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bhack/android-cmake 1

CMake toolchain file and other scripts for the Android NDK

bhack/benchmark_results 1

visual tracker benchmark results

bhack/CppCoreGuidelines 1

The C++ Core Guidelines are a set of tried-and-true guidelines, rules, and best practices about coding in C++

bhack/addons 0

Useful extra functionality for TensorFlow 2.x maintained by SIG-addons

bhack/bitnami-docker-redis 0

Bitnami Redis Docker Image

bhack/blitznet 0

Deep neural network for object detection and semantic segmentation in real-time. Official code for the paper "BlitzNet: A Real-Time Deep Network for Scene Understanding"

bhack/caffe 0

Caffe: a Fast framework for neural networks. For the most recent version, check out branch dev. For a more stable version, check out branch master.

bhack/caffe_ssl 0

caffe for Self-supervised Structure-sensitive Learning, CVPR'17

bhack/camelot-flutter 0

Cross platform Flutter application to subscribe and vote on Rousseau.

bhack/charts 0

Curated applications for Kubernetes

issue openedassociazione-rousseau/camelot-flutter

Discourse

Is It possibile to setup a Discourse instance? It would be nice to have this instance authetnicated with your Keycloak instance as It will be useful also to discuss non Camelot topics.

created time in 6 hours

pull request commenttensorflow/addons

Add Modulated Deformable Convolution layer

I put this under ecosystem review. If any project in TF ecosystem is interested in this or has this in It own internal roadmap we will try to review It here.

Licht-T

comment created time in 8 hours

pull request commenttensorflow/addons

Add Modulated Deformable Convolution layer

/cc @tanzhenyu @dynamicwebpaige for ecosystem check.

Licht-T

comment created time in 8 hours

pull request commenttensorflow/tensorflow

Overview sanilty check fails on pylint upgrade

@bhack Can you please check @mihaimaruseac's comments and keep us posted ? Thanks!

I don't understand cause it seems to me that there Is still any plan on your side.

bhack

comment created time in 2 days

issue commenttensorflow/tensorflow

Mysterious exception when using tf.data.Dataset.from_tensor_slices

For this as It Is not a bug or feature request you need to close the ticket and open a support question at https://stackoverflow.com/questions/tagged/tensorflow

firebird52

comment created time in 2 days

issue commenttensorflow/tensorflow

Mysterious exception when using tf.data.Dataset.from_tensor_slices

If you want to use from_tensor_slices you need to prepare something compatible

firebird52

comment created time in 2 days

Pull request review commenttensorflow/addons

Update cuda config for CUDA11.1

 def _find_libs(repository_ctx, cuda_config):             repository_ctx,             cpu_value,             cuda_config.config["cuda_library_dir"],-            cuda_config.cuda_version,

is cuda_version removed?

perfinion

comment created time in 3 days

PullRequestReviewEvent

issue commenttensorflow/addons

Add AdaBelief optimizer [https://arxiv.org/abs/2010.07468]

TF reference implementation at https://github.com/juntang-zhuang/Adabelief-Optimizer/blob/master/TensorFlow_Experiments/AdaBelief_tf.py

TimbusCalin

comment created time in 3 days

issue closedtensorflow/addons

TypeError, while creating timeseries windows for training and validation data in TFX

I'm using TensorFlow version: 2.3.1 and TFX version: 0.24.1.

I am developing machine learning workflow for timeseries using TFX. The ExampleGen, StatisticsGen, SchemaGen and Example validator and transform components worked fine. In the Trainer component, i used the tf.keras.preprocessing.timeseries_dataset_from_array API within the run_fn() of the Trainer component to generate the train and evaluation windows then i got a TypeError: dataset length is infinite. Is this the right way to create the train and eval data window. Below is the modification i made to the run_fn() in the Trainer module file and the error message;

def run_fn(fn_args: TrainerFnArgs):
    """Train the model based on given args.

    Args:
    fn_args: Holds args used to train the model as name/value pairs.
    """
    # Number of nodes in the first layer of the DNN
    first_dnn_layer_size = 100
    num_dnn_layers = 4
    dnn_decay_factor = 0.7

    sequence_length = 144
    batch_size = 1

    tf_transform_output = tft.TFTransformOutput(fn_args.transform_output)

    train_dataset = _input_fn(fn_args.train_files, fn_args.data_accessor, 
                            tf_transform_output, 40)
    
    # Generate timeseries sliding window for the training data
    train_dataset = tf.keras.preprocessing.timeseries_dataset_from_array(train_dataset, train_dataset, sequence_length=sequence_length , batch_size=batch_size)

    # Generate timeseries sliding window for the eval data
    eval_dataset = _input_fn(fn_args.eval_files, fn_args.data_accessor, 
                           tf_transform_output, 40)
    eval_dataset = tf.keras.preprocessing.timeseries_dataset_from_array(eval_dataset, eval_dataset, sequence_length=sequence_length , batch_size=batch_size)

    model = _build_keras_model(
      # Construct layers sizes with exponetial decay
      hidden_units=[
          max(2, int(first_dnn_layer_size * dnn_decay_factor**i))
          for i in range(num_dnn_layers)
      ])

    tensorboard_callback = tf.keras.callbacks.TensorBoard(
      log_dir=fn_args.model_run_dir, update_freq='batch')
    model.fit(
      train_dataset,
      steps_per_epoch=fn_args.train_steps,
      validation_data=eval_dataset,
      validation_steps=fn_args.eval_steps,
      callbacks=[tensorboard_callback])

    signatures = {
      'serving_default':
          _get_serve_tf_examples_fn(model,
                                    tf_transform_output).get_concrete_function(
                                        tf.TensorSpec(
                                            shape=[None],
                                            dtype=tf.string,
                                            name='examples')),
    }
trainer = Trainer(
    module_file=os.path.abspath(trainer_module_file),
    custom_executor_spec=executor_spec.ExecutorClassSpec(GenericExecutor),
    examples=transform.outputs['transformed_examples'],
    transform_graph=transform.outputs['transform_graph'],
    schema=schema_gen.outputs['schema'],
    train_args=trainer_pb2.TrainArgs(num_steps=10000),
    eval_args=trainer_pb2.EvalArgs(num_steps=5000))
context.run(trainer)

Error message:

INFO:absl:Running driver for Trainer
INFO:absl:MetadataStore with DB connection initialized
INFO:absl:Running executor for Trainer
INFO:absl:Train on the 'train' split when train_args.splits is not set.
INFO:absl:Evaluate on the 'eval' split when eval_args.splits is not set.
INFO:absl:Training model.
INFO:absl:We decided to produce LargeList and LargeBinary types.
INFO:absl:Feature Appliances_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature Press_mm_hg_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature RH_1_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature RH_2_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature RH_3_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature RH_4_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature RH_5_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature RH_6_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature RH_7_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature RH_8_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature RH_9_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature RH_out_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature T1_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature T2_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature T3_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature T4_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature T5_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature T6_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature T7_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature T8_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature T9_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature T_out_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature Tdewpoint_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature Visibility_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature Windspeed_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature lights_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature rv1_xf has a shape . Setting to DenseTensor.
INFO:absl:Feature rv2_xf has a shape . Setting to DenseTensor.
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-21-cc5cfdc341eb> in <module>()
      7     train_args=trainer_pb2.TrainArgs(num_steps=10000),
      8     eval_args=trainer_pb2.EvalArgs(num_steps=5000))
----> 9 context.run(trainer)

7 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/data/ops/dataset_ops.py in __len__(self)
    443     length = self.cardinality()
    444     if length.numpy() == INFINITE:
--> 445       raise TypeError("dataset length is infinite.")
    446     if length.numpy() == UNKNOWN:
    447       raise TypeError("dataset length is unknown.")
TypeError: dataset length is infinite.

closed time in 3 days

Jaydeemourg

issue commenttensorflow/addons

TypeError, while creating timeseries windows for training and validation data in TFX

Please reopen this if you are using Tensorflow addons.

Jaydeemourg

comment created time in 3 days

issue commenttensorflow/tensorflow

Looking for tf-nightly 2.4.0.dev20200817

I don't think so the old one is 2.4.0.dev20200902.

jvahl

comment created time in 3 days

issue commenttensorflow/tensorflow

Concrete Function output shape sometimes changes after save/load cycle

/cc @andresusanopinto

gmacon

comment created time in 3 days

issue commenttensorflow/addons

EmbeddingBag and Product-Key Memory Layers

/cc Gently ping for @tanzhenyu @dynamicwebpaige for ecosystem pre-check

Rocketknight1

comment created time in 3 days

issue commenttensorflow/tensorflow

Concrete Function output shape sometimes changes after save/load cycle

@gmacon If you insert print(t1.__call__.pretty_printed_concrete_signatures())

You will see:

ConcreteFunction __call__(x)
  Args:
    x: float32 Tensor, shape=(None, 3)
  Returns:
    float32 Tensor, shape=(None, 3)
__call__(x)
  Args:
    x: float32 Tensor, shape=(7, 3)
  Returns:
    float32 Tensor, shape=(7, 3)

When you save the model the saved concrete function could be in any order. E.g.

Tensor("StatefulPartitionedCall:0", shape=(7, 3), dtype=float32)
Tensor("StatefulPartitionedCall_1:0", shape=(None, 3), dtype=float32)
Tensor("StatefulPartitionedCall_2:0", shape=(None, 3), dtype=float32)

The logic is that it takes the first saved concrete function with a "compatible" in a loop over all the saved concrete functions:

https://github.com/tensorflow/tensorflow/blob/4e281e9a211729a9a57df04dad22d15c988f2919/tensorflow/python/saved_model/function_deserialization.py#L246-L253

And the compatibility check is: https://github.com/tensorflow/tensorflow/blob/4e281e9a211729a9a57df04dad22d15c988f2919/tensorflow/python/saved_model/function_deserialization.py#L92-L119

So as the order of the concrete functions change on save you could enter in any compatible concrete function.

gmacon

comment created time in 3 days

issue commenttensorflow/tensorflow

Concrete Function output shape sometimes changes after save/load cycle

Yes working with kwargs is still in the todo list. Why do you require to trace with t1(np.ones((7, 3), dtype=np.float32))?

gmacon

comment created time in 3 days

issue commenttensorflow/tensorflow

Single threaded TensorFlow

On master you can try to make a look at

https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/common_runtime/threadpool_device.cc#L45:L69

fisakhan

comment created time in 3 days

issue commenttensorflow/tensorflow

Concrete Function output shape sometimes changes after save/load cycle

@gmacon Can you try with @tf.function(input_signature=(tf.TensorSpec(shape=[None, 3], dtype=tf.float32),))?

gmacon

comment created time in 3 days

issue commenttensorflow/tensorflow

why gradient of clip_by_value(x) is zero, when x is outside of range?

If you want to preserve the gradient you could take a look at https://www.tensorflow.org/probability/api_docs/python/tfp/math/clip_by_value_preserve_gradient

iperov

comment created time in 3 days

issue commenttensorflow/tensorflow

Bug in CosineSimilarity

cosine_loss(y_true.astype(dtype=np.float32), y_pred).numpy()

abhinavsp0730

comment created time in 3 days

issue commenttensorflow/tensorflow

Single threaded TensorFlow

@fisakhan Have you tried to control also OMP_THREAD_NUM?

fisakhan

comment created time in 3 days

pull request commenttensorflow/addons

Kendall's Tau metric, based loosely on scipy.

I don't know what you mean by "migrate".

Yes I supposed that we had a related issue about this features as it was mandatory in our contribution policy. But in this case we have a PR directly.

For now, we could perhaps put something like this under tfp.experimental.stats, where we have other streaming metrics like covariance etc. Or you could modify to drop the streaming component and simply compute on a single tensor of observations, which would be more consistent w/ e.g. tfp.stats.covariance. We would likely want to eliminate the for loops for TFP. If you really need a loop, we usually use tf.while_loop or tf.scan to avoid bloating the tf.function graph.

The plan is ok for me

@sorensenjs Can you open the PR at https://github.com/tensorflow/probability/ ?

sorensenjs

comment created time in 4 days

pull request commenttensorflow/addons

Kendall's Tau metric, based loosely on scipy.

@jvdillon @brianwa84 Do you have permission to migrate this in TFP?

sorensenjs

comment created time in 4 days

push eventtensorflow/addons

Guillaume Klein

commit sha 7d8d2b3a1a84917b1165d5c415eb2fd4c5fe1c54

Improve docstring format and cross-linking (#2216) * Improve docstring format and cross-linking * Revert reformat

view details

push time in 4 days

PR merged tensorflow/addons

Improve docstring format and cross-linking cla: yes seq2seq

Description

Using absolute reference to other classes tfa.seq2seq. will create a link in the online documentation.

Type of change

Checklist:

  • [x] I've properly formatted my code according to the guidelines
    • [x] By running Black + Flake8
    • [ ] By running pre-commit hooks
  • [ ] This PR addresses an already submitted issue for TensorFlow Addons
  • [ ] I have made corresponding changes to the documentation
  • [ ] I have added tests that prove my fix is effective or that my feature works
  • [ ] This PR contains modifications to C++ custom-ops
+67 -67

0 comment

5 changed files

guillaumekln

pr closed time in 4 days

pull request commenttensorflow/community

RFC: Creating SIG Models

In general I think that is great and strategic to have this SIG cause Models and Hub are really one of the unique places in the ecosystem where all of the pieces converges together tocomposed our end2end goal. Also we often suffer more then the Pytorch ecosystem about availability of third party papers official (and unofficial) reference implementations in the github "universe".

But we really need to take this SIG as an occasion to improve our ecosystem about component re-usability and ownership disambiguation. It could be nice to have a short grace period for the new components (layer, loss, optimizer, preprocessing, postprocessing, etc.) that would be introduced in TF model. As we know external members don't know the public roadmap of the different internal teams (and this could happen also between different internal teams) you can find a recap thread at https://github.com/tensorflow/community/issues/29. This could create duplicates, unaware supersedes, implementation divergences and so on.. In Addons we have tried to Draft an early ecosystem-review process to notify the ecosystem that probably could be standardized and improved for general use to improve the coherence between internal teams and external members. By this point of view I see this SIG one of the most critical points to achieve this goal in our ecosystem.

jaeyounkim

comment created time in 4 days

issue commenttensorflow/tensorflow

Lr keyword parameter for learning rate does not accept a learning rate schdule

The PR add Just an extra check but you need to use learning_rate not lr

nsorros

comment created time in 4 days

issue commenttensorflow/tensorflow

Mysterious exception when using tf.data.Dataset.from_tensor_slices

Do you need to use tf.data.Dataset.from_tensor_slices(data[1][0])?

firebird52

comment created time in 5 days

issue commenttensorflow/tensorflow

Downloading pretrained Efficient Det in google colab using TensorFlow Object Detection Api gives a series of unknown warnings?

@AshTech25 Are you training/fine-tuning the model?

No I'm just using the model for my inferences

So without the training you will not got the error but the warning has the same origin

AshTech25

comment created time in 5 days

issue commenttensorflow/tensorflow

Heteregnous inference

Check https://www.tensorflow.org/tutorials/distribute/save_and_load?hl=en#the_tfsaved_model_api And the glossary at https://www.tensorflow.org/api_docs/python/tf/distribute

peter197321

comment created time in 5 days

issue commenttensorflow/tensorflow

Lr keyword parameter for learning rate does not accept a learning rate schdule

Check https://github.com/tensorflow/tensorflow/pull/44295

nsorros

comment created time in 6 days

PR opened tensorflow/tensorflow

Add extra warning info for lr

Fix https://github.com/tensorflow/tensorflow/issues/44293 https://github.com/tensorflow/tensorflow/issues/44172

+6 -0

0 comment

1 changed file

pr created time in 6 days

create barnchbhack/tensorflow

branch : fix_44293

created branch time in 6 days

issue commenttensorflow/tensorflow

tf.keras.experimental.CosineDecay error

Please check https://github.com/tensorflow/tensorflow/issues/44172#issuecomment-712776248

palatos

comment created time in 6 days

Pull request review commenttensorflow/community

RFC: Creating SIG Models

+# Creating SIG Models++| Status        | Proposed                                             |+:-------------- |:---------------------------------------------------- |+| **RFC #**     |                                                      |+| **Author(s)** | Jaeyoun Kim (jaeyounkim@google.com), Jing Li (jingli@google.com), Mike Liang (mliang@google.com), Shuangfeng Li (shuangfeng@google.com) |+| **Sponsor**   | Thea Lamkin (thealamkin@google.com)                  |+| **Updated**   | 2020-10-23                                           |++## What is this group for?++This group is for discussions and collaborations on enabling community+contributions to [TensorFlow Model Garden](https://github.com/tensorflow/models)+and [Tensorflow Hub](https://github.com/tensorflow/hub).++SIG Models will focus on empowering the community to contribute state-of-the-art+model implementation in TensorFlow 2. It will benefit the whole community by+providing recommended implementations and models with reproducible results.++SIG Models will have several subgroups (e.g., SIG Models Vision and SIG Models+NLP) covering different machine learning areas. There are several SIG leads for+each group to coordinate the contributions, run community events like contests,+and maintain the code quality through the review process. Each group has the+flexibility to operate differently.++SIG is also a place for community discussions and sharing best practices of+using TensorFlow 2 for state-of-the-art research. Furthermore, SIG provides+product feedback to help TensorFlow to be improved.++## Who will be part of it?++SIG Models are open, membership will be entirely public, and anybody interested+in model contributions or participating in the discussion can join the SIG.+There can be different ways to participate:++* Everyone in the community can pick GitHub model tasks and contribute models.+* They can also join the regular SIG meetings or email groups to participate in+  the discussions. To participate, request an invitation to join the SIG mailing+  list. Archives of the mailing list are publicly accessible.++We will identify SIG leads from the community to run the SIG Models groups.+Initially, we plan to set up the SIG Models Vision group:++* SIG Models Vision leads+  * George Thiruvathukal, gkt@cs.luc.edu (Loyola University Chicago)+  * Yung-Hsiang Lu, yunglu@purdue.edu (Purdue University)+* Co-leads and sponsors from TensorFlow+  * Jaeyoun Kim, jaeyounkim@google.com (TensorFlow Model Garden)+  * Jing Li, jingli@google.com (TensorFlow Model Garden)+  * Mike Liang, mliang@google.com (TensorFlow Hub)+  * Shuangfeng Li, shuangfeng@google.com (TensorFlow Lite)++## What initial problems will the group tackle?++We will start with the SIG Models Vision group. Initially, the group leads will+run a contest to motivate the community to contribute state-of-the-art computer+vision models using TensorFlow 2. ++For example:++* Define/release model implementation tasks, rules, and evaluation criteria for+  the contest+* Review initial submissions and provide more support for selected high-quality+  submissions+* Review final submissions and announce results++## What modes of communication do you intend to use?

There are many dups or semi-dups like https://github.com/tensorflow/addons/issues/1903#issuecomment-715897109 that confuse users and third party SIG maintainers.

jaeyounkim

comment created time in 6 days

PullRequestReviewEvent

issue commenttensorflow/addons

Support `exclude_from_weight_decay` in AdamW

We had this in https://github.com/tensorflow/models/blob/master/official/nlp/optimization.py#L112

jarednielsen

comment created time in 6 days

issue commenttensorflow/addons

Support `exclude_from_weight_decay` in AdamW

/cc @philjd

jarednielsen

comment created time in 6 days

issue closedtensorflow/addons

missing `exclude_from_weight_decay` in AdamW

System information

  • OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04
  • TensorFlow version and how it was installed (source or binary): binary 2.3
  • TensorFlow-Addons version and how it was installed (source or binary): binary nightly
  • Python version: 3.6
  • Is GPU used? (yes/no): yes

Describe the bug

seem exclude_from_weight_decay is missed in tfa.optimizers.AdamW. in tfa.optimizers.LAMB, we have this parameter.

Code to reproduce the issue

Provide a reproducible test case that is the bare minimum necessary to generate the problem.

Other info / logs

Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.

closed time in 6 days

dathudeptrai

issue commenttensorflow/addons

missing `exclude_from_weight_decay` in AdamW

https://github.com/tensorflow/addons/issues/1903

dathudeptrai

comment created time in 6 days

Pull request review commenttensorflow/community

RFC: Creating SIG Models

+# Creating SIG Models++| Status        | Proposed                                             |+:-------------- |:---------------------------------------------------- |+| **RFC #**     |                                                      |+| **Author(s)** | Jaeyoun Kim (jaeyounkim@google.com), Jing Li (jingli@google.com), Mike Liang (mliang@google.com), Shuangfeng Li (shuangfeng@google.com) |+| **Sponsor**   | Thea Lamkin (thealamkin@google.com)                  |+| **Updated**   | 2020-10-23                                           |++## What is this group for?++This group is for discussions and collaborations on enabling community+contributions to [TensorFlow Model Garden](https://github.com/tensorflow/models)+and [Tensorflow Hub](https://github.com/tensorflow/hub).++SIG Models will focus on empowering the community to contribute state-of-the-art+model implementation in TensorFlow 2. It will benefit the whole community by+providing recommended implementations and models with reproducible results.++SIG Models will have several subgroups (e.g., SIG Models Vision and SIG Models+NLP) covering different machine learning areas. There are several SIG leads for+each group to coordinate the contributions, run community events like contests,+and maintain the code quality through the review process. Each group has the+flexibility to operate differently.++SIG is also a place for community discussions and sharing best practices of+using TensorFlow 2 for state-of-the-art research. Furthermore, SIG provides+product feedback to help TensorFlow to be improved.++## Who will be part of it?++SIG Models are open, membership will be entirely public, and anybody interested+in model contributions or participating in the discussion can join the SIG.+There can be different ways to participate:++* Everyone in the community can pick GitHub model tasks and contribute models.+* They can also join the regular SIG meetings or email groups to participate in+  the discussions. To participate, request an invitation to join the SIG mailing+  list. Archives of the mailing list are publicly accessible.++We will identify SIG leads from the community to run the SIG Models groups.+Initially, we plan to set up the SIG Models Vision group:++* SIG Models Vision leads+  * George Thiruvathukal, gkt@cs.luc.edu (Loyola University Chicago)+  * Yung-Hsiang Lu, yunglu@purdue.edu (Purdue University)+* Co-leads and sponsors from TensorFlow+  * Jaeyoun Kim, jaeyounkim@google.com (TensorFlow Model Garden)+  * Jing Li, jingli@google.com (TensorFlow Model Garden)+  * Mike Liang, mliang@google.com (TensorFlow Hub)+  * Shuangfeng Li, shuangfeng@google.com (TensorFlow Lite)++## What initial problems will the group tackle?++We will start with the SIG Models Vision group. Initially, the group leads will+run a contest to motivate the community to contribute state-of-the-art computer+vision models using TensorFlow 2. ++For example:++* Define/release model implementation tasks, rules, and evaluation criteria for+  the contest+* Review initial submissions and provide more support for selected high-quality+  submissions+* Review final submissions and announce results++## What modes of communication do you intend to use?

This comment is connected to the models's reusable components topic. Some times ago we had a ticket about this at https://github.com/tensorflow/community/issues/223

jaeyounkim

comment created time in 7 days

PullRequestReviewEvent

Pull request review commenttensorflow/community

RFC: Creating SIG Models

+# Creating SIG Models++| Status        | Proposed                                             |+:-------------- |:---------------------------------------------------- |+| **RFC #**     |                                                      |+| **Author(s)** | Jaeyoun Kim (jaeyounkim@google.com), Jing Li (jingli@google.com), Mike Liang (mliang@google.com), Shuangfeng Li (shuangfeng@google.com) |+| **Sponsor**   | Thea Lamkin (thealamkin@google.com)                  |+| **Updated**   | 2020-10-23                                           |++## What is this group for?++This group is for discussions and collaborations on enabling community+contributions to [TensorFlow Model Garden](https://github.com/tensorflow/models)+and [Tensorflow Hub](https://github.com/tensorflow/hub).++SIG Models will focus on empowering the community to contribute state-of-the-art+model implementation in TensorFlow 2. It will benefit the whole community by+providing recommended implementations and models with reproducible results.++SIG Models will have several subgroups (e.g., SIG Models Vision and SIG Models+NLP) covering different machine learning areas. There are several SIG leads for+each group to coordinate the contributions, run community events like contests,+and maintain the code quality through the review process. Each group has the+flexibility to operate differently.++SIG is also a place for community discussions and sharing best practices of+using TensorFlow 2 for state-of-the-art research. Furthermore, SIG provides+product feedback to help TensorFlow to be improved.++## Who will be part of it?++SIG Models are open, membership will be entirely public, and anybody interested+in model contributions or participating in the discussion can join the SIG.+There can be different ways to participate:++* Everyone in the community can pick GitHub model tasks and contribute models.+* They can also join the regular SIG meetings or email groups to participate in+  the discussions. To participate, request an invitation to join the SIG mailing+  list. Archives of the mailing list are publicly accessible.++We will identify SIG leads from the community to run the SIG Models groups.+Initially, we plan to set up the SIG Models Vision group:++* SIG Models Vision leads+  * George Thiruvathukal, gkt@cs.luc.edu (Loyola University Chicago)+  * Yung-Hsiang Lu, yunglu@purdue.edu (Purdue University)+* Co-leads and sponsors from TensorFlow+  * Jaeyoun Kim, jaeyounkim@google.com (TensorFlow Model Garden)+  * Jing Li, jingli@google.com (TensorFlow Model Garden)+  * Mike Liang, mliang@google.com (TensorFlow Hub)+  * Shuangfeng Li, shuangfeng@google.com (TensorFlow Lite)++## What initial problems will the group tackle?++We will start with the SIG Models Vision group. Initially, the group leads will+run a contest to motivate the community to contribute state-of-the-art computer+vision models using TensorFlow 2. ++For example:++* Define/release model implementation tasks, rules, and evaluation criteria for+  the contest+* Review initial submissions and provide more support for selected high-quality+  submissions+* Review final submissions and announce results

The real question is also how to implement something like https://github.com/tensorflow/addons/issues/236. I don't know if this new SIG will have the resource to implement something like to solve this open topic. I think it would be really useful for all the SIGs but also for the TF ecosystem.

jaeyounkim

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Pull request review commenttensorflow/community

RFC: Creating SIG Models

+# Creating SIG Models++| Status        | Proposed                                             |+:-------------- |:---------------------------------------------------- |+| **RFC #**     |                                                      |+| **Author(s)** | Jaeyoun Kim (jaeyounkim@google.com), Jing Li (jingli@google.com), Mike Liang (mliang@google.com), Shuangfeng Li (shuangfeng@google.com) |+| **Sponsor**   | Thea Lamkin (thealamkin@google.com)                  |+| **Updated**   | 2020-10-23                                           |++## What is this group for?++This group is for discussions and collaborations on enabling community+contributions to [TensorFlow Model Garden](https://github.com/tensorflow/models)+and [Tensorflow Hub](https://github.com/tensorflow/hub).++SIG Models will focus on empowering the community to contribute state-of-the-art+model implementation in TensorFlow 2. It will benefit the whole community by+providing recommended implementations and models with reproducible results.++SIG Models will have several subgroups (e.g., SIG Models Vision and SIG Models+NLP) covering different machine learning areas. There are several SIG leads for+each group to coordinate the contributions, run community events like contests,+and maintain the code quality through the review process. Each group has the+flexibility to operate differently.++SIG is also a place for community discussions and sharing best practices of+using TensorFlow 2 for state-of-the-art research. Furthermore, SIG provides+product feedback to help TensorFlow to be improved.++## Who will be part of it?++SIG Models are open, membership will be entirely public, and anybody interested+in model contributions or participating in the discussion can join the SIG.+There can be different ways to participate:++* Everyone in the community can pick GitHub model tasks and contribute models.+* They can also join the regular SIG meetings or email groups to participate in+  the discussions. To participate, request an invitation to join the SIG mailing+  list. Archives of the mailing list are publicly accessible.++We will identify SIG leads from the community to run the SIG Models groups.+Initially, we plan to set up the SIG Models Vision group:++* SIG Models Vision leads+  * George Thiruvathukal, gkt@cs.luc.edu (Loyola University Chicago)+  * Yung-Hsiang Lu, yunglu@purdue.edu (Purdue University)+* Co-leads and sponsors from TensorFlow+  * Jaeyoun Kim, jaeyounkim@google.com (TensorFlow Model Garden)+  * Jing Li, jingli@google.com (TensorFlow Model Garden)+  * Mike Liang, mliang@google.com (TensorFlow Hub)+  * Shuangfeng Li, shuangfeng@google.com (TensorFlow Lite)++## What initial problems will the group tackle?++We will start with the SIG Models Vision group. Initially, the group leads will+run a contest to motivate the community to contribute state-of-the-art computer+vision models using TensorFlow 2. ++For example:++* Define/release model implementation tasks, rules, and evaluation criteria for+  the contest+* Review initial submissions and provide more support for selected high-quality+  submissions+* Review final submissions and announce results

We are also trying to figure if a bot could do some keep-alive/MIA checks https://github.com/tensorflow/addons/pull/2024

jaeyounkim

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pull request commenttensorflow/community

RFC: Keras SavedModel Update

What is the status of this? I don't know if this is also will cover the issue about optimizer with slots. In this case I want to add a warning somewhere in the meantime https://github.com/tensorflow/tensorflow/pull/42846

k-w-w

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Pull request review commenttensorflow/community

RFC: Creating SIG Models

+# Creating SIG Models++| Status        | Proposed                                             |+:-------------- |:---------------------------------------------------- |+| **RFC #**     |                                                      |+| **Author(s)** | Jaeyoun Kim (jaeyounkim@google.com), Jing Li (jingli@google.com), Mike Liang (mliang@google.com), Shuangfeng Li (shuangfeng@google.com) |+| **Sponsor**   | Thea Lamkin (thealamkin@google.com)                  |+| **Updated**   | 2020-10-23                                           |++## What is this group for?++This group is for discussions and collaborations on enabling community+contributions to [TensorFlow Model Garden](https://github.com/tensorflow/models)+and [Tensorflow Hub](https://github.com/tensorflow/hub).++SIG Models will focus on empowering the community to contribute state-of-the-art+model implementation in TensorFlow 2. It will benefit the whole community by+providing recommended implementations and models with reproducible results.++SIG Models will have several subgroups (e.g., SIG Models Vision and SIG Models+NLP) covering different machine learning areas. There are several SIG leads for+each group to coordinate the contributions, run community events like contests,+and maintain the code quality through the review process. Each group has the+flexibility to operate differently.++SIG is also a place for community discussions and sharing best practices of+using TensorFlow 2 for state-of-the-art research. Furthermore, SIG provides+product feedback to help TensorFlow to be improved.++## Who will be part of it?

What about keras-cv/keras-nlp?

jaeyounkim

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PullRequestReviewEvent

Pull request review commenttensorflow/community

RFC: Creating SIG Models

+# Creating SIG Models++| Status        | Proposed                                             |+:-------------- |:---------------------------------------------------- |+| **RFC #**     |                                                      |+| **Author(s)** | Jaeyoun Kim (jaeyounkim@google.com), Jing Li (jingli@google.com), Mike Liang (mliang@google.com), Shuangfeng Li (shuangfeng@google.com) |+| **Sponsor**   | Thea Lamkin (thealamkin@google.com)                  |+| **Updated**   | 2020-10-23                                           |++## What is this group for?++This group is for discussions and collaborations on enabling community+contributions to [TensorFlow Model Garden](https://github.com/tensorflow/models)+and [Tensorflow Hub](https://github.com/tensorflow/hub).++SIG Models will focus on empowering the community to contribute state-of-the-art+model implementation in TensorFlow 2. It will benefit the whole community by+providing recommended implementations and models with reproducible results.++SIG Models will have several subgroups (e.g., SIG Models Vision and SIG Models+NLP) covering different machine learning areas. There are several SIG leads for+each group to coordinate the contributions, run community events like contests,+and maintain the code quality through the review process. Each group has the+flexibility to operate differently.++SIG is also a place for community discussions and sharing best practices of+using TensorFlow 2 for state-of-the-art research. Furthermore, SIG provides+product feedback to help TensorFlow to be improved.++## Who will be part of it?++SIG Models are open, membership will be entirely public, and anybody interested+in model contributions or participating in the discussion can join the SIG.+There can be different ways to participate:++* Everyone in the community can pick GitHub model tasks and contribute models.+* They can also join the regular SIG meetings or email groups to participate in+  the discussions. To participate, request an invitation to join the SIG mailing+  list. Archives of the mailing list are publicly accessible.++We will identify SIG leads from the community to run the SIG Models groups.+Initially, we plan to set up the SIG Models Vision group:++* SIG Models Vision leads+  * George Thiruvathukal, gkt@cs.luc.edu (Loyola University Chicago)+  * Yung-Hsiang Lu, yunglu@purdue.edu (Purdue University)+* Co-leads and sponsors from TensorFlow+  * Jaeyoun Kim, jaeyounkim@google.com (TensorFlow Model Garden)+  * Jing Li, jingli@google.com (TensorFlow Model Garden)+  * Mike Liang, mliang@google.com (TensorFlow Hub)+  * Shuangfeng Li, shuangfeng@google.com (TensorFlow Lite)++## What initial problems will the group tackle?++We will start with the SIG Models Vision group. Initially, the group leads will+run a contest to motivate the community to contribute state-of-the-art computer+vision models using TensorFlow 2. ++For example:++* Define/release model implementation tasks, rules, and evaluation criteria for+  the contest+* Review initial submissions and provide more support for selected high-quality+  submissions+* Review final submissions and announce results

We have already something about this in SIG-addons in the approved RFC https://github.com/tensorflow/community/blob/master/rfcs/20190308-addons-proxy-maintainership.md#repository-growth-and-review /cc @seanpmorgan

jaeyounkim

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issue commentmicrosoft/vscode-remote-release

Support of having multiple .devcontainer configs

Also for build/rebuild action we need to control platform flag https://www.docker.com/blog/multi-platform-docker-builds/

andyli

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issue commentmicrosoft/vscode-remote-release

Support of having multiple .devcontainer configs

How this will fit with the multiarch case? https://github.com/multiarch/qemu-user-static

andyli

comment created time in 7 days

PullRequestReviewEvent

pull request commentjitsi/jitsi-meet

Polls/Voting Feature in Jitsi-meet

See also https://github.com/jitsi/jitsi-meet/issues/6864

mmoanis

comment created time in 8 days

issue commenttensorflow/tensorflow

Multi-output custom loss model crashes: ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() ... Error occurred when finalizing GeneratorDataset iterator: Failed precondition: Python interpreter state is not initialized. The process may be terminated. [[{{node PyFunc}}]]

@QColeman97 I modified you example also to run to the next TF version. I think that problem is that you are producing a loss: in the log dictionary with an array of batch size len. The monitor is expected to be a single value.

QColeman97

comment created time in 8 days

issue commenttensorflow/addons

TypeError, while creating timeseries windows for training and validation data in TFX

Are you using Tensorflow addons features?

Jaydeemourg

comment created time in 8 days

issue commenttensorflow/addons

Jetson AGX/TX2/Nano - Build release from pip3 install

You could require to use https://github.com/tensorflow/addons/blob/master/tools/install_so_files.sh

Check https://github.com/tensorflow/addons/pull/1909 /cc @seanpmorgan

MI-LA01

comment created time in 8 days

issue commenttensorflow/tensorflow

Illegal instruction (core dumped)

Check https://github.com/tensorflow/tensorflow/issues/24548

codePerfectPlus

comment created time in 9 days

issue commentmicrosoft/vscode-remote-release

Multiple container config

@bamurtaugh Ok can you summarize this thread there?

davidtvs

comment created time in 9 days

issue commenttensorflow/tensorflow

Bug in loading keras model with custom losses (metrics)

@mantonios107 Ok but I am quite sure that nobody will backport this on 2.1

mantonios107

comment created time in 9 days

issue commenttensorflow/tensorflow

Multi-output custom loss model crashes: ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() ... Error occurred when finalizing GeneratorDataset iterator: Failed precondition: Python interpreter state is not initialized. The process may be terminated. [[{{node PyFunc}}]]

The problem is that in the callback your current is an array and self.min_delta is just an int. https://github.com/tensorflow/tensorflow/blob/c587b9a2dcd6595b70b57bd8d8a4563968cdb128/tensorflow/python/keras/callbacks.py#L1762-L1765

QColeman97

comment created time in 9 days

issue commenttensorflow/tensorflow

Illegal instruction (core dumped)

TensorFlow installed from (source or binary): source

Are you compiling TF from source?

codePerfectPlus

comment created time in 9 days

issue commenttensorflow/tensorflow

Bug in loading keras model with custom losses (metrics)

Cause this one is running correctly on colab.

mantonios107

comment created time in 9 days

issue commenttensorflow/tensorflow

Bug in loading keras model with custom losses (metrics)

Yes I meant can I reproduce your issue with your code?

from tensorflow.keras.losses import Loss
import tensorflow as tf
import tensorflow.keras.backend as K
import numpy as np


class BatchMeanSquaredError(Loss):

    def __init__(self, reduction='auto', name='batch_mean_squared_error'):
        super().__init__(reduction=reduction, name=name)

    def call(self, y_true, y_pred):
        y_pred = tf.convert_to_tensor(y_pred)
        y_true = tf.cast(y_true, y_pred.dtype)
        L = K.mean((y_pred - y_true) ** 2, axis=0)
        return L

X = np.random.random((1000, 3))
y = np.ones(shape=(1000, 3))

model = tf.keras.Sequential(
    [
        tf.keras.layers.Dense(3, activation='relu'),
        tf.keras.layers.Dense(3, activation='relu'),
        tf.keras.layers.Dense(3)
    ]
)

bmse = BatchMeanSquaredError()
model.compile(loss=bmse, optimizer='sgd')

model.fit(X, y, batch_size=10, epochs=5)

tf.keras.models.save_model(model=model, filepath='model.h5')

custom_objects = {'BatchMeanSquaredError': BatchMeanSquaredError}
tf.keras.models.load_model('model.h5', custom_objects=custom_objects)
mantonios107

comment created time in 9 days

issue commenttensorflow/tensorflow

Bug in loading keras model with custom losses (metrics)

Can I reproduce with Standalone code to reproduce the issue?

mantonios107

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issue commenttensorflow/tensorflow

Report: AutoGraph could not transform, module 'gast' has no attribute 'Index'

https://docs.python.org/3/library/ast.html Deprecated since version 3.9: Old classes ast.Index and ast.ExtSlice are still available, but they will be removed in future Python releases. In the meantime, instantiating them will return an instance of a different class.

I think this is a different ticket as the version was already reverted https://github.com/tensorflow/tensorflow/commit/710f3c83b4147eb76e748efcc218325c4978726c. /cc @mihaimaruseac

AlexandrParkhomenko

comment created time in 9 days

issue commenttensorflow/tensorflow

Normalization.adapt() not working on tf.data.Dataset()

/cc @MarkDaoust What do you think?

palatos

comment created time in 9 days

issue commenttensorflow/tensorflow

Normalization.adapt() not working on tf.data.Dataset()

This solved the problem! Thank you so much. Maybe this could be added to the documentation at https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing/Normalization ? I'm not sure why the map step is necessary, but in either case it does solve the issue. Thanks once again!

Can you open a new documentation issue? I think that probably we could also raise an exception.

palatos

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issue commentassociazione-rousseau/camelot-flutter

Autofill SMS backend

For the bugs, we can create an issue based on the description. Although it's more work, we need to go through the feedbacks anyway to aggregate them and sort them by count. Same for the most requested features

I suppose It will be quite time consuming on your side if you will start to do It formally.

If we don't have still enought github users around the project we could try to build up at least a prosumers community around the project. I think that entry level users probably will still use the feedback form.

Do you think It is hard to set-up a discourse instance and configure it with Rosseau auth?

emanuelconunaemme

comment created time in 9 days

issue commenttensorflow/addons

Jetson AGX/TX2/Nano - Build release from pip3 install

Yes the problem seems that It cannot find Cudnn files in your system

MI-LA01

comment created time in 9 days

issue commenttensorflow/addons

Jetson AGX/TX2/Nano - Build release from pip3 install

Can you configure in the same python3 env?

MI-LA01

comment created time in 10 days

issue commenttensorflow/addons

Jetson AGX/TX2/Nano - Build release from pip3 install

“ ImportError: No module named pip._internal.req”

MI-LA01

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issue commenttensorflow/addons

Jetson AGX/TX2/Nano - Build release from pip3 install

What Is the error now?

MI-LA01

comment created time in 10 days

issue commenttensorflow/tensorflow

Normalization.adapt() not working on tf.data.Dataset()

I think that you need to use something like:

feature_ds = sample_dataset.map(lambda x, y: x)
normalizer = preprocessing.Normalization()
normalizer.adapt(feature_ds)
palatos

comment created time in 10 days

issue commenttensorflow/tensorflow

Report: AutoGraph could not transform, module 'gast' has no attribute 'Index'

As I see It was a wrongly updated dependency

AlexandrParkhomenko

comment created time in 10 days

push eventtensorflow/addons

Guillaume Klein

commit sha 20886c3b0bef4bd7425d25c9c0628d21a0af89b4

Graduate _BaseAttentionMechanism to a public base class (#2209)

view details

push time in 10 days

PR merged tensorflow/addons

Graduate _BaseAttentionMechanism to a public base class cla: yes seq2seq

Description

See #2197 for additional context.

Type of change

Checklist:

  • [x] I've properly formatted my code according to the guidelines
    • [x] By running Black + Flake8
    • [ ] By running pre-commit hooks
  • [ ] This PR addresses an already submitted issue for TensorFlow Addons
  • [ ] I have made corresponding changes to the documentation
  • [ ] I have added tests that prove my fix is effective or that my feature works
  • [ ] This PR contains modifications to C++ custom-ops

How Has This Been Tested?

Ran the existing tests.

+11 -28

0 comment

1 changed file

guillaumekln

pr closed time in 10 days

issue commenttensorflow/tensorflow

CUDA_ERROR_NOT_INITIALIZED when using multiprocessing with ImageDataGenerator + random hue preprocessing function

@smehra34 If you try with tf-nightly you will see a new warning: WARNING:tensorflow:multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. For high performance data pipelines tf.data is recommended.

smehra34

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issue commenttensorflow/addons

Jetson AGX/TX2/Nano - Build release from pip3 install

Can you try in python:

from pip._internal.req import parse_requirements
MI-LA01

comment created time in 10 days

issue commenttensorflow/addons

Jetson AGX/TX2/Nano - Build release from pip3 install

I think you have problem with pip. Try to force reinstall pip.

MI-LA01

comment created time in 10 days

issue commenttensorflow/tensorflow

Report: AutoGraph could not transform, module 'gast' has no attribute 'Index'

In the meantime you can use pip install gast==0.3.3 with tf-nightly.

AlexandrParkhomenko

comment created time in 10 days

issue commenttensorflow/tensorflow

Report: AutoGraph could not transform, module 'gast' has no attribute 'Index'

Please check https://github.com/serge-sans-paille/gast/issues/53#issuecomment-712421906

AlexandrParkhomenko

comment created time in 10 days

issue commenttensorflow/tensorflow

Downloading pretrained Efficient Det in google colab using TensorFlow Object Detection Api gives a series of unknown warnings?

I think is a dup of https://github.com/tensorflow/tensorflow/issues/40166 /cc @andresp

AshTech25

comment created time in 10 days

issue commenttensorflow/tensorflow

Normalization.adapt() not working on tf.data.Dataset()

Do you have a very minimal but runnable (copy, paste and run) example to reproduce this?

palatos

comment created time in 10 days

issue commenttensorflow/tensorflow

Lr keyword parameter for learning rate does not accept a learning rate schdule

Please check https://github.com/tensorflow/tensorflow/blob/fcc4b966f1265f466e82617020af93670141b009/tensorflow/python/keras/optimizer_v2/adam.py#L362-L363

nsorros

comment created time in 10 days

issue commenttensorflow/addons

Jetson AGX/TX2/Nano - Build release from pip3 install

https://docs.nvidia.com/deeplearning/frameworks/install-tf-jetson-platform/index.html

MI-LA01

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starteddiscourse/discourse-voting

started time in 10 days

issue commentflutter/flutter

Future of the Camera Plugin (Refactor/Rework)

What about desktop support?

bparrishMines

comment created time in 11 days

pull request commentopencv/opencv

fixes #18613

This file has many differences compared to the existing dasiamrpn.py, while they are actually very similar algorithm. I think it would be better if we create a 'tracker' dir under https://github.com/opencv/opencv/blob/master/samples/dnn/ , and move both file inside, so that we could make them share a utils.py which generate anchors etc.

@ZhiyuanChen I think it is ok. Are you interested to open a new PR?

ZhiyuanChen

comment created time in 11 days

PullRequestReviewEvent

pull request commentopencv/opencv

fixes #18613

/cc @jinyup100

ZhiyuanChen

comment created time in 11 days

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