 estimator.py:1145] Calling model_fn.
I0828 15:46:38.450161 139684777486208 t2t_model.py:2248] Setting T2TModel mode to 'train'
W0828 15:46:38.529374 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/t2t_model.py:244: The name tf.summary.text is deprecated. Please use tf.compat.v1.summary.text instead.
I0828 15:46:39.269068 139684777486208 api.py:255] Using variable initializer: uniform_unit_scaling
I0828 15:46:39.718456 139684777486208 t2t_model.py:2248] Transforming feature 'inputs' with speech_recognition_modality.bottom
W0828 15:46:39.720613 139684777486208 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/layers/modalities.py:439: conv2d (from tensorflow.python.layers.convolutional) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.keras.layers.Conv2D` instead.
I0828 15:46:40.186799 139684777486208 t2t_model.py:2248] Transforming feature 'targets' with symbol_modality_256_384.targets_bottom
I0828 15:46:40.323158 139684777486208 t2t_model.py:2248] Building model body
W0828 15:46:40.388057 139684777486208 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/models/transformer.py:96: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
W0828 15:46:40.435504 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/layers/common_layers.py:3077: The name tf.layers.Dense is deprecated. Please use tf.compat.v1.layers.Dense instead.
W0828 15:46:40.844527 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/layers/common_attention.py:1249: The name tf.summary.image is deprecated. Please use tf.compat.v1.summary.image instead.
I0828 15:46:48.565067 139684777486208 t2t_model.py:2248] Transforming body output with symbol_modality_256_384.top
W0828 15:46:48.689695 139684777486208 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/learning_rate.py:120: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead.
I0828 15:46:48.691083 139684777486208 learning_rate.py:29] Base learning rate: 2.000000
I0828 15:46:48.704310 139684777486208 optimize.py:338] Trainable Variables Total size: 70343552
I0828 15:46:48.704722 139684777486208 optimize.py:338] Non-trainable variables Total size: 5
I0828 15:46:48.705073 139684777486208 optimize.py:193] Using optimizer adam
I0828 15:47:00.715373 139684777486208 estimator.py:1147] Done calling model_fn.
I0828 15:47:00.717198 139684777486208 basic_session_run_hooks.py:541] Create CheckpointSaverHook.
I0828 15:47:05.476253 139684777486208 monitored_session.py:240] Graph was finalized.
2019-08-28 15:47:05.480538: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200000000 Hz
2019-08-28 15:47:05.480819: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x22a5640 executing computations on platform Host. Devices:
2019-08-28 15:47:05.480857: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): <undefined>, <undefined>
W0828 15:47:05.483572 139684777486208 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py:1276: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.
Instructions for updating:
Use standard file APIs to check for files with this prefix.
Traceback (most recent call last):
File "/usr/local/bin/t2t-trainer", line 33, in <module>
tf.app.run()
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/platform/app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "/usr/local/lib/python3.6/dist-packages/absl/app.py", line 300, in run
_run_main(main, args)
File "/usr/local/lib/python3.6/dist-packages/absl/app.py", line 251, in _run_main
sys.exit(main(argv))
File "/usr/local/bin/t2t-trainer", line 28, in main
t2t_trainer.main(argv)
File "/usr/local/lib/python3.6/dist-packages/tensor2tensor/bin/t2t_trainer.py", line 412, in main
execute_schedule(exp)
File "/usr/local/lib/python3.6/dist-packages/tensor2tensor/bin/t2t_trainer.py", line 367, in execute_schedule
getattr(exp, FLAGS.schedule)()
File "/usr/local/lib/python3.6/dist-packages/tensor2tensor/utils/trainer_lib.py", line 456, in continuous_train_and_eval
self._eval_spec)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/training.py", line 473, in train_and_evaluate
return executor.run()
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/training.py", line 613, in run
return self.run_local()
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/training.py", line 714, in run_local
saving_listeners=saving_listeners)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 367, in train
loss = self._train_model(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1158, in _train_model
return self._train_model_default(input_fn, hooks, saving_listeners)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1192, in _train_model_default
saving_listeners)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_estimator/python/estimator/estimator.py", line 1480, in _train_with_estimator_spec
log_step_count_steps=log_step_count_steps) as mon_sess:
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py", line 584, in MonitoredTrainingSession
stop_grace_period_secs=stop_grace_period_secs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py", line 1007, in __init__
stop_grace_period_secs=stop_grace_period_secs)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py", line 725, in __init__
self._sess = _RecoverableSession(self._coordinated_creator)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py", line 1200, in __init__
_WrappedSession.__init__(self, self._create_session())
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py", line 1205, in _create_session
return self._sess_creator.create_session()
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py", line 871, in create_session
self.tf_sess = self._session_creator.create_session()
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/monitored_session.py", line 647, in create_session
init_fn=self._scaffold.init_fn)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/session_manager.py", line 290, in prepare_session
config=config)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/session_manager.py", line 220, in _restore_checkpoint
saver.restore(sess, ckpt.model_checkpoint_path)
File "/usr/local/lib/python3.6/dist-packages/tensorflow/python/training/saver.py", line 1278, in restore
compat.as_text(save_path))
ValueError: The passed save_path is not a valid checkpoint: /content/gdrive/My Drive/TCC/T2T LibriSpeech/output/model.ckpt-13000
```
| open | 2019-08-28T15:55:05Z | 2019-09-20T09:56:14Z | https://github.com/tensorflow/tensor2tensor/issues/1683 | [] | Victor-Almeida | 1 |
Kanaries/pygwalker | pandas | 538 | [BUG] pygwalker bug report | Error Message
Failed to load model class 'BoxModel' from module '@jupyter-widgets/controls'
ChunkLoadError: Loading chunk 345 failed.
Difficult to decode such an error message.
| closed | 2024-05-03T15:46:43Z | 2024-05-17T15:50:18Z | https://github.com/Kanaries/pygwalker/issues/538 | [
"bug"
] | updiversity | 1 |
jmcnamara/XlsxWriter | pandas | 942 | how to set the glow effect to the line | ### Question
hello, I have searched this question in docs and
issues, but, I can't find how to set.
Is this function is available?
thanks. | closed | 2023-01-16T02:05:46Z | 2023-01-16T11:02:38Z | https://github.com/jmcnamara/XlsxWriter/issues/942 | [
"question"
] | youth54 | 3 |
CorentinJ/Real-Time-Voice-Cloning | python | 1,285 | Failed to build webrtcvad when installing a package | Hi, sorry for bothering but I had and an issue trying to install a package with pip, I don't know if maybe it's an error I'm making but I haven't been able to solve it.
This is what I was trying to install and the error that appeared
```
pip install ffsubsync
Defaulting to user installation because normal site-packages is not writeable
Collecting ffsubsync
Using cached ffsubsync-0.4.25-py2.py3-none-any.whl (36 kB)
Collecting auditok==0.1.5 (from ffsubsync)
Using cached auditok-0.1.5-py3-none-any.whl
Requirement already satisfied: charset-normalizer in /usr/lib/python3.12/site-packages (from ffsubsync) (3.2.0)
Collecting faust-cchardet (from ffsubsync)
Obtaining dependency information for faust-cchardet from https://files.pythonhosted.org/packages/81/33/a705c39e89b7ca7564b90c1a4ab4d4c2c0534cde911191d87a89b87b6c60/faust_cchardet-2.1.19-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata
Using cached faust_cchardet-2.1.19-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (8.3 kB)
Collecting ffmpeg-python (from ffsubsync)
Using cached ffmpeg_python-0.2.0-py3-none-any.whl (25 kB)
Collecting future>=0.18.2 (from ffsubsync)
Using cached future-0.18.3-py3-none-any.whl
Collecting numpy>=1.12.0 (from ffsubsync)
Obtaining dependency information for numpy>=1.12.0 from https://files.pythonhosted.org/packages/c4/c6/f971d43a272e574c21707c64f12730c390f2bfa6426185fbdf0265a63cbd/numpy-1.26.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata
Using cached numpy-1.26.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)
Collecting rich (from ffsubsync)
Obtaining dependency information for rich from https://files.pythonhosted.org/packages/be/be/1520178fa01eabe014b16e72a952b9f900631142ccd03dc36cf93e30c1ce/rich-13.7.0-py3-none-any.whl.metadata
Using cached rich-13.7.0-py3-none-any.whl.metadata (18 kB)
Requirement already satisfied: six in /usr/lib/python3.12/site-packages (from ffsubsync) (1.16.0)
Collecting srt>=3.0.0 (from ffsubsync)
Using cached srt-3.5.3-py3-none-any.whl
Collecting tqdm (from ffsubsync)
Obtaining dependency information for tqdm from https://files.pythonhosted.org/packages/00/e5/f12a80907d0884e6dff9c16d0c0114d81b8cd07dc3ae54c5e962cc83037e/tqdm-4.66.1-py3-none-any.whl.metadata
Using cached tqdm-4.66.1-py3-none-any.whl.metadata (57 kB)
Collecting typing-extensions (from ffsubsync)
Obtaining dependency information for typing-extensions from https://files.pythonhosted.org/packages/b7/f4/6a90020cd2d93349b442bfcb657d0dc91eee65491600b2cb1d388bc98e6b/typing_extensions-4.9.0-py3-none-any.whl.metadata
Using cached typing_extensions-4.9.0-py3-none-any.whl.metadata (3.0 kB)
Collecting webrtcvad (from ffsubsync)
Using cached webrtcvad-2.0.10.tar.gz (66 kB)
Installing build dependencies ... done
Getting requirements to build wheel ... done
Preparing metadata (pyproject.toml) ... done
Collecting chardet (from ffsubsync)
Obtaining dependency information for chardet from https://files.pythonhosted.org/packages/38/6f/f5fbc992a329ee4e0f288c1fe0e2ad9485ed064cac731ed2fe47dcc38cbf/chardet-5.2.0-py3-none-any.whl.metadata
Using cached chardet-5.2.0-py3-none-any.whl.metadata (3.4 kB)
Collecting pysubs2>=1.2.0 (from ffsubsync)
Using cached pysubs2-1.6.1-py3-none-any.whl (35 kB)
Collecting markdown-it-py>=2.2.0 (from rich->ffsubsync)
Obtaining dependency information for markdown-it-py>=2.2.0 from https://files.pythonhosted.org/packages/42/d7/1ec15b46af6af88f19b8e5ffea08fa375d433c998b8a7639e76935c14f1f/markdown_it_py-3.0.0-py3-none-any.whl.metadata
Using cached markdown_it_py-3.0.0-py3-none-any.whl.metadata (6.9 kB)
Collecting pygments<3.0.0,>=2.13.0 (from rich->ffsubsync)
Obtaining dependency information for pygments<3.0.0,>=2.13.0 from https://files.pythonhosted.org/packages/97/9c/372fef8377a6e340b1704768d20daaded98bf13282b5327beb2e2fe2c7ef/pygments-2.17.2-py3-none-any.whl.metadata
Using cached pygments-2.17.2-py3-none-any.whl.metadata (2.6 kB)
Collecting mdurl~=0.1 (from markdown-it-py>=2.2.0->rich->ffsubsync)
Using cached mdurl-0.1.2-py3-none-any.whl (10.0 kB)
Using cached numpy-1.26.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.0 MB)
Using cached chardet-5.2.0-py3-none-any.whl (199 kB)
Using cached faust_cchardet-2.1.19-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (317 kB)
Using cached rich-13.7.0-py3-none-any.whl (240 kB)
Using cached tqdm-4.66.1-py3-none-any.whl (78 kB)
Using cached typing_extensions-4.9.0-py3-none-any.whl (32 kB)
Using cached markdown_it_py-3.0.0-py3-none-any.whl (87 kB)
Using cached pygments-2.17.2-py3-none-any.whl (1.2 MB)
Building wheels for collected packages: webrtcvad
Building wheel for webrtcvad (pyproject.toml) ... error
error: subprocess-exited-with-error
× Building wheel for webrtcvad (pyproject.toml) did not run successfully.
│ exit code: 1
╰─> [20 lines of output]
running bdist_wheel
running build
running build_py
creating build
creating build/lib.linux-x86_64-cpython-312
copying webrtcvad.py -> build/lib.linux-x86_64-cpython-312
running build_ext
building '_webrtcvad' extension
creating build/temp.linux-x86_64-cpython-312
creating build/temp.linux-x86_64-cpython-312/cbits
creating build/temp.linux-x86_64-cpython-312/cbits/webrtc
creating build/temp.linux-x86_64-cpython-312/cbits/webrtc/common_audio
creating build/temp.linux-x86_64-cpython-312/cbits/webrtc/common_audio/signal_processing
creating build/temp.linux-x86_64-cpython-312/cbits/webrtc/common_audio/vad
gcc -fno-strict-overflow -Wsign-compare -DDYNAMIC_ANNOTATIONS_ENABLED=1 -DNDEBUG -fexceptions -fcf-protection -fexceptions -fcf-protection -fexceptions -fcf-protection -fPIC -DWEBRTC_POSIX -Icbits -I/usr/include/python3.12 -c cbits/pywebrtcvad.c -o build/temp.linux-x86_64-cpython-312/cbits/pywebrtcvad.o
cbits/pywebrtcvad.c:1:10: fatal error: Python.h: No such file or directory
1 | #include <Python.h>
| ^~~~~~~~~~
compilation terminated.
error: command '/usr/bin/gcc' failed with exit code 1
[end of output]
note: This error originates from a subprocess, and is likely not a problem with pip.
ERROR: Failed building wheel for webrtcvad
Failed to build webrtcvad
ERROR: Could not build wheels for webrtcvad, which is required to install pyproject.toml-based projects
```
OS: Fedora Linux (KDE Desktop environment) | closed | 2024-01-15T01:51:04Z | 2024-02-07T19:13:26Z | https://github.com/CorentinJ/Real-Time-Voice-Cloning/issues/1285 | [] | emanuelps2708 | 3 |
graphql-python/graphene-django | graphql | 533 | How to use Django models which have no "name" attribute? | **Do my Django model has to have "name" attribute??? Can I override is somehow?** I have some Django models which don't have "name" attribute. The don't work :( Only those which has "name" attribute work and I can query them with GraphiQL.:/
> ImportError at /graphql
> Could not import 'myproject.schema.schema' for Graphene setting 'SCHEMA'. AttributeError: type object 'MyModel' has no attribute 'name'. | closed | 2018-10-12T14:47:38Z | 2018-10-12T15:14:26Z | https://github.com/graphql-python/graphene-django/issues/533 | [] | ghost | 1 |
coqui-ai/TTS | pytorch | 4,007 | [ I have Vietnamese voice data] Please provide support for Vietnamese | ## Support for Vietnamese Language
- Hi, I found 100 hours of voice data from VinBigdata - https://institute.vinbigdata.org/events/vinbigdata-chia-se-100-gio-du-lieu-tieng-noi-cho-cong-dong/
- Here is the download link https://drive.google.com/file/d/1vUSxdORDxk-ePUt-bUVDahpoXiqKchMx/view?usp=sharing
- Also there are also about 200 hours of data from Mozzila - https://commonvoice.mozilla.org/en/datasets (choose Vietnamese then download)
Hope this will help. Thanks for your work.
| closed | 2024-10-05T17:41:54Z | 2025-01-03T08:48:49Z | https://github.com/coqui-ai/TTS/issues/4007 | [
"wontfix",
"feature request"
] | tuanpham09 | 2 |
coqui-ai/TTS | pytorch | 2,879 | doesn't work...no way. please help, windows11, miniconda[Bug] | ### Describe the bug
after many problems to install it, finally it installed...but can't use it.
why so many problems using it on windows?
I saw people installing it in 2 min on linux.
I need it for work, it is making me crazy.
Please help me to make it works.
I must create batch file to make it tts many files, but actually I am unable to make it tts one file.
I am wasting weeks trying to solve all conflicts about this software.
I need help.
### To Reproduce
(TTS) C:\Users\Administrator>tts --text "Text for TTS" --out_path output/path/speech.wav
> tts_models/en/ljspeech/tacotron2-DDC is already downloaded.
> vocoder_models/en/ljspeech/hifigan_v2 is already downloaded.
> Using model: Tacotron2
> Setting up Audio Processor...
| > sample_rate:22050
| > resample:False
| > num_mels:80
| > log_func:np.log
| > min_level_db:-100
| > frame_shift_ms:None
| > frame_length_ms:None
| > ref_level_db:20
| > fft_size:1024
| > power:1.5
| > preemphasis:0.0
| > griffin_lim_iters:60
| > signal_norm:False
| > symmetric_norm:True
| > mel_fmin:0
| > mel_fmax:8000.0
| > pitch_fmin:1.0
| > pitch_fmax:640.0
| > spec_gain:1.0
| > stft_pad_mode:reflect
| > max_norm:4.0
| > clip_norm:True
| > do_trim_silence:True
| > trim_db:60
| > do_sound_norm:False
| > do_amp_to_db_linear:True
| > do_amp_to_db_mel:True
| > do_rms_norm:False
| > db_level:None
| > stats_path:None
| > base:2.718281828459045
| > hop_length:256
| > win_length:1024
> Model's reduction rate `r` is set to: 1
> Vocoder Model: hifigan
> Setting up Audio Processor...
| > sample_rate:22050
| > resample:False
| > num_mels:80
| > log_func:np.log
| > min_level_db:-100
| > frame_shift_ms:None
| > frame_length_ms:None
| > ref_level_db:20
| > fft_size:1024
| > power:1.5
| > preemphasis:0.0
| > griffin_lim_iters:60
| > signal_norm:False
| > symmetric_norm:True
| > mel_fmin:0
| > mel_fmax:8000.0
| > pitch_fmin:1.0
| > pitch_fmax:640.0
| > spec_gain:1.0
| > stft_pad_mode:reflect
| > max_norm:4.0
| > clip_norm:True
| > do_trim_silence:False
| > trim_db:60
| > do_sound_norm:False
| > do_amp_to_db_linear:True
| > do_amp_to_db_mel:True
| > do_rms_norm:False
| > db_level:None
| > stats_path:None
| > base:2.718281828459045
| > hop_length:256
| > win_length:1024
> Generator Model: hifigan_generator
> Discriminator Model: hifigan_discriminator
Removing weight norm...
> Text: Text for TTS
> Text splitted to sentences.
['Text for TTS']
> Processing time: 0.4076573848724365
> Real-time factor: 0.23714767139186432
> Saving output to output/path/speech.wav
Traceback (most recent call last):
File "C:\Users\shula\miniconda3\envs\TTS\lib\runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\shula\miniconda3\envs\TTS\lib\runpy.py", line 86, in _run_code
exec(code, run_globals)
File "C:\Users\shula\miniconda3\envs\TTS\Scripts\tts.exe\__main__.py", line 7, in <module>
File "C:\Users\shula\miniconda3\envs\TTS\lib\site-packages\TTS\bin\synthesize.py", line 451, in main
synthesizer.save_wav(wav, args.out_path)
File "C:\Users\shula\miniconda3\envs\TTS\lib\site-packages\TTS\utils\synthesizer.py", line 244, in save_wav
save_wav(wav=wav, path=path, sample_rate=self.output_sample_rate)
File "C:\Users\shula\miniconda3\envs\TTS\lib\site-packages\TTS\utils\audio\numpy_transforms.py", line 439, in save_wav
scipy.io.wavfile.write(path, sample_rate, wav_norm.astype(np.int16))
File "C:\Users\shula\miniconda3\envs\TTS\lib\site-packages\scipy\io\wavfile.py", line 767, in write
fid = open(filename, 'wb')
FileNotFoundError: [Errno 2] No such file or directory: 'output/path/speech.wav'
### Expected behavior
i made the most simple test, it failed
### Logs
_No response_
### Environment
```shell
(TTS) C:\Users\Administrator>conda list
# packages in environment at C:\Users\shula\miniconda3\envs\TTS:
#
# Name Version Build Channel
absl-py 1.4.0 pypi_0 pypi
accelerate 0.21.0 pypi_0 pypi
aiohttp 3.8.5 pypi_0 pypi
aiosignal 1.3.1 pypi_0 pypi
anyascii 0.3.2 pypi_0 pypi
appdirs 1.4.4 pypi_0 pypi
async-timeout 4.0.3 pypi_0 pypi
attrs 23.1.0 pypi_0 pypi
audioread 3.0.0 pypi_0 pypi
babel 2.12.1 pypi_0 pypi
bangla 0.0.2 pypi_0 pypi
blinker 1.6.2 pypi_0 pypi
bnnumerizer 0.0.2 pypi_0 pypi
bnunicodenormalizer 0.1.1 pypi_0 pypi
boltons 23.0.0 pypi_0 pypi
bzip2 1.0.8 he774522_0
ca-certificates 2023.05.30 haa95532_0
cachetools 5.3.1 pypi_0 pypi
certifi 2023.7.22 pypi_0 pypi
cffi 1.15.1 pypi_0 pypi
charset-normalizer 3.2.0 pypi_0 pypi
clean-fid 0.1.35 pypi_0 pypi
click 8.1.6 pypi_0 pypi
clip-anytorch 2.5.2 pypi_0 pypi
colorama 0.4.6 pypi_0 pypi
contourpy 1.1.0 pypi_0 pypi
coqpit 0.0.17 pypi_0 pypi
cycler 0.11.0 pypi_0 pypi
cython 0.29.30 pypi_0 pypi
dateparser 1.1.8 pypi_0 pypi
decorator 5.1.1 pypi_0 pypi
docker-pycreds 0.4.0 pypi_0 pypi
docopt 0.6.2 pypi_0 pypi
einops 0.6.1 pypi_0 pypi
encodec 0.1.1 pypi_0 pypi
filelock 3.12.2 pypi_0 pypi
flask 2.3.2 pypi_0 pypi
fonttools 4.42.0 pypi_0 pypi
frozenlist 1.4.0 pypi_0 pypi
fsspec 2023.6.0 pypi_0 pypi
ftfy 6.1.1 pypi_0 pypi
g2pkk 0.1.2 pypi_0 pypi
gitdb 4.0.10 pypi_0 pypi
gitpython 3.1.32 pypi_0 pypi
google-auth 2.22.0 pypi_0 pypi
google-auth-oauthlib 1.0.0 pypi_0 pypi
grpcio 1.57.0 pypi_0 pypi
gruut 2.2.3 pypi_0 pypi
gruut-ipa 0.13.0 pypi_0 pypi
gruut-lang-de 2.0.0 pypi_0 pypi
gruut-lang-en 2.0.0 pypi_0 pypi
gruut-lang-es 2.0.0 pypi_0 pypi
gruut-lang-fr 2.0.2 pypi_0 pypi
huggingface-hub 0.16.4 pypi_0 pypi
idna 3.4 pypi_0 pypi
imageio 2.31.1 pypi_0 pypi
inflect 5.6.0 pypi_0 pypi
itsdangerous 2.1.2 pypi_0 pypi
jamo 0.4.1 pypi_0 pypi
jieba 0.42.1 pypi_0 pypi
jinja2 3.1.2 pypi_0 pypi
joblib 1.3.2 pypi_0 pypi
jsonlines 1.2.0 pypi_0 pypi
jsonmerge 1.9.2 pypi_0 pypi
jsonschema 4.19.0 pypi_0 pypi
jsonschema-specifications 2023.7.1 pypi_0 pypi
k-diffusion 0.0.15 pypi_0 pypi
kiwisolver 1.4.4 pypi_0 pypi
kornia 0.7.0 pypi_0 pypi
lazy-loader 0.3 pypi_0 pypi
libffi 3.4.4 hd77b12b_0
librosa 0.10.0 pypi_0 pypi
llvmlite 0.40.1 pypi_0 pypi
markdown 3.4.4 pypi_0 pypi
markupsafe 2.1.3 pypi_0 pypi
matplotlib 3.7.2 pypi_0 pypi
mpmath 1.3.0 pypi_0 pypi
msgpack 1.0.5 pypi_0 pypi
multidict 6.0.4 pypi_0 pypi
networkx 2.8.8 pypi_0 pypi
nltk 3.8.1 pypi_0 pypi
num2words 0.5.12 pypi_0 pypi
numba 0.57.0 pypi_0 pypi
numpy 1.23.0 pypi_0 pypi
oauthlib 3.2.2 pypi_0 pypi
openssl 3.0.10 h2bbff1b_0
packaging 23.1 pypi_0 pypi
pandas 2.0.3 pypi_0 pypi
pathtools 0.1.2 pypi_0 pypi
pillow 10.0.0 pypi_0 pypi
pip 23.2.1 py310haa95532_0
platformdirs 3.10.0 pypi_0 pypi
pooch 1.7.0 pypi_0 pypi
protobuf 4.24.0 pypi_0 pypi
psutil 5.9.5 pypi_0 pypi
pyasn1 0.5.0 pypi_0 pypi
pyasn1-modules 0.3.0 pypi_0 pypi
pycparser 2.21 pypi_0 pypi
pynndescent 0.5.10 pypi_0 pypi
pyparsing 3.0.9 pypi_0 pypi
pypinyin 0.49.0 pypi_0 pypi
pysbd 0.3.4 pypi_0 pypi
python 3.10.12 he1021f5_0
python-crfsuite 0.9.9 pypi_0 pypi
python-dateutil 2.8.2 pypi_0 pypi
pytz 2023.3 pypi_0 pypi
pywavelets 1.4.1 pypi_0 pypi
pyyaml 6.0.1 pypi_0 pypi
referencing 0.30.2 pypi_0 pypi
regex 2023.8.8 pypi_0 pypi
requests 2.31.0 pypi_0 pypi
requests-oauthlib 1.3.1 pypi_0 pypi
resize-right 0.0.2 pypi_0 pypi
rpds-py 0.9.2 pypi_0 pypi
rsa 4.9 pypi_0 pypi
safetensors 0.3.2 pypi_0 pypi
scikit-image 0.21.0 pypi_0 pypi
scikit-learn 1.3.0 pypi_0 pypi
scipy 1.11.1 pypi_0 pypi
sentry-sdk 1.29.2 pypi_0 pypi
setproctitle 1.3.2 pypi_0 pypi
setuptools 68.0.0 py310haa95532_0
six 1.16.0 pypi_0 pypi
smmap 5.0.0 pypi_0 pypi
soundfile 0.12.1 pypi_0 pypi
soxr 0.3.5 pypi_0 pypi
sqlite 3.41.2 h2bbff1b_0
sympy 1.12 pypi_0 pypi
tensorboard 2.14.0 pypi_0 pypi
tensorboard-data-server 0.7.1 pypi_0 pypi
threadpoolctl 3.2.0 pypi_0 pypi
tifffile 2023.7.18 pypi_0 pypi
tk 8.6.12 h2bbff1b_0
tokenizers 0.13.3 pypi_0 pypi
torch 2.0.1 pypi_0 pypi
torchaudio 2.0.2 pypi_0 pypi
torchdiffeq 0.2.3 pypi_0 pypi
torchsde 0.2.5 pypi_0 pypi
torchvision 0.15.2 pypi_0 pypi
tqdm 4.66.1 pypi_0 pypi
trainer 0.0.30 pypi_0 pypi
trampoline 0.1.2 pypi_0 pypi
transformers 4.31.0 pypi_0 pypi
tts 0.16.1 pypi_0 pypi
typing-extensions 4.7.1 pypi_0 pypi
tzdata 2023.3 pypi_0 pypi
tzlocal 5.0.1 pypi_0 pypi
umap-learn 0.5.1 pypi_0 pypi
urllib3 1.26.16 pypi_0 pypi
vc 14.2 h21ff451_1
vs2015_runtime 14.27.29016 h5e58377_2
wandb 0.15.8 pypi_0 pypi
wcwidth 0.2.6 pypi_0 pypi
werkzeug 2.3.6 pypi_0 pypi
wheel 0.38.4 py310haa95532_0
xz 5.4.2 h8cc25b3_0
yarl 1.9.2 pypi_0 pypi
zlib 1.2.13 h8cc25b3_0
```
### Additional context
DEPRECATION: torchsde 0.2.5 has a non-standard dependency specifier numpy>=1.19.*; python_version >= "3.7". pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of torchsde or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
Installing collected packages: mecab-python3
Successfully installed mecab-python3-1.0.6
(TTS) C:\Users\Administrator>pip install unidic-lite
Collecting unidic-lite
Using cached unidic-lite-1.0.8.tar.gz (47.4 MB)
Preparing metadata (setup.py) ... done
Building wheels for collected packages: unidic-lite
Building wheel for unidic-lite (setup.py) ... done
Created wheel for unidic-lite: filename=unidic_lite-1.0.8-py3-none-any.whl size=47658833 sha256=57d389c236768b40af598139cab9147bb1ceca437eac9c206ed9a8c85b85811f
Stored in directory: c:\users\administrator\appdata\local\pip\cache\wheels\89\e8\68\f9ac36b8cc6c8b3c96888cd57434abed96595d444f42243853
Successfully built unidic-lite
DEPRECATION: torchsde 0.2.5 has a non-standard dependency specifier numpy>=1.19.*; python_version >= "3.7". pip 23.3 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of torchsde or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063
Installing collected packages: unidic-lite
Successfully installed unidic-lite-1.0.8
(TTS) C:\Users\Administrator>tts --text "Text for TTS" --out_path output/path/speech.wav
> Downloading model to C:\Users\Administrator\AppData\Local\tts\tts_models--en--ljspeech--tacotron2-DDC
100%|██████████████████████████████████████████████████████████████████████████████| 113M/113M [01:25<00:00, 1.31MiB/s]
> Model's license - apache 2.0
> Check https://choosealicense.com/licenses/apache-2.0/ for more info.
> Downloading model to C:\Users\Administrator\AppData\Local\tts\vocoder_models--en--ljspeech--hifigan_v2
100%|█████████████████████████████████████████████████████████████████████████████| 3.80M/3.80M [00:05<00:00, 712kiB/s]
> Model's license - apache 2.0
> Check https://choosealicense.com/licenses/apache-2.0/ for more info.
> Using model: Tacotron2
> Setting up Audio Processor...
| > sample_rate:22050
| > resample:False
| > num_mels:80
| > log_func:np.log
| > min_level_db:-100
| > frame_shift_ms:None
| > frame_length_ms:None
| > ref_level_db:20
| > fft_size:1024
| > power:1.5
| > preemphasis:0.0
| > griffin_lim_iters:60
| > signal_norm:False
| > symmetric_norm:True
| > mel_fmin:0
| > mel_fmax:8000.0
| > pitch_fmin:1.0
| > pitch_fmax:640.0
| > spec_gain:1.0
| > stft_pad_mode:reflect
| > max_norm:4.0
| > clip_norm:True
| > do_trim_silence:True
| > trim_db:60
| > do_sound_norm:False
| > do_amp_to_db_linear:True
| > do_amp_to_db_mel:True
| > do_rms_norm:False
| > db_level:None
| > stats_path:None
| > base:2.718281828459045
| > hop_length:256
| > win_length:1024
> Model's reduction rate `r` is set to: 1
> Vocoder Model: hifigan
> Setting up Audio Processor...
| > sample_rate:22050
| > resample:False
| > num_mels:80
| > log_func:np.log
| > min_level_db:-100
| > frame_shift_ms:None
| > frame_length_ms:None
| > ref_level_db:20
| > fft_size:1024
| > power:1.5
| > preemphasis:0.0
| > griffin_lim_iters:60
| > signal_norm:False
| > symmetric_norm:True
| > mel_fmin:0
| > mel_fmax:8000.0
| > pitch_fmin:1.0
| > pitch_fmax:640.0
| > spec_gain:1.0
| > stft_pad_mode:reflect
| > max_norm:4.0
| > clip_norm:True
| > do_trim_silence:False
| > trim_db:60
| > do_sound_norm:False
| > do_amp_to_db_linear:True
| > do_amp_to_db_mel:True
| > do_rms_norm:False
| > db_level:None
| > stats_path:None
| > base:2.718281828459045
| > hop_length:256
| > win_length:1024
> Generator Model: hifigan_generator
> Discriminator Model: hifigan_discriminator
Removing weight norm...
> Text: Text for TTS
> Text splitted to sentences.
['Text for TTS']
> Processing time: 0.5023317337036133
> Real-time factor: 0.298266230293103
> Saving output to output/path/speech.wav
Traceback (most recent call last):
File "C:\Users\shula\miniconda3\envs\TTS\lib\runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\shula\miniconda3\envs\TTS\lib\runpy.py", line 86, in _run_code
exec(code, run_globals)
File "C:\Users\shula\miniconda3\envs\TTS\Scripts\tts.exe\__main__.py", line 7, in <module>
File "C:\Users\shula\miniconda3\envs\TTS\lib\site-packages\TTS\bin\synthesize.py", line 451, in main
synthesizer.save_wav(wav, args.out_path)
File "C:\Users\shula\miniconda3\envs\TTS\lib\site-packages\TTS\utils\synthesizer.py", line 244, in save_wav
save_wav(wav=wav, path=path, sample_rate=self.output_sample_rate)
File "C:\Users\shula\miniconda3\envs\TTS\lib\site-packages\TTS\utils\audio\numpy_transforms.py", line 439, in save_wav scipy.io.wavfile.write(path, sample_rate, wav_norm.astype(np.int16))
File "C:\Users\shula\miniconda3\envs\TTS\lib\site-packages\scipy\io\wavfile.py", line 767, in write
fid = open(filename, 'wb')
FileNotFoundError: [Errno 2] No such file or directory: 'output/path/speech.wav'
(TTS) C:\Users\Administrator>tts --text "Text for TTS" --out_path output/path/speech.wav
> tts_models/en/ljspeech/tacotron2-DDC is already downloaded.
> vocoder_models/en/ljspeech/hifigan_v2 is already downloaded.
> Using model: Tacotron2
> Setting up Audio Processor...
| > sample_rate:22050
| > resample:False
| > num_mels:80
| > log_func:np.log
| > min_level_db:-100
| > frame_shift_ms:None
| > frame_length_ms:None
| > ref_level_db:20
| > fft_size:1024
| > power:1.5
| > preemphasis:0.0
| > griffin_lim_iters:60
| > signal_norm:False
| > symmetric_norm:True
| > mel_fmin:0
| > mel_fmax:8000.0
| > pitch_fmin:1.0
| > pitch_fmax:640.0
| > spec_gain:1.0
| > stft_pad_mode:reflect
| > max_norm:4.0
| > clip_norm:True
| > do_trim_silence:True
| > trim_db:60
| > do_sound_norm:False
| > do_amp_to_db_linear:True
| > do_amp_to_db_mel:True
| > do_rms_norm:False
| > db_level:None
| > stats_path:None
| > base:2.718281828459045
| > hop_length:256
| > win_length:1024
> Model's reduction rate `r` is set to: 1
> Vocoder Model: hifigan
> Setting up Audio Processor...
| > sample_rate:22050
| > resample:False
| > num_mels:80
| > log_func:np.log
| > min_level_db:-100
| > frame_shift_ms:None
| > frame_length_ms:None
| > ref_level_db:20
| > fft_size:1024
| > power:1.5
| > preemphasis:0.0
| > griffin_lim_iters:60
| > signal_norm:False
| > symmetric_norm:True
| > mel_fmin:0
| > mel_fmax:8000.0
| > pitch_fmin:1.0
| > pitch_fmax:640.0
| > spec_gain:1.0
| > stft_pad_mode:reflect
| > max_norm:4.0
| > clip_norm:True
| > do_trim_silence:False
| > trim_db:60
| > do_sound_norm:False
| > do_amp_to_db_linear:True
| > do_amp_to_db_mel:True
| > do_rms_norm:False
| > db_level:None
| > stats_path:None
| > base:2.718281828459045
| > hop_length:256
| > win_length:1024
> Generator Model: hifigan_generator
> Discriminator Model: hifigan_discriminator
Removing weight norm...
> Text: Text for TTS
> Text splitted to sentences.
['Text for TTS']
> Processing time: 0.4537527561187744
> Real-time factor: 0.2712919813562629
> Saving output to output/path/speech.wav
Traceback (most recent call last):
File "C:\Users\shula\miniconda3\envs\TTS\lib\runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\shula\miniconda3\envs\TTS\lib\runpy.py", line 86, in _run_code
exec(code, run_globals)
File "C:\Users\shula\miniconda3\envs\TTS\Scripts\tts.exe\__main__.py", line 7, in <module>
File "C:\Users\shula\miniconda3\envs\TTS\lib\site-packages\TTS\bin\synthesize.py", line 451, in main
synthesizer.save_wav(wav, args.out_path)
File "C:\Users\shula\miniconda3\envs\TTS\lib\site-packages\TTS\utils\synthesizer.py", line 244, in save_wav
save_wav(wav=wav, path=path, sample_rate=self.output_sample_rate)
File "C:\Users\shula\miniconda3\envs\TTS\lib\site-packages\TTS\utils\audio\numpy_transforms.py", line 439, in save_wav scipy.io.wavfile.write(path, sample_rate, wav_norm.astype(np.int16))
File "C:\Users\shula\miniconda3\envs\TTS\lib\site-packages\scipy\io\wavfile.py", line 767, in write
fid = open(filename, 'wb')
FileNotFoundError: [Errno 2] No such file or directory: 'output/path/speech.wav'
| closed | 2023-08-20T14:00:05Z | 2023-08-21T08:19:56Z | https://github.com/coqui-ai/TTS/issues/2879 | [
"bug"
] | Zerthura | 0 |
gradio-app/gradio | machine-learning | 10,177 | New event to trigger when a tab is in view, not necessarily explicitly selected by the user | - [x] I have searched to see if a similar issue already exists.
**Is your feature request related to a problem? Please describe.**
While `gr.Tab` as the `select` event, it is only triggered when a user explicitly _selects_ the tab. What if the tab is selected by default and the user only has to select that tab in order to return to that tab after navigating away from it? Another event, such as `focus` or `default_selected` will be a good idea.
**Describe the solution you'd like**
Support a new event for tabs that is triggered when the tab is in view, not necessarily because the user selected it.
**Additional context**
To see what I mean, select the "Pydantic entity profiles" tab https://huggingface.co/spaces/anirbanbasu/gradio-experiments. By default, a sub-tab called "View (decorative)" is selected. However, this automatic selection does not trigger the `select` event on that tab. Thus the user will see something like this.
<img width="1975" alt="Screenshot 2024-12-11 at 16 27 26" src="https://github.com/user-attachments/assets/28ee7b8a-78a0-4a3b-ae65-1a7876ae8240">
Yet, if the user selects another sub-tab, e.g., "View (JSON)" and then selects "View (decorative)", the `select` event is triggered and the user gets to see something like the following.
<img width="1975" alt="Screenshot 2024-12-11 at 16 27 40" src="https://github.com/user-attachments/assets/f73fca00-fb11-452a-8334-f85c127110fa">
| closed | 2024-12-11T07:35:57Z | 2024-12-12T15:51:46Z | https://github.com/gradio-app/gradio/issues/10177 | [] | anirbanbasu | 4 |
plotly/dash | flask | 2,725 | Allow background callback tasks to programmatically retry later. | **Is your feature request related to a problem? Please describe.**
Background callbacks running in a distributed environment (Openshift or Kubernetes) can fail for reasons that are recoverable via application logic. e.g. a data resource isn't available at a point in time, but will be available in the future.
A bad solution is to have the background callback task check for a resource and `sleep` for some amount of time, then check again later, and repeat. This consumes the `Celery Worker` thread for no reason, and in our app, leads to worker pool exhaustion.
**Describe the solution you'd like**
It'd make sense for a background callback task to:
1. check whether it can execute given the current state,
2. proceed if it can,
3. re-enqueue itself if it can't, yielding the worker thread to be used by another task.
Since background callbacks are Celery tasks, the features to enable programatic retries are already available with the `bind` argument: a task receives a `self` parameter that can be instructed to retry.
This might look like the following pseudocode:
```python
@dash.callback(
... # Inputs and Outputs
background=True,
celery_bind=True, # first param to func must be for 'self'
retry_on_exceptions=[DBNotAvailableRetry],
)
def func(self, conn):
val = conn.get_value() # raises DBNotAvailableRetry exception
if not val:
self.retry(after="5s", exponential_backoff=True, jitter=True)
return val
```
**Describe alternatives you've considered**
Since Dash controls the context of the executing tasks when it's enqueued in Celery, the functionality of pushing the `self` parameter into the background callback arguments could be avoided if Dash instead implemented exception handling that would trigger retries when caught.
```python
@celery_app.task(
bind=True
)
def dash_bg_callback_wrapper(self, user_func, args):
try:
results = user_func(*args)
return results
except dash.BG_RETRY_EXCEPTION as e:
self.retry(
after=e.args["after"] # user could set this, knowing their app- would default to 0 time before retry.
)
```
| open | 2024-01-12T17:30:21Z | 2024-08-13T19:44:46Z | https://github.com/plotly/dash/issues/2725 | [
"feature",
"P3"
] | JamesKunstle | 10 |
huggingface/text-generation-inference | nlp | 2,994 | Request failed during generation: Server error: Value out of range: -29146814772 | ### System Info
text-generation-launcher 3.1.1-dev0
Single RTX 4070 S GPU
NVIDIA-SMI 572.16 Driver Version: 572.16 CUDA Version: 12.8
Models Used : meta-llama/Llama-3.1-8B-Instruct, Yujivus/DeepSeek-R1-Distill-Llama-8B-AWQ, Yujivus/Phi-4-Health-CoT-1.1-AWQ
Docker Command:
docker run --name tgi-server --gpus all -p 80:81 --network tgi -v volume:/data --env HUGGING_FACE_HUB_TOKEN=... ghcr.io/huggingface/text-generation-inference:latest --model-id Yujivus/Phi-4-Health-CoT-1.1-AWQ --quantize awq
(Quantize awq is used for only Yujivus/DeepSeek-R1-Distill-Llama-8B-AWQ and Yujivus/Phi-4-Health-CoT-1.1-AWQ models. For meta-llama/Llama-3.1-8B-Instruct, I tried eetq, bitsandbytes-nf4 and bitsandbytes-fp4. None of them worked.)
### Information
- [x] Docker
- [ ] The CLI directly
### Tasks
- [x] An officially supported command
- [ ] My own modifications
### Reproduction
I am running simple function for inference :
async def get_response(query):
"""Send a single request to the TGI server."""
try:
async with AsyncInferenceClient(base_url="http://tgi-server:80") as client:
output = await client.chat.completions.create(
model="tgi",
messages=[
{
"role": "system",
"content": "You are a helpful assistant\n\n",
},
{
"role": "user",
"content": query,
},
],
stream=False,
max_tokens=3000,
)
return output.choices[0].message.content
except Exception as e:
print(f"Error: {e}")
return None
Error : 2025-02-05 16:11:02 2025-02-05T13:11:02.646154Z ERROR text_generation_launcher: Method Decode encountered an error.
2025-02-05 16:11:02 Traceback (most recent call last):
2025-02-05 16:11:02 File "/opt/conda/bin/text-generation-server", line 10, in <module>
2025-02-05 16:11:02 sys.exit(app())
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/site-packages/typer/main.py", line 323, in __call__
2025-02-05 16:11:02 return get_command(self)(*args, **kwargs)
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/site-packages/click/core.py", line 1161, in __call__
2025-02-05 16:11:02 return self.main(*args, **kwargs)
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/site-packages/typer/core.py", line 743, in main
2025-02-05 16:11:02 return _main(
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/site-packages/typer/core.py", line 198, in _main
2025-02-05 16:11:02 rv = self.invoke(ctx)
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/site-packages/click/core.py", line 1697, in invoke
2025-02-05 16:11:02 return _process_result(sub_ctx.command.invoke(sub_ctx))
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/site-packages/click/core.py", line 1443, in invoke
2025-02-05 16:11:02 return ctx.invoke(self.callback, **ctx.params)
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/site-packages/click/core.py", line 788, in invoke
2025-02-05 16:11:02 return __callback(*args, **kwargs)
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/site-packages/typer/main.py", line 698, in wrapper
2025-02-05 16:11:02 return callback(**use_params)
2025-02-05 16:11:02 File "/usr/src/server/text_generation_server/cli.py", line 119, in serve
2025-02-05 16:11:02 server.serve(
2025-02-05 16:11:02 File "/usr/src/server/text_generation_server/server.py", line 315, in serve
2025-02-05 16:11:02 asyncio.run(
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/asyncio/runners.py", line 190, in run
2025-02-05 16:11:02 return runner.run(main)
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/asyncio/runners.py", line 118, in run
2025-02-05 16:11:02 return self._loop.run_until_complete(task)
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 641, in run_until_complete
2025-02-05 16:11:02 self.run_forever()
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 608, in run_forever
2025-02-05 16:11:02 self._run_once()
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/asyncio/base_events.py", line 1936, in _run_once
2025-02-05 16:11:02 handle._run()
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/asyncio/events.py", line 84, in _run
2025-02-05 16:11:02 self._context.run(self._callback, *self._args)
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/site-packages/grpc_interceptor/server.py", line 165, in invoke_intercept_method
2025-02-05 16:11:02 return await self.intercept(
2025-02-05 16:11:02 > File "/usr/src/server/text_generation_server/interceptor.py", line 24, in intercept
2025-02-05 16:11:02 return await response
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/site-packages/opentelemetry/instrumentation/grpc/_aio_server.py", line 120, in _unary_interceptor
2025-02-05 16:11:02 raise error
2025-02-05 16:11:02 File "/opt/conda/lib/python3.11/site-packages/opentelemetry/instrumentation/grpc/_aio_server.py", line 111, in _unary_interceptor
2025-02-05 16:11:02 return await behavior(request_or_iterator, context)
2025-02-05 16:11:02 File "/usr/src/server/text_generation_server/server.py", line 221, in Decode
2025-02-05 16:11:02 return generate_pb2.DecodeResponse(
2025-02-05 16:11:02 ValueError: Value out of range: -29146814772
2025-02-05 16:11:02 2025-02-05T13:11:02.646395Z ERROR batch{batch_size=1}:decode:decode{size=1}:decode{size=1}: text_generation_router_v3::client: backends/v3/src/client/mod.rs:45: Server error: Value out of range: -29146814772
2025-02-05 16:11:02 2025-02-05T13:11:02.647196Z ERROR chat_completions:generate:generate_stream:schedule:infer:send_error: text_generation_router_v3::backend: backends/v3/src/backend.rs:546: Request failed during generation: Server error: Value out of range: -29146814772
Only 40-50% of the requests to TGI return a successful response (for Yujivus/Phi-4-Health-CoT-1.1-AWQ), while the rest fail. I tried with several models but I encountered same error for all of them except smaller llama 3.2 1b and 3b models. I observed the highest success rate was when using the EETQ quantization method with the LLaMA 3.1 8B model. But still, I encountered this error oftenly.
The Phi-4 model is fine tuned with FreedomIntelligence/medical-o1-reasoning-SFT dataset. Since this dataset is CoT dataset, the model is talking too much. And I get more errors. If response takes longer tokens, I get more errors. Probably because for every token, there is a change for getting value of range error. It is maybe because of quantization, but to test this, I tried Llama 3.2 1b model with and without quantization and I didn't encounter this error. But still, maybe the error occurs for larger models, which perform much more computation, during the decode phase because quantization causes the values to exceed the expected range.
I tried with smaller max_tokens but probability of getting error did not change. Error occurs roughly for 1/2000 tokens.
If there is a way to fix, it would be good to know. Thanks.
### Expected behavior
2025-02-05 16:49:39 2025-02-05T13:49:39.259073Z INFO text_generation_router_v3::radix: backends/v3/src/radix.rs:108: Prefix 0 - Suffix 1045
2025-02-05 16:49:54 2025-02-05T13:49:54.901547Z INFO chat_completions{total_time="15.645054793s" validation_time="2.504013ms" queue_time="550.086µs" inference_time="15.642000896s" time_per_token="21.876924ms" seed="Some(15551805739611344766)"}: text_generation_router::server: router/src/server.rs:625: Success | open | 2025-02-05T14:09:44Z | 2025-02-05T14:09:44Z | https://github.com/huggingface/text-generation-inference/issues/2994 | [] | AlperYildirim1 | 0 |
Nemo2011/bilibili-api | api | 305 | 函数使用 | 
我想获取某个人主页的所有视频信息，我翻了文档，应该是用这个函数，async def get_videos() ，(https://nemo2011.github.io/bilibili-api/#/modules/user?id=async-def-get_videos)，但是我不知道具体该怎么用，请问有详细的示例介绍吗，谢谢兄弟！ | closed | 2023-05-27T02:34:16Z | 2023-05-27T15:31:11Z | https://github.com/Nemo2011/bilibili-api/issues/305 | [
"need"
] | pengchengKong | 5 |
pytorch/vision | computer-vision | 8,359 | About uint16 support | 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> | open | 2024-03-28T12:53:19Z | 2024-11-07T09:48:20Z | https://github.com/pytorch/vision/issues/8359 | [] | NicolasHug | 1 |
graphistry/pygraphistry | pandas | 71 | Color API use is not discoverable | 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.
| closed | 2016-07-22T16:33:31Z | 2020-06-10T06:47:31Z | https://github.com/graphistry/pygraphistry/issues/71 | [
"enhancement",
"p4"
] | lmeyerov | 0 |
tfranzel/drf-spectacular | rest-api | 984 | Standard headers properties to all endpoints in one project | 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. | closed | 2023-05-08T08:44:15Z | 2023-07-14T16:29:38Z | https://github.com/tfranzel/drf-spectacular/issues/984 | [] | barak-payem | 2 |
huggingface/datasets | numpy | 7,051 | How to set_epoch with interleave_datasets? | 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')
``` | closed | 2024-07-15T18:24:52Z | 2024-08-05T20:58:04Z | https://github.com/huggingface/datasets/issues/7051 | [] | jonathanasdf | 7 |
deepset-ai/haystack | nlp | 8,329 | API key should be made optional in `LLMEvaluator` and co | 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.
| closed | 2024-09-05T08:42:57Z | 2024-09-20T08:47:14Z | https://github.com/deepset-ai/haystack/issues/8329 | [] | shadeMe | 1 |
deepspeedai/DeepSpeed | pytorch | 7,165 | Install DeepSpeed fail with setuptools-77.0.3 | ```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.
| open | 2025-03-21T09:01:13Z | 2025-03-24T18:56:19Z | https://github.com/deepspeedai/DeepSpeed/issues/7165 | [] | kuailehaha | 6 |
kornia/kornia | computer-vision | 2,246 | Improve speed for box blur (mean filter) | ## 🚀 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>
| closed | 2023-03-02T02:02:03Z | 2023-04-19T09:07:52Z | https://github.com/kornia/kornia/issues/2246 | [
"help wanted"
] | gau-nernst | 0 |
unionai-oss/pandera | pandas | 946 | pandera 0.12.0 does not support Python 3.7 | 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.
| closed | 2022-09-19T22:30:28Z | 2022-10-18T18:34:40Z | https://github.com/unionai-oss/pandera/issues/946 | [
"bug"
] | DManowitz | 2 |
zwczou/weixin-python | flask | 91 | 希望能在WeixinMP构造方法中增加proxies代理设置参数 | 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
```
| closed | 2023-01-05T02:23:07Z | 2023-02-11T05:02:29Z | https://github.com/zwczou/weixin-python/issues/91 | [] | lo106258 | 1 |
jschneier/django-storages | django | 748 | S3 custom domain should be based on bucket name | 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! | closed | 2019-09-08T09:36:59Z | 2020-02-03T07:46:50Z | https://github.com/jschneier/django-storages/issues/748 | [
"s3boto"
] | rivernews | 3 |
developmentseed/lonboard | data-visualization | 302 | H3 support | How difficult would adding h3 cell support be given that deck.gl already supports it? Would it be possible to quickly list the steps that are required to add another feature layer type? | open | 2023-12-20T16:48:03Z | 2024-01-02T18:59:57Z | https://github.com/developmentseed/lonboard/issues/302 | [] | jakebolewski | 1 |
hankcs/HanLP | nlp | 1,196 | 每个用户使用单独的词典 | <!--
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。-->
| closed | 2019-06-10T04:02:35Z | 2019-06-10T04:04:40Z | https://github.com/hankcs/HanLP/issues/1196 | [] | hualiang9602 | 0 |
betodealmeida/shillelagh | sqlalchemy | 445 | Running query in superset sqllab with shillelagh driver returns Unsupported table error | **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
}
]
```
| closed | 2024-04-03T19:32:35Z | 2024-08-17T05:49:20Z | https://github.com/betodealmeida/shillelagh/issues/445 | [
"waiting"
] | smetis | 7 |
Ehco1996/django-sspanel | django | 131 | 同，后台添加节点报500，附上debug | 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。。。 | closed | 2018-06-05T04:59:30Z | 2018-07-30T03:19:32Z | https://github.com/Ehco1996/django-sspanel/issues/131 | [] | AndyNull | 3 |
supabase/supabase-py | flask | 72 | Can't upload anything to bucket | 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.
| closed | 2021-10-22T11:11:29Z | 2021-11-08T10:05:15Z | https://github.com/supabase/supabase-py/issues/72 | [] | kebsont | 12 |
Farama-Foundation/PettingZoo | api | 815 | [Proposal] docs: gym -> gymnasium | ### 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**)
| closed | 2022-10-07T15:21:14Z | 2022-10-08T00:00:50Z | https://github.com/Farama-Foundation/PettingZoo/issues/815 | [] | WillDudley | 0 |
babysor/MockingBird | deep-learning | 324 | 运行toolbox后进行录音，随后卡死 | 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
| closed | 2022-01-07T14:45:52Z | 2022-01-13T10:25:01Z | https://github.com/babysor/MockingBird/issues/324 | [] | Starkxim | 6 |
wkentaro/labelme | deep-learning | 1,003 | Install on Macbook m1 chip | conda install pyqt
conda install matplotlib
pip install labelme
**That works for me!**
| closed | 2022-03-15T07:01:56Z | 2022-06-25T04:27:01Z | https://github.com/wkentaro/labelme/issues/1003 | [] | XinBow99 | 1 |
pytorch/pytorch | machine-learning | 149,097 | Aten arange behavior when dtype is int64 and step size is greater than range | ### 🐛 Describe the bug
While testing corner cases on torch.arange, i see the following behavior when dtype is int64 and step size is greater than range.
On CPU, i get the following behavior for arange.
>> a = torch.arange(0, 0.5, 1, dtype=torch.int64)
>> a
tensor([], dtype=torch.int64)
>> a = torch.arange(0, 0.5, 1, dtype=torch.int32)
>>a
tensor([0], dtype=torch.int32)
Why is it that size of ‘a’ is 0 when dtype is int64 where as it is 1 for int32? Logically speaking the first element is anyways 0 and size should have been 1 even for int64 type, isn’t it?
### Versions
2025-03-13 05:10:24 (2.62 MB/s) - ‘collect_env.py’ saved [24353/24353]
Collecting environment information...
PyTorch version: 2.6.0+hpu_1.21.0-202.git603340c
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.5 (ssh://git@github.com/habana-internal/tpc_llvm10 6423f90703886aa37631daf63eaf24f24df9ba3d)
CMake version: version 3.29.5
Libc version: glibc-2.35
Python version: 3.10.12 (main, Feb 4 2025, 14:57:36) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-131-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 43 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 12
On-line CPU(s) list: 0-11
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Gold 6132 CPU @ 2.60GHz
CPU family: 6
Model: 85
Thread(s) per core: 1
Core(s) per socket: 6
Socket(s): 2
Stepping: 0
BogoMIPS: 5187.81
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon nopl xtopology tsc_reliable nonstop_tsc cpuid tsc_known_freq pni pclmulqdq vmx ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xsaves arat pku ospke md_clear flush_l1d arch_capabilities
Virtualization: VT-x
Hypervisor vendor: VMware
Virtualization type: full
L1d cache: 384 KiB (12 instances)
L1i cache: 384 KiB (12 instances)
L2 cache: 12 MiB (12 instances)
L3 cache: 38.5 MiB (2 instances)
NUMA node(s): 1
NUMA node0 CPU(s): 0-11
Vulnerability Gather data sampling: Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit: KVM: Mitigation: VMX disabled
Vulnerability L1tf: Mitigation; PTE Inversion; VMX flush not necessary, SMT disabled
Vulnerability Mds: Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Mitigation; IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; IBRS; IBPB conditional; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI SW loop, KVM SW loop
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Versions of relevant libraries:
[pip3] flake8==7.0.0
[pip3] habana-torch-dataloader==1.21.0+git9d09025dd
[pip3] habana-torch-plugin==1.21.0+git9d09025dd
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.4
[pip3] torch==2.6.0+hpu.1.21.0.202.git603340c
[pip3] torch_tb_profiler==0.4.0
[pip3] torchaudio==2.5.1a0+1661daf
[pip3] torchdata==0.9.0+d4bb3e6
[pip3] torchmetrics==1.2.1
[pip3] torchtext==0.18.0a0+9bed85d
[pip3] torchvision==0.20.1a0+3ac97aa
[conda] Could not collect
cc @albanD | open | 2025-03-13T03:11:13Z | 2025-03-17T04:22:59Z | https://github.com/pytorch/pytorch/issues/149097 | [
"triaged",
"module: python frontend"
] | satheeshhab | 1 |
graphql-python/graphene | graphql | 1,045 | Date argument is returned as String type in info.field_asts | ```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?
| closed | 2019-08-02T17:26:48Z | 2019-08-27T19:59:30Z | https://github.com/graphql-python/graphene/issues/1045 | [] | namanjain236 | 4 |
LibreTranslate/LibreTranslate | api | 576 | Web UI language not changing | 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. | closed | 2024-01-23T22:27:09Z | 2024-04-22T17:46:26Z | https://github.com/LibreTranslate/LibreTranslate/issues/576 | [
"possible bug"
] | loviuz | 2 |
JaidedAI/EasyOCR | deep-learning | 601 | Downloading detection model too slow | 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
| closed | 2021-11-27T07:12:11Z | 2022-08-07T05:01:23Z | https://github.com/JaidedAI/EasyOCR/issues/601 | [] | LiHangBing | 2 |
bigscience-workshop/petals | nlp | 279 | cpufeature fails to be installed on some non-x86 CPUs | We use the [cpufeature](https://github.com/robbmcleod/cpufeature) module to detect if a CPU supports AVX512, however, cpufeature fails to be installed on some non-x86 CPUs (e.g., ARMs).
We need to remove this dependency and use code like this to detect if the CPU supports AVX512:
```python
def is_fast_bfloat16_supported() -> bool:
if os.name != "posix" or platform.processor() != "x86_64":
return False
try:
eax = _run_x86_assembler(
b"\xB8\x07\x00\x00\x00" # mov eax, 7
b"\xB9\x01\x00\x00\x00" # mov ecx, 1
b"\x0f\xa2" # cpuid
b"\xC3" # ret
)
avx512_bf16 = bool(eax & (1 << 5))
return amx_bf16 or avx512_bf16
except:
logger.warning("Failed to check for the CPU's native bfloat16 support:", exc_info=True)
return False
def _run_x86_assembler(machine_code: bytes) -> int:
memory = mmap.mmap(
-1,
len(machine_code),
flags=mmap.MAP_PRIVATE | mmap.MAP_ANONYMOUS,
prot=mmap.PROT_WRITE | mmap.PROT_READ | mmap.PROT_EXEC,
)
try:
memory.write(machine_code)
address = ctypes.addressof(ctypes.c_int.from_buffer(memory))
functype = ctypes.CFUNCTYPE(ctypes.c_uint32)
func = functype(address)
return func()
finally:
memory.close()
```
Note that the code above checks for the presence of AVX512_BF16 instruction set, but actually pure AVX512 is enough. This needs to be changed before integrating this code, see https://en.wikichip.org/wiki/x86/avx-512 for the relevant instruction params.
Before merging, it's necessary to test on **(a)** x86-64 CPUs without AVX512, **(b)** x86-64 CPUs with AVX512, **(c)** ARM CPUs (e.g. available on Oracle free-tier cloud or other places). | open | 2023-03-01T17:29:14Z | 2023-04-12T10:11:14Z | https://github.com/bigscience-workshop/petals/issues/279 | [
"bug",
"good first issue",
"help wanted",
"1day"
] | borzunov | 6 |
wandb/wandb | data-science | 8,807 | [Bug]: Cannot change the run name through UI | ### 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
| closed | 2024-11-07T22:34:35Z | 2024-11-14T22:20:29Z | https://github.com/wandb/wandb/issues/8807 | [
"ty:bug",
"a:app"
] | Boltzmachine | 4 |
microsoft/qlib | machine-learning | 925 | 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!]. |
## 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):
| closed | 2022-02-22T02:36:55Z | 2024-10-23T14:36:41Z | https://github.com/microsoft/qlib/issues/925 | [
"bug"
] | marcuswang6 | 6 |
plotly/dash | data-visualization | 2,594 | [BUG] App restarts without error message. | 
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. | closed | 2023-07-11T00:52:01Z | 2024-05-23T10:15:14Z | https://github.com/plotly/dash/issues/2594 | [] | sorenwacker | 3 |
jupyter-widgets-contrib/ipycanvas | jupyter | 89 | Use Github actions for the CI | closed | 2020-05-01T07:10:05Z | 2020-05-01T07:46:44Z | https://github.com/jupyter-widgets-contrib/ipycanvas/issues/89 | [] | martinRenou | 1 |
|
WZMIAOMIAO/deep-learning-for-image-processing | deep-learning | 633 | 绘制PR曲线 | 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 | closed | 2022-08-31T08:26:51Z | 2022-08-31T13:27:01Z | https://github.com/WZMIAOMIAO/deep-learning-for-image-processing/issues/633 | [] | lunaWU628 | 2 |
deeppavlov/DeepPavlov | tensorflow | 1,222 | Train using GPU, predict using CPU | 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:) )
| closed | 2020-05-19T09:13:16Z | 2020-06-02T05:51:01Z | https://github.com/deeppavlov/DeepPavlov/issues/1222 | [] | ostreech1997 | 3 |
pydata/bottleneck | numpy | 132 | consider splitting memory allocation and computation functions in the c_rewrite branch | 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.
| closed | 2016-07-25T15:55:44Z | 2016-09-26T18:30:43Z | https://github.com/pydata/bottleneck/issues/132 | [] | jennolsen84 | 7 |
keras-team/keras | python | 20,857 | Several Problems when using Keras Image Augmentation Layers to Augment Images and Masks for a Semantic Segmentation Dataset | 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. | open | 2025-02-04T11:08:42Z | 2025-03-12T08:32:24Z | https://github.com/keras-team/keras/issues/20857 | [
"type:Bug"
] | RabJon | 3 |
dunossauro/fastapi-do-zero | pydantic | 292 | poetry - PEP 735 | 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 | open | 2025-02-04T03:51:59Z | 2025-02-24T20:58:21Z | https://github.com/dunossauro/fastapi-do-zero/issues/292 | [] | dunossauro | 4 |
unionai-oss/pandera | pandas | 1,284 | Should `pa.check_types` always do schema validation? | #### 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? | closed | 2023-08-01T21:53:36Z | 2023-08-02T01:28:04Z | https://github.com/unionai-oss/pandera/issues/1284 | [
"question"
] | selavy | 1 |
neuml/txtai | nlp | 611 | Add llama.cpp support to LLM | 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. | closed | 2023-12-07T12:25:22Z | 2023-12-09T16:59:43Z | https://github.com/neuml/txtai/issues/611 | [] | davidmezzetti | 0 |
tortoise/tortoise-orm | asyncio | 1,732 | Unittest failed after upgraded pydantic to 2.9.2 | **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 git@github.com:tortoise/tortoise-orm
cd tortoise-orm
poetry shell
make deps
pip install --upgrade pydantic
make test_sqlite
```
**Expected behavior**
Support pydantic2.9
| closed | 2024-10-11T07:55:24Z | 2024-10-13T07:18:05Z | https://github.com/tortoise/tortoise-orm/issues/1732 | [] | waketzheng | 0 |
pydantic/FastUI | pydantic | 231 | filterform still queries for last field of Basemodel | ```
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">
| open | 2024-03-01T15:12:26Z | 2024-03-01T15:15:42Z | https://github.com/pydantic/FastUI/issues/231 | [] | alexmondaini | 0 |
phalt/django-api-domains | graphql | 8 | [FEEDBACK] Change "All data returned from APIs must be JSON serializable." | 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... | closed | 2019-04-13T04:23:55Z | 2019-06-10T13:26:29Z | https://github.com/phalt/django-api-domains/issues/8 | [] | lorddaedra | 2 |
KaiyangZhou/deep-person-reid | computer-vision | 170 | Incredibly high map, are there potential bugs in evaluation codes? | 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? | closed | 2019-05-07T11:49:46Z | 2019-05-09T22:55:38Z | https://github.com/KaiyangZhou/deep-person-reid/issues/170 | [] | yang-jin-hai | 1 |
paperless-ngx/paperless-ngx | django | 8,820 | [BUG] Dashboard link on 404 page not working | ### 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. | closed | 2025-01-19T18:28:32Z | 2025-02-19T03:07:44Z | https://github.com/paperless-ngx/paperless-ngx/issues/8820 | [
"bug",
"frontend"
] | patheticpat | 1 |
ghtmtt/DataPlotly | plotly | 25 | Axis enhancements | * 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? | closed | 2017-05-31T15:06:26Z | 2017-06-26T13:43:17Z | https://github.com/ghtmtt/DataPlotly/issues/25 | [
"enhancement"
] | ghtmtt | 0 |
ydataai/ydata-profiling | data-science | 1,033 | Generating ProfileReport from pickle files does not work (methods ProfileReport.dumps() and ProfileReport.loads()) | ### 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). | open | 2022-09-01T06:48:42Z | 2023-02-01T06:09:18Z | https://github.com/ydataai/ydata-profiling/issues/1033 | [
"question/discussion ❓"
] | Tuxedo94 | 1 |
supabase/supabase-py | fastapi | 463 | Functions not working due invalid URL formatting | 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)` | closed | 2023-06-14T09:21:58Z | 2023-06-15T03:43:48Z | https://github.com/supabase/supabase-py/issues/463 | [
"duplicate"
] | tobias-scheepers | 2 |
pallets/flask | python | 5,388 | jinja_loader type bug | 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
| closed | 2024-01-23T16:34:25Z | 2024-02-18T00:06:19Z | https://github.com/pallets/flask/issues/5388 | [] | Siecje | 0 |
holoviz/panel | matplotlib | 7,361 | Tabulator with row_content : not toggling (collapse/expand) properly | <!--
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
```
| closed | 2024-10-05T13:39:13Z | 2024-10-15T12:40:04Z | https://github.com/holoviz/panel/issues/7361 | [] | jleonard99 | 1 |
pywinauto/pywinauto | automation | 1,216 | Focus changes from the control to the entire browser when working with Google Chrome, Yandex Browser. | ## 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
| open | 2022-06-01T15:22:34Z | 2022-06-02T11:57:08Z | https://github.com/pywinauto/pywinauto/issues/1216 | [] | ArchieAlexArkhipov | 0 |
microsoft/nni | machine-learning | 4,788 | HPO: gpu_metrics file does not exist! | **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
| open | 2022-04-21T02:32:35Z | 2022-04-26T07:29:11Z | https://github.com/microsoft/nni/issues/4788 | [] | MinYounZhang | 6 |
flaskbb/flaskbb | flask | 41 | Moderator Panel | 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
| closed | 2014-06-15T10:05:01Z | 2018-04-15T07:47:31Z | https://github.com/flaskbb/flaskbb/issues/41 | [] | sh4nks | 0 |
tensorpack/tensorpack | tensorflow | 996 | [Q] MultiThreadMapData and AugmentImageComponents? | 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))`.
| closed | 2018-12-02T12:26:21Z | 2018-12-10T08:43:39Z | https://github.com/tensorpack/tensorpack/issues/996 | [
"usage"
] | ywpkwon | 1 |
darrenburns/posting | rest-api | 232 | Autocomplete path portion in URL bar | 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. | open | 2025-03-18T09:36:25Z | 2025-03-18T09:51:53Z | https://github.com/darrenburns/posting/issues/232 | [
"planned"
] | hongquan | 1 |
piskvorky/gensim | nlp | 2,969 | Load full native fastText Facebook model is partial | #### 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
| open | 2020-09-30T21:04:09Z | 2021-03-15T23:01:33Z | https://github.com/piskvorky/gensim/issues/2969 | [
"bug",
"reach MEDIUM",
"impact MEDIUM"
] | aviclu | 16 |
modin-project/modin | data-science | 7,058 | Update exception message for `insert` function | closed | 2024-03-12T11:26:56Z | 2024-03-12T13:33:35Z | https://github.com/modin-project/modin/issues/7058 | [
"Code Quality 💯"
] | anmyachev | 0 |
|
postmanlabs/httpbin | api | 596 | Support for HTTP 308 | 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 | closed | 2020-01-28T16:28:27Z | 2022-01-03T22:27:52Z | https://github.com/postmanlabs/httpbin/issues/596 | [] | duckinator | 2 |
fbdesignpro/sweetviz | pandas | 113 | compare_intra() keeps giving 'index X is out of bounds for axis 0 with size Y' error | 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? | open | 2022-03-13T14:16:47Z | 2023-10-04T16:35:45Z | https://github.com/fbdesignpro/sweetviz/issues/113 | [
"bug"
] | ishandesilva2 | 0 |
nvbn/thefuck | python | 1,232 | fuck takes about 10s to show corrected commands, annoying. why fuck so slow? ubuntu18, pip3 install | <!-- If you have any issue with The Fuck, sorry about that, but we will do what we
can to fix that. Actually, maybe we already have, so first thing to do is to
update The Fuck and see if the bug is still there. -->
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The output of `thefuck --version` (something like `The Fuck 3.1 using Python
3.5.0 and Bash 4.4.12(1)-release`):
FILL THIS IN
Your system (Debian 7, ArchLinux, Windows, etc.):
FILL THIS IN
How to reproduce the bug:
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The output of The Fuck with `THEFUCK_DEBUG=true` exported (typically execute `export THEFUCK_DEBUG=true` in your shell before The Fuck):
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| open | 2021-09-05T01:49:44Z | 2021-12-20T15:21:59Z | https://github.com/nvbn/thefuck/issues/1232 | [] | AstroShen | 4 |
wagtail/wagtail | django | 12,080 | Concurrent editing notifications - backend additions to identify concurrent edits | 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. | closed | 2024-06-21T15:55:38Z | 2024-07-12T08:45:28Z | https://github.com/wagtail/wagtail/issues/12080 | [
"type:Enhancement"
] | gasman | 0 |
Kanaries/pygwalker | matplotlib | 348 | [Question]How to set width & height parameter when you use pygwalker on flask app? | 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}) | closed | 2023-12-10T05:21:33Z | 2023-12-12T15:51:54Z | https://github.com/Kanaries/pygwalker/issues/348 | [] | relakuman | 4 |
albumentations-team/albumentations | machine-learning | 2,174 | Unexpected Negative Coordinates in YOLOv8 Annotations after Transformations | **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!
| closed | 2024-12-04T15:13:57Z | 2024-12-05T18:39:51Z | https://github.com/albumentations-team/albumentations/issues/2174 | [
"question"
] | qweqweq1222 | 2 |
sherlock-project/sherlock | python | 1,962 | Make requests over Tor. Following error. OSError: 'tor' isn't available on your system. Maybe it's not in your PATH? | <!--
######################################################################
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IGNORING THE FOLLOWING TEMPLATE WILL RESULT IN ISSUE CLOSED AS INCOMPLETE.
######################################################################
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## Checklist
<!--
Put x into all boxes (like this [x]) once you have completed what they say.
Make sure complete everything in the checklist.
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- [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.
| closed | 2023-12-24T12:38:19Z | 2023-12-25T07:44:56Z | https://github.com/sherlock-project/sherlock/issues/1962 | [
"question"
] | PiShindo0 | 1 |
matterport/Mask_RCNN | tensorflow | 2,642 | Test on multiple GPu | 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. | open | 2021-07-22T02:16:47Z | 2021-07-22T02:16:47Z | https://github.com/matterport/Mask_RCNN/issues/2642 | [] | arashtavakoli | 0 |
pallets/flask | python | 4,986 | Tutorial Documentation Issue with --debug | # 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
| closed | 2023-02-16T21:35:52Z | 2023-03-03T01:47:16Z | https://github.com/pallets/flask/issues/4986 | [] | ProsperousHeart | 2 |
python-visualization/folium | data-visualization | 1,387 | Is it possible to add multiple images in a folium popup? | 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??
| closed | 2020-09-15T11:06:52Z | 2022-11-22T16:03:15Z | https://github.com/python-visualization/folium/issues/1387 | [] | gmehta1996 | 1 |
pydata/pandas-datareader | pandas | 932 | Key Error (trying to fetch out of range data) | 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 | open | 2022-05-11T15:50:12Z | 2022-05-26T19:23:46Z | https://github.com/pydata/pandas-datareader/issues/932 | [] | jlaurentino | 1 |
vvbbnn00/WARP-Clash-API | flask | 69 | 获取帐号太频繁了,可以调整拉取时间吗? | <img width="1265" alt="image" src="https://github.com/vvbbnn00/WARP-Clash-API/assets/19187969/f888f5ce-56e7-4379-aee4-bf678d059c14">
| closed | 2024-02-27T02:57:06Z | 2024-02-27T04:13:16Z | https://github.com/vvbbnn00/WARP-Clash-API/issues/69 | [
"enhancement"
] | summerKK | 0 |
dask/dask | numpy | 11,011 | value_counts with NaN sometimes raises ValueError: No objects to concatenate | <!-- 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
| closed | 2024-03-19T13:10:22Z | 2024-03-26T07:12:54Z | https://github.com/dask/dask/issues/11011 | [
"needs triage"
] | m-rossi | 2 |
tensorflow/tensor2tensor | machine-learning | 1,218 | Best way to decode (translate) a hundred thousand files? | 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.) | closed | 2018-11-13T00:37:47Z | 2018-11-13T19:21:27Z | https://github.com/tensorflow/tensor2tensor/issues/1218 | [] | echan00 | 1 |
piskvorky/gensim | data-science | 2,938 | Fix deprecations in SoftCosineSimilarity | 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. | closed | 2020-09-09T07:20:19Z | 2020-09-16T07:32:48Z | https://github.com/piskvorky/gensim/issues/2938 | [
"bug",
"reach HIGH",
"impact MEDIUM",
"housekeeping"
] | piskvorky | 6 |
davidsandberg/facenet | tensorflow | 336 | Regarding training images | Hi,
Please let me the format of the training images. can we have the training images in .jpg format?
Thanks
| closed | 2017-06-20T11:48:43Z | 2017-07-15T16:14:23Z | https://github.com/davidsandberg/facenet/issues/336 | [] | saravanan0206 | 2 |
allenai/allennlp | data-science | 5,710 | error message occuied “zipfile.BadZipFile: File is not a zip file” |
## Description
error message occuied “zipfile.BadZipFile: File is not a zip file”
When applied the code：
from transformers import PreTrainedTokenizerBase, AutoTokenizer
from allennlp.predictors.predictor import Predictor
from allennlp.common import Params
from allennlp.common.model_card import ModelCard
import tarfile
from pathlib import Path
from typing import List, Union, Dict, Iterable, Sequence
from allennlp.common.plugins import import_plugins
predictor = Predictor.from_path('./elmo-constituency-parser-2020.02.10.tar.gz')
<details>
elmo-constituency-parser-2020.02.10.tar.gz has already been downloaded about 678M
## Environment
OS： Linux
<!-- Provide the Python version you were using (e.g. 3.7.1) -->
Python version: 3.8
```
absl-py 1.0.0
aiohttp 3.8.1
aiosignal 1.2.0
allennlp 2.9.1
allennlp-models 2.9.0
argon2-cffi 21.3.0
argon2-cffi-bindings 21.2.0
asttokens 2.0.8
astunparse 1.6.3
async-timeout 4.0.2
attrs 22.1.0
autocommand 2.2.1
backcall 0.2.0
backports.csv 1.0.7
base58 2.1.1
beautifulsoup4 4.11.1
bleach 5.0.1
blis 0.7.8
boto3 1.24.69
botocore 1.27.69
cached-path 1.1.5
cachetools 5.0.0
catalogue 2.0.8
certifi 2022.6.15
cffi 1.15.1
charset-normalizer 2.1.0
checklist 0.0.11
cheroot 8.6.0
CherryPy 18.8.0
click 8.0.4
clip 1.0
commonmark 0.9.1
conllu 4.4.1
cryptography 38.0.1
cycler 0.11.0
cymem 2.0.6
datasets 1.18.4
debugpy 1.6.3
decorator 5.1.1
defusedxml 0.7.1
dill 0.3.5.1
docker-pycreds 0.4.0
easydict 1.9
EasyProcess 1.1
entrypoints 0.4
et-xmlfile 1.1.0
executing 0.10.0
fairscale 0.4.5
fastjsonschema 2.16.1
feedparser 6.0.10
filelock 3.6.0
flatbuffers 2.0
fonttools 4.34.4
frozenlist 1.3.1
fsspec 2022.8.2
ftfy 6.1.1
future 0.18.2
gitdb 4.0.9
GitPython 3.1.27
google-api-core 2.10.0
google-auth 2.6.5
google-auth-oauthlib 0.4.6
google-cloud-core 2.3.2
google-cloud-storage 2.5.0
google-crc32c 1.5.0
google-pasta 0.2.0
google-resumable-media 2.3.3
googleapis-common-protos 1.56.4
grpcio 1.44.0
gym 0.10.9
h5py 2.10.0
huggingface-hub 0.8.1
idna 3.3
importlib-metadata 4.11.3
importlib-resources 5.9.0
inflect 6.0.0
iniconfig 1.1.1
ipykernel 6.15.1
ipython 8.4.0
ipython-genutils 0.2.0
ipywidgets 8.0.2
iso-639 0.4.5
jaraco.classes 3.2.2
jaraco.collections 3.5.2
jaraco.context 4.1.2
jaraco.functools 3.5.1
jaraco.text 3.9.1
jedi 0.18.1
Jinja2 3.1.2
jmespath 1.0.1
joblib 1.1.0
json5 0.9.10
jsonlines 2.0.0
jsonnet 0.18.0
jsonschema 4.15.0
jupyter 1.0.0
jupyter-client 7.3.4
jupyter-console 6.4.4
jupyter-core 4.11.1
jupyterlab-pygments 0.2.2
jupyterlab-widgets 3.0.3
keras 2.8.0
Keras-Preprocessing 1.1.2
kiwisolver 1.4.2
langcodes 3.3.0
libclang 13.0.0
lmdb 1.3.0
lxml 4.9.1
Markdown 3.3.6
MarkupSafe 2.1.1
matplotlib 3.5.2
matplotlib-inline 0.1.6
mistune 2.0.4
more-itertools 8.14.0
multidict 6.0.2
multiprocess 0.70.13
munch 2.5.0
murmurhash 1.0.8
nbclient 0.6.7
nbconvert 7.0.0
nbformat 5.4.0
nest-asyncio 1.5.5
networkx 2.5.1
nltk 3.6.7
nn-builder 1.0.5
notebook 6.4.12
numpy 1.20.3
oauthlib 3.2.0
openai 0.20.0
opencv-python 4.6.0.66
openpyxl 3.0.10
opt-einsum 3.3.0
packaging 21.3
pandas 1.4.3
pandas-stubs 1.4.3.220704
pandocfilters 1.5.0
parso 0.8.3
path 16.4.0
pathtools 0.1.2
pathy 0.6.2
patternfork-nosql 3.6
pdfminer.six 20220524
pexpect 4.8.0
pickleshare 0.7.5
Pillow 9.1.1
pip 21.2.4
pkgutil_resolve_name 1.3.10
pluggy 1.0.0
portend 3.1.0
preshed 3.0.7
prometheus-client 0.14.1
promise 2.3
prompt-toolkit 3.0.20
protobuf 3.20.1
psutil 5.9.2
ptyprocess 0.7.0
pure-eval 0.2.2
py 1.11.0
py-rouge 1.1
pyarrow 9.0.0
pyasn1 0.4.8
pyasn1-modules 0.2.8
pycparser 2.21
pydantic 1.8.2
pyglet 1.5.23
Pygments 2.13.0
pyparsing 3.0.9
pyrsistent 0.18.1
pytest 7.1.3
python-dateutil 2.8.2
python-docx 0.8.11
pytz 2022.1
PyVirtualDisplay 0.2.1
PyYAML 6.0
pyzmq 23.2.1
qtconsole 5.3.2
QtPy 2.2.0
regex 2022.6.2
requests 2.28.1
requests-oauthlib 1.3.1
responses 0.18.0
rich 12.5.1
rsa 4.8
s3transfer 0.6.0
sacremoses 0.0.53
scikit-learn 1.1.2
scipy 1.8.0
Send2Trash 1.8.0
sentencepiece 0.1.97
sentry-sdk 1.9.8
setproctitle 1.3.2
setuptools 63.4.1
sgmllib3k 1.0.0
Shapely 1.7.1
shortuuid 1.0.9
six 1.16.0
smart-open 5.2.1
smmap 5.0.0
soupsieve 2.3.2.post1
spacy 3.2.4
spacy-legacy 3.0.10
spacy-loggers 1.0.3
sqlitedict 2.0.0
srsly 2.4.4
stack-data 0.4.0
stanfordcorenlp 3.9.1.1
tempora 5.0.2
tensorboard 2.8.0
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.1
tensorboardX 2.4.1
tensorflow-io-gcs-filesystem 0.24.0
termcolor 1.1.0
terminado 0.15.0
tf-estimator-nightly 2.8.0.dev2021122109
thinc 8.0.17
threadpoolctl 3.1.0
tinycss2 1.1.1
tokenizers 0.10.3
tomli 2.0.1
torch 1.8.1+cu101
torchaudio 0.8.1
torchvision 0.9.1+cu101
tornado 6.2
tqdm 4.64.1
traitlets 5.3.0
transformers 4.12.5
typer 0.4.2
typing_extensions 4.1.1
urllib3 1.26.12
wandb 0.12.21
wasabi 0.10.1
wcwidth 0.2.5
webencodings 0.5.1
Werkzeug 2.1.1
wheel 0.37.1
widgetsnbextension 4.0.3
word2number 1.1
wrapt 1.14.0
xxhash 3.0.0
yarl 1.8.1
zc.lockfile 2.0
zipp 3.8.0
```
| closed | 2022-09-12T12:36:27Z | 2022-10-03T16:11:32Z | https://github.com/allenai/allennlp/issues/5710 | [
"bug",
"stale"
] | Hoyyyaard | 3 |
saulpw/visidata | pandas | 2,283 | Avoid Status Spam/Visual Noise | 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. | closed | 2024-01-31T22:49:39Z | 2024-02-01T21:58:51Z | https://github.com/saulpw/visidata/issues/2283 | [
"wishlist",
"duplicate"
] | gordo-veda | 4 |
jwkvam/bowtie | jupyter | 263 | Proposing a PR to fix a few small typos | # 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.
| open | 2021-06-03T12:59:35Z | 2021-06-03T12:59:35Z | https://github.com/jwkvam/bowtie/issues/263 | [] | timgates42 | 0 |
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