Handwriting Recognition System based on a deep Convolutional Recurrent Neural Network architecture

Edgard Chammas

Last update: Jan 7, 2023

Related tags

Computer Vision machine-learning deep-learning tensorflow cnn rnn handwriting-recognition

Overview

Handwriting Recognition System

This repository is the Tensorflow implementation of the Handwriting Recognition System described in Handwriting Recognition of Historical Documents with Few Labeled Data (please cite the paper if you use this code in your research paper). This code was also used for the baseline system in Fine-tuning Handwriting Recognition systems with Temporal Dropout.

This code is free for academic and research use. For commercial use of the code please contact Edgard Chammas.

To help run the system, sample images from ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset are added.

Configuration

General configuration can be found in config.py

CNN-specific architecture configuration can be found in cnn.py

Training

python train.py

This will generate a text log file and a Tensorflow summary.

Decoding

python test.py

This will generate, for each image, the line transcription. The output will be written to decoded.txt by default.

python compute_probs.py

This will generate, for each image, the posterior probabilities at each timestep. Files will be stored in Probs by default.

Dependencies

Tensorflow
OpenCV-Python

Citation

Please cite the following paper if you use this code in your research paper:

@inproceedings{chammas2018handwriting,
  title={Handwriting Recognition of Historical Documents with few labeled data},
  author={Chammas, Edgard and Mokbel, Chafic and Likforman-Sulem, Laurence},
  booktitle={2018 13th IAPR International Workshop on Document Analysis Systems (DAS)},
  pages={43--48},
  year={2018},
  organization={IEEE}
}

Acknowledgment

We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.

Contributions

Feel free to send your pull request or open issues.

Comments

Problems trying to get this working with a Docker/Podman TF container . .

I am new to both Python (but familiar with C and Ruby) and TF - I have the TF container going OK with a "Hello World" but when I do this:

# podman run -it --rm -v ~phr/src/python/HandwritingRecognitionSystem:/tmp -w /tmp tensorflow/tensorflow python train.py

I get this:

2020-05-04 19:57:54.104113: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory
2020-05-04 19:57:54.104167: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory
2020-05-04 19:57:54.104174: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
Traceback (most recent call last):
  File "train.py", line 11, in <module>
    import cv2
ImportError: No module named cv2

It appears that the TF processor thinks I am trying to use an Nvidia GPU and I have tried a few options I found to tell the system there is no GPU but haven't had any luck yet . . - but there is also the missing module cv2 . .

Suggestions?

Thanks, Phil.

opened by philiprhoades 3

Is this compatible with latest Tensorflow version?

I get the following error

2020-01-23 10:04:15.612666: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer.so.6'; dlerror: libnvinfer.so.6: cannot open shared object file: No such file or directory 2020-01-23 10:04:15.612724: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libnvinfer_plugin.so.6'; dlerror: libnvinfer_plugin.so.6: cannot open shared object file: No such file or directory 2020-01-23 10:04:15.612734: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:30] Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly. Traceback (most recent call last): File "config.py", line 12, in flags = tf.app.flags AttributeError: 'module' object has no attribute 'app'

opened by stevethearkiv 3
Not working Python37
c:\Python37>python.exe HR\train.py W0709 14:09:30.364569 6332 deprecation_wrapper.py:119] From HR\train.py:41: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

W0709 14:09:30.366611 6332 deprecation_wrapper.py:119] From c:\Python37\HR\cnn. py:71: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variabl e_scope instead.

W0709 14:09:30.367612 6332 deprecation_wrapper.py:119] From c:\Python37\HR\util .py:40: The name tf.truncated_normal is deprecated. Please use tf.random.truncat ed_normal instead.

W0709 14:09:30.459672 6332 deprecation_wrapper.py:119] From c:\Python37\HR\util .py:47: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d inste ad.

W0709 14:09:31.796136 6332 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-su nset.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.

W0709 14:09:31.797139 6332 deprecation.py:323] From c:\Python37\HR\rnn.py:73: L STMCell.init (from tensorflow.python.ops.rnn_cell_impl) is deprecated and wi ll be removed in a future version. Instructions for updating: This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by th at in Tensorflow 2.0. W0709 14:09:31.798139 6332 deprecation.py:323] From c:\Python37\HR\rnn.py:74: M ultiRNNCell.init (from tensorflow.python.ops.rnn_cell_impl) is deprecated an d will be removed in a future version. Instructions for updating: This class is equivalent as tf.keras.layers.StackedRNNCells, and will be replace d by that in Tensorflow 2.0. W0709 14:09:31.800641 6332 deprecation.py:323] From c:\Python37\HR\rnn.py:81: b idirectional_dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version. Instructions for updating: Please use keras.layers.Bidirectional(keras.layers.RNN(cell)), which is equiva lent to this API W0709 14:09:31.801141 6332 deprecation.py:323] From c:\Python37\lib\site-packag es\tensorflow\python\ops\rnn.py:464: dynamic_rnn (from tensorflow.python.ops.rnn ) is deprecated and will be removed in a future version. Instructions for updating: Please use keras.layers.RNN(cell), which is equivalent to this API W0709 14:09:32.159433 6332 deprecation.py:506] From c:\Python37\lib\site-packag es\tensorflow\python\ops\rnn_cell_impl.py:961: calling Zeros.init (from tens orflow.python.ops.init_ops) with dtype is deprecated and will be removed in a fu ture version. Instructions for updating: Call initializer instance with the dtype argument instead of passing it to the c onstructor W0709 14:09:32.971470 6332 deprecation.py:323] From c:\Python37\lib\site-packag es\tensorflow\python\ops\rnn.py:244: add_dispatch_support..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 W0709 14:09:33.300187 6332 deprecation_wrapper.py:119] From HR\train.py:61: The name tf.nn.ctc_loss is deprecated. Please use tf.compat.v1.nn.ctc_loss instead.

W0709 14:09:33.302189 6332 deprecation_wrapper.py:119] From HR\train.py:65: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar in stead.

W0709 14:09:33.304190 6332 deprecation.py:323] From HR\train.py:70: to_int32 (f rom tensorflow.python.ops.math_ops) is deprecated and will be removed in a futur e version. Instructions for updating: Use tf.cast instead. W0709 14:09:33.309194 6332 deprecation.py:323] From HR\train.py:72: to_float (f rom tensorflow.python.ops.math_ops) is deprecated and will be removed in a futur e version. Instructions for updating: Use tf.cast instead. W0709 14:09:33.311257 6332 deprecation_wrapper.py:119] From HR\train.py:76: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_va riables instead.

W0709 14:09:35.607799 6332 deprecation_wrapper.py:119] From HR\train.py:80: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOp timizer instead.

Reading ./samples/Images//000033/READ17.000033_01_01_pg_line_1486644351571_780.p ng Reading ./samples/Images//000033/READ17.000033_01_02_pg_line_1486644356245_781.p ng Traceback (most recent call last): File "HR\train.py", line 95, in inputList, seqLens, targetList = ReadData(cfg.TRAIN_LOCATION, cfg.TRAIN_LIST , cfg.TRAIN_NB, WND_HEIGHT, WND_WIDTH, WND_SHIFT, VEC_PER_WND, cfg.TRAIN_TRANS) File "c:\Python37\HR\util.py", line 130, in ReadData h, w = np.shape(image) ValueError: not enough values to unpack (expected 2, got 0)

c:\Python37>
opened by JonhSilver 3
GENERATE CHECK POINT

Hi, i passed the training step (using train.py file) and model directory is created but the thing is there is no model at all. However i can see the log file but no checkpoint at all.

In shot, i want to ask how to generate a checkpoint (model) to test this script.

Thanks

opened by PrakharPrashantSingh 2
Bump tensorflow-gpu from 2.5.3 to 2.7.2
Bumps tensorflow-gpu from 2.5.3 to 2.7.2.

Release notes

Sourced from tensorflow-gpu's releases.

TensorFlow 2.7.2

Release 2.7.2

This releases introduces several vulnerability fixes:

Fixes a code injection in saved_model_cli (CVE-2022-29216)

Fixes a missing validation which causes TensorSummaryV2 to crash (CVE-2022-29193)

Fixes a missing validation which crashes QuantizeAndDequantizeV4Grad (CVE-2022-29192)

Fixes a missing validation which causes denial of service via DeleteSessionTensor (CVE-2022-29194)

Fixes a missing validation which causes denial of service via GetSessionTensor (CVE-2022-29191)

Fixes a missing validation which causes denial of service via StagePeek (CVE-2022-29195)

Fixes a missing validation which causes denial of service via UnsortedSegmentJoin (CVE-2022-29197)

Fixes a missing validation which causes denial of service via LoadAndRemapMatrix (CVE-2022-29199)

Fixes a missing validation which causes denial of service via SparseTensorToCSRSparseMatrix (CVE-2022-29198)

Fixes a missing validation which causes denial of service via LSTMBlockCell (CVE-2022-29200)

Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29196)

Fixes a CHECK failure in depthwise ops via overflows (CVE-2021-41197)

Fixes issues arising from undefined behavior stemming from users supplying invalid resource handles (CVE-2022-29207)

Fixes a segfault due to missing support for quantized types (CVE-2022-29205)

Fixes a missing validation which results in undefined behavior in SparseTensorDenseAdd (CVE-2022-29206)

Fixes a missing validation which results in undefined behavior in QuantizedConv2D (CVE-2022-29201)

Fixes an integer overflow in SpaceToBatchND (CVE-2022-29203)

Fixes a segfault and OOB write due to incomplete validation in EditDistance (CVE-2022-29208)

Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29204)

Fixes a denial of service in tf.ragged.constant due to lack of validation (CVE-2022-29202)

Fixes a segfault when tf.histogram_fixed_width is called with NaN values (CVE-2022-29211)

Fixes a core dump when loading TFLite models with quantization (CVE-2022-29212)

Fixes crashes stemming from incomplete validation in signal ops (CVE-2022-29213)

Fixes a type confusion leading to CHECK-failure based denial of service (CVE-2022-29209)

Updates curl to 7.83.1 to handle (CVE-2022-22576, (CVE-2022-27774, (CVE-2022-27775, (CVE-2022-27776, (CVE-2022-27778, (CVE-2022-27779, (CVE-2022-27780, (CVE-2022-27781, (CVE-2022-27782 and (CVE-2022-30115

Updates zlib to 1.2.12 after 1.2.11 was pulled due to security issue

TensorFlow 2.7.1

Release 2.7.1

This releases introduces several vulnerability fixes:

Fixes a floating point division by 0 when executing convolution operators (CVE-2022-21725)

Fixes a heap OOB read in shape inference for ReverseSequence (CVE-2022-21728)

Fixes a heap OOB access in Dequantize (CVE-2022-21726)

Fixes an integer overflow in shape inference for Dequantize (CVE-2022-21727)

Fixes a heap OOB access in FractionalAvgPoolGrad (CVE-2022-21730)

Fixes an overflow and divide by zero in UnravelIndex (CVE-2022-21729)

Fixes a type confusion in shape inference for ConcatV2 (CVE-2022-21731)

Fixes an OOM in ThreadPoolHandle (CVE-2022-21732)

Fixes an OOM due to integer overflow in StringNGrams (CVE-2022-21733)

Fixes more issues caused by incomplete validation in boosted trees code (CVE-2021-41208)

Fixes an integer overflows in most sparse component-wise ops (CVE-2022-23567)

Fixes an integer overflows in AddManySparseToTensorsMap (CVE-2022-23568)

... (truncated)

Changelog

Sourced from tensorflow-gpu's changelog.

Release 2.7.2

This releases introduces several vulnerability fixes:

Fixes a code injection in saved_model_cli (CVE-2022-29216)

Fixes a missing validation which causes TensorSummaryV2 to crash (CVE-2022-29193)

Fixes a missing validation which crashes QuantizeAndDequantizeV4Grad (CVE-2022-29192)

Fixes a missing validation which causes denial of service via DeleteSessionTensor (CVE-2022-29194)

Fixes a missing validation which causes denial of service via GetSessionTensor (CVE-2022-29191)

Fixes a missing validation which causes denial of service via StagePeek (CVE-2022-29195)

Fixes a missing validation which causes denial of service via UnsortedSegmentJoin (CVE-2022-29197)

Fixes a missing validation which causes denial of service via LoadAndRemapMatrix (CVE-2022-29199)

Fixes a missing validation which causes denial of service via SparseTensorToCSRSparseMatrix (CVE-2022-29198)

Fixes a missing validation which causes denial of service via LSTMBlockCell (CVE-2022-29200)

Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29196)

Fixes a CHECK failure in depthwise ops via overflows (CVE-2021-41197)

Fixes issues arising from undefined behavior stemming from users supplying invalid resource handles (CVE-2022-29207)

Fixes a segfault due to missing support for quantized types (CVE-2022-29205)

Fixes a missing validation which results in undefined behavior in SparseTensorDenseAdd (CVE-2022-29206)

Fixes a missing validation which results in undefined behavior in QuantizedConv2D (CVE-2022-29201)

Fixes an integer overflow in SpaceToBatchND (CVE-2022-29203)

Fixes a segfault and OOB write due to incomplete validation in EditDistance (CVE-2022-29208)

Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29204)

Fixes a denial of service in tf.ragged.constant due to lack of validation (CVE-2022-29202)

Fixes a segfault when tf.histogram_fixed_width is called with NaN values (CVE-2022-29211)

Fixes a core dump when loading TFLite models with quantization (CVE-2022-29212)

Fixes crashes stemming from incomplete validation in signal ops (CVE-2022-29213)

Fixes a type confusion leading to CHECK-failure based denial of service (CVE-2022-29209)

Updates curl to 7.83.1 to handle (CVE-2022-22576, (CVE-2022-27774, (CVE-2022-27775, (CVE-2022-27776, (CVE-2022-27778, (CVE-2022-27779, (CVE-2022-27780, (CVE-2022-27781, (CVE-2022-27782 and (CVE-2022-30115

Updates zlib to 1.2.12 after 1.2.11 was pulled due to security issue

Release 2.6.4

This releases introduces several vulnerability fixes:

Fixes a code injection in saved_model_cli (CVE-2022-29216)

Fixes a missing validation which causes TensorSummaryV2 to crash (CVE-2022-29193)

Fixes a missing validation which crashes QuantizeAndDequantizeV4Grad (CVE-2022-29192)

Fixes a missing validation which causes denial of service via DeleteSessionTensor (CVE-2022-29194)

Fixes a missing validation which causes denial of service via GetSessionTensor (CVE-2022-29191)

Fixes a missing validation which causes denial of service via StagePeek (CVE-2022-29195)

Fixes a missing validation which causes denial of service via UnsortedSegmentJoin (CVE-2022-29197)

Fixes a missing validation which causes denial of service via LoadAndRemapMatrix (CVE-2022-29199)

Fixes a missing validation which causes denial of service via SparseTensorToCSRSparseMatrix (CVE-2022-29198)

Fixes a missing validation which causes denial of service via LSTMBlockCell (CVE-2022-29200)

Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29196)

Fixes a CHECK failure in depthwise ops via overflows (CVE-2021-41197)

Fixes issues arising from undefined behavior stemming from users supplying invalid resource handles (CVE-2022-29207)

Fixes a segfault due to missing support for quantized types (CVE-2022-29205)

Fixes a missing validation which results in undefined behavior in SparseTensorDenseAdd (CVE-2022-29206)

... (truncated)

Commits

dd7b8a3 Merge pull request #56034 from tensorflow-jenkins/relnotes-2.7.2-15779

1e7d6ea Update RELEASE.md

5085135 Merge pull request #56069 from tensorflow/mm-cp-52488e5072f6fe44411d70c6af09e...

adafb45 Merge pull request #56060 from yongtang:curl-7.83.1

01cb1b8 Merge pull request #56038 from tensorflow-jenkins/version-numbers-2.7.2-4733

8c90c2f Update version numbers to 2.7.2

43f3cdc Update RELEASE.md

98b0a48 Insert release notes place-fill

dfa5cf3 Merge pull request #56028 from tensorflow/disable-tests-on-r2.7

501a65c Disable timing out tests

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dependencies
opened by dependabot[bot] 1
Bump tensorflow-gpu from 2.5.3 to 2.6.4
Bumps tensorflow-gpu from 2.5.3 to 2.6.4.

Release notes

Sourced from tensorflow-gpu's releases.

TensorFlow 2.6.4

Release 2.6.4

This releases introduces several vulnerability fixes:

Fixes a code injection in saved_model_cli (CVE-2022-29216)

Fixes a missing validation which causes TensorSummaryV2 to crash (CVE-2022-29193)

Fixes a missing validation which crashes QuantizeAndDequantizeV4Grad (CVE-2022-29192)

Fixes a missing validation which causes denial of service via DeleteSessionTensor (CVE-2022-29194)

Fixes a missing validation which causes denial of service via GetSessionTensor (CVE-2022-29191)

Fixes a missing validation which causes denial of service via StagePeek (CVE-2022-29195)

Fixes a missing validation which causes denial of service via UnsortedSegmentJoin (CVE-2022-29197)

Fixes a missing validation which causes denial of service via LoadAndRemapMatrix (CVE-2022-29199)

Fixes a missing validation which causes denial of service via SparseTensorToCSRSparseMatrix (CVE-2022-29198)

Fixes a missing validation which causes denial of service via LSTMBlockCell (CVE-2022-29200)

Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29196)

Fixes a CHECK failure in depthwise ops via overflows (CVE-2021-41197)

Fixes issues arising from undefined behavior stemming from users supplying invalid resource handles (CVE-2022-29207)

Fixes a segfault due to missing support for quantized types (CVE-2022-29205)

Fixes a missing validation which results in undefined behavior in SparseTensorDenseAdd (CVE-2022-29206)

Fixes a missing validation which results in undefined behavior in QuantizedConv2D (CVE-2022-29201)

Fixes an integer overflow in SpaceToBatchND (CVE-2022-29203)

Fixes a segfault and OOB write due to incomplete validation in EditDistance (CVE-2022-29208)

Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29204)

Fixes a denial of service in tf.ragged.constant due to lack of validation (CVE-2022-29202)

Fixes a segfault when tf.histogram_fixed_width is called with NaN values (CVE-2022-29211)

Fixes a core dump when loading TFLite models with quantization (CVE-2022-29212)

Fixes crashes stemming from incomplete validation in signal ops (CVE-2022-29213)

Fixes a type confusion leading to CHECK-failure based denial of service (CVE-2022-29209)

Updates curl to 7.83.1 to handle (CVE-2022-22576, (CVE-2022-27774, (CVE-2022-27775, (CVE-2022-27776, (CVE-2022-27778, (CVE-2022-27779, (CVE-2022-27780, (CVE-2022-27781, (CVE-2022-27782 and (CVE-2022-30115

Updates zlib to 1.2.12 after 1.2.11 was pulled due to security issue

TensorFlow 2.6.3

Release 2.6.3

This releases introduces several vulnerability fixes:

Fixes a floating point division by 0 when executing convolution operators (CVE-2022-21725)

Fixes a heap OOB read in shape inference for ReverseSequence (CVE-2022-21728)

Fixes a heap OOB access in Dequantize (CVE-2022-21726)

Fixes an integer overflow in shape inference for Dequantize (CVE-2022-21727)

Fixes a heap OOB access in FractionalAvgPoolGrad (CVE-2022-21730)

Fixes an overflow and divide by zero in UnravelIndex (CVE-2022-21729)

Fixes a type confusion in shape inference for ConcatV2 (CVE-2022-21731)

Fixes an OOM in ThreadPoolHandle (CVE-2022-21732)

Fixes an OOM due to integer overflow in StringNGrams (CVE-2022-21733)

Fixes more issues caused by incomplete validation in boosted trees code (CVE-2021-41208)

Fixes an integer overflows in most sparse component-wise ops (CVE-2022-23567)

Fixes an integer overflows in AddManySparseToTensorsMap (CVE-2022-23568)

Fixes a number of CHECK-failures in MapStage (CVE-2022-21734)

... (truncated)

Changelog

Sourced from tensorflow-gpu's changelog.

Release 2.6.4

This releases introduces several vulnerability fixes:

Fixes a code injection in saved_model_cli (CVE-2022-29216)

Fixes a missing validation which causes TensorSummaryV2 to crash (CVE-2022-29193)

Fixes a missing validation which crashes QuantizeAndDequantizeV4Grad (CVE-2022-29192)

Fixes a missing validation which causes denial of service via DeleteSessionTensor (CVE-2022-29194)

Fixes a missing validation which causes denial of service via GetSessionTensor (CVE-2022-29191)

Fixes a missing validation which causes denial of service via StagePeek (CVE-2022-29195)

Fixes a missing validation which causes denial of service via UnsortedSegmentJoin (CVE-2022-29197)

Fixes a missing validation which causes denial of service via LoadAndRemapMatrix (CVE-2022-29199)

Fixes a missing validation which causes denial of service via SparseTensorToCSRSparseMatrix (CVE-2022-29198)

Fixes a missing validation which causes denial of service via LSTMBlockCell (CVE-2022-29200)

Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29196)

Fixes a CHECK failure in depthwise ops via overflows (CVE-2021-41197)

Fixes issues arising from undefined behavior stemming from users supplying invalid resource handles (CVE-2022-29207)

Fixes a segfault due to missing support for quantized types (CVE-2022-29205)

Fixes a missing validation which results in undefined behavior in SparseTensorDenseAdd (CVE-2022-29206)

Fixes a missing validation which results in undefined behavior in QuantizedConv2D (CVE-2022-29201)

Fixes an integer overflow in SpaceToBatchND (CVE-2022-29203)

Fixes a segfault and OOB write due to incomplete validation in EditDistance (CVE-2022-29208)

Fixes a missing validation which causes denial of service via Conv3DBackpropFilterV2 (CVE-2022-29204)

Fixes a denial of service in tf.ragged.constant due to lack of validation (CVE-2022-29202)

Fixes a segfault when tf.histogram_fixed_width is called with NaN values (CVE-2022-29211)

Fixes a core dump when loading TFLite models with quantization (CVE-2022-29212)

Fixes crashes stemming from incomplete validation in signal ops (CVE-2022-29213)

Fixes a type confusion leading to CHECK-failure based denial of service (CVE-2022-29209)

Updates curl to 7.83.1 to handle (CVE-2022-22576, (CVE-2022-27774, (CVE-2022-27775, (CVE-2022-27776, (CVE-2022-27778, (CVE-2022-27779, (CVE-2022-27780, (CVE-2022-27781, (CVE-2022-27782 and (CVE-2022-30115

Updates zlib to 1.2.12 after 1.2.11 was pulled due to security issue

Release 2.8.0

Major Features and Improvements

tf.lite:

Added TFLite builtin op support for the following TF ops:

tf.raw_ops.Bucketize op on CPU.

tf.where op for data types tf.int32/tf.uint32/tf.int8/tf.uint8/tf.int64.

tf.random.normal op for output data type tf.float32 on CPU.

tf.random.uniform op for output data type tf.float32 on CPU.

tf.random.categorical op for output data type tf.int64 on CPU.

tensorflow.experimental.tensorrt:

conversion_params is now deprecated inside TrtGraphConverterV2 in favor of direct arguments: max_workspace_size_bytes, precision_mode, minimum_segment_size, maximum_cached_engines, use_calibration and

... (truncated)

Commits

33ed2b1 Merge pull request #56102 from tensorflow/mihaimaruseac-patch-1

e1ec480 Fix build due to importlib-metadata/setuptools

63f211c Merge pull request #56033 from tensorflow-jenkins/relnotes-2.6.4-6677

22b8fe4 Update RELEASE.md

ec30684 Merge pull request #56070 from tensorflow/mm-cp-adafb45c781-on-r2.6

38774ed Merge pull request #56060 from yongtang:curl-7.83.1

9ef1604 Merge pull request #56036 from tensorflow-jenkins/version-numbers-2.6.4-9925

a6526a3 Update version numbers to 2.6.4

cb1a481 Update RELEASE.md

4da550f Insert release notes place-fill

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dependencies
opened by dependabot[bot] 1
Bump tensorflow-gpu from 2.4.1 to 2.5.2
Bumps tensorflow-gpu from 2.4.1 to 2.5.2.

Release notes

Sourced from tensorflow-gpu's releases.

TensorFlow 2.5.2

Release 2.5.2

This release introduces several vulnerability fixes:

Fixes a code injection issue in saved_model_cli (CVE-2021-41228)

Fixes a vulnerability due to use of uninitialized value in Tensorflow (CVE-2021-41225)

Fixes a heap OOB in FusedBatchNorm kernels (CVE-2021-41223)

Fixes an arbitrary memory read in ImmutableConst (CVE-2021-41227)

Fixes a heap OOB in SparseBinCount (CVE-2021-41226)

Fixes a heap OOB in SparseFillEmptyRows (CVE-2021-41224)

Fixes a segfault due to negative splits in SplitV (CVE-2021-41222)

Fixes segfaults and vulnerabilities caused by accesses to invalid memory during shape inference in Cudnn* ops (CVE-2021-41221)

Fixes a null pointer exception when Exit node is not preceded by Enter op (CVE-2021-41217)

Fixes an integer division by 0 in tf.raw_ops.AllToAll (CVE-2021-41218)

Fixes an undefined behavior via nullptr reference binding in sparse matrix multiplication (CVE-2021-41219)

Fixes a heap buffer overflow in Transpose (CVE-2021-41216)

Prevents deadlocks arising from mutually recursive tf.function objects (CVE-2021-41213)

Fixes a null pointer exception in DeserializeSparse (CVE-2021-41215)

Fixes an undefined behavior arising from reference binding to nullptr in tf.ragged.cross (CVE-2021-41214)

Fixes a heap OOB read in tf.ragged.cross (CVE-2021-41212)

Fixes a heap OOB read in all tf.raw_ops.QuantizeAndDequantizeV* ops (CVE-2021-41205)

Fixes an FPE in ParallelConcat (CVE-2021-41207)

Fixes FPE issues in convolutions with zero size filters (CVE-2021-41209)

Fixes a heap OOB read in tf.raw_ops.SparseCountSparseOutput (CVE-2021-41210)

Fixes vulnerabilities caused by incomplete validation in boosted trees code (CVE-2021-41208)

Fixes vulnerabilities caused by incomplete validation of shapes in multiple TF ops (CVE-2021-41206)

Fixes a segfault produced while copying constant resource tensor (CVE-2021-41204)

Fixes a vulnerability caused by unitialized access in EinsumHelper::ParseEquation (CVE-2021-41201)

Fixes several vulnerabilities and segfaults caused by missing validation during checkpoint loading (CVE-2021-41203)

Fixes an overflow producing a crash in tf.range (CVE-2021-41202)

Fixes an overflow producing a crash in tf.image.resize when size is large (CVE-2021-41199)

Fixes an overflow producing a crash in tf.tile when tiling tensor is large (CVE-2021-41198)

Fixes a vulnerability produced due to incomplete validation in tf.summary.create_file_writer (CVE-2021-41200)

Fixes multiple crashes due to overflow and CHECK-fail in ops with large tensor shapes (CVE-2021-41197)

Fixes a crash in max_pool3d when size argument is 0 or negative (CVE-2021-41196)

Fixes a crash in tf.math.segment_* operations (CVE-2021-41195)

Updates curl to 7.78.0 to handle CVE-2021-22922, CVE-2021-22923, CVE-2021-22924, CVE-2021-22925, and CVE-2021-22926.

TensorFlow 2.5.1

Release 2.5.1

This release introduces several vulnerability fixes:

Fixes a heap out of bounds access in sparse reduction operations (CVE-2021-37635)

Fixes a floating point exception in SparseDenseCwiseDiv (CVE-2021-37636)

Fixes a null pointer dereference in CompressElement (CVE-2021-37637)

Fixes a null pointer dereference in RaggedTensorToTensor (CVE-2021-37638)

Fixes a null pointer dereference and a heap OOB read arising from operations restoring tensors (CVE-2021-37639)

Fixes an integer division by 0 in sparse reshaping (CVE-2021-37640)

... (truncated)

Changelog

Sourced from tensorflow-gpu's changelog.

Release 2.5.2

This release introduces several vulnerability fixes:

Fixes a code injection issue in saved_model_cli (CVE-2021-41228)

Fixes a vulnerability due to use of uninitialized value in Tensorflow (CVE-2021-41225)

Fixes a heap OOB in FusedBatchNorm kernels (CVE-2021-41223)

Fixes an arbitrary memory read in ImmutableConst (CVE-2021-41227)

Fixes a heap OOB in SparseBinCount (CVE-2021-41226)

Fixes a heap OOB in SparseFillEmptyRows (CVE-2021-41224)

Fixes a segfault due to negative splits in SplitV (CVE-2021-41222)

Fixes segfaults and vulnerabilities caused by accesses to invalid memory during shape inference in Cudnn* ops (CVE-2021-41221)

Fixes a null pointer exception when Exit node is not preceded by Enter op (CVE-2021-41217)

Fixes an integer division by 0 in tf.raw_ops.AllToAll (CVE-2021-41218)

Fixes an undefined behavior via nullptr reference binding in sparse matrix multiplication (CVE-2021-41219)

Fixes a heap buffer overflow in Transpose (CVE-2021-41216)

Prevents deadlocks arising from mutually recursive tf.function objects (CVE-2021-41213)

Fixes a null pointer exception in DeserializeSparse (CVE-2021-41215)

Fixes an undefined behavior arising from reference binding to nullptr in tf.ragged.cross (CVE-2021-41214)

Fixes a heap OOB read in tf.ragged.cross (CVE-2021-41212)

Fixes a heap OOB read in all tf.raw_ops.QuantizeAndDequantizeV* ops (CVE-2021-41205)

Fixes an FPE in ParallelConcat ([CVE-2021-41207] (https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2021-41207))

Fixes FPE issues in convolutions with zero size filters (CVE-2021-41209)

Fixes a heap OOB read in tf.raw_ops.SparseCountSparseOutput (CVE-2021-41210)

Fixes vulnerabilities caused by incomplete validation in boosted trees code (CVE-2021-41208)

Fixes vulnerabilities caused by incomplete validation of shapes in multiple TF ops (CVE-2021-41206)

... (truncated)

Commits

957590e Merge pull request #52873 from tensorflow-jenkins/relnotes-2.5.2-20787

2e1d16d Update RELEASE.md

2fa6dd9 Merge pull request #52877 from tensorflow-jenkins/version-numbers-2.5.2-192

4807489 Merge pull request #52881 from tensorflow/fix-build-1-on-r2.5

d398bdf Disable failing test

857ad5e Merge pull request #52878 from tensorflow/fix-build-1-on-r2.5

6c2a215 Disable failing test

f5c57d4 Update version numbers to 2.5.2

e51f949 Insert release notes place-fill

2620d2c Merge pull request #52863 from tensorflow/fix-build-3-on-r2.5

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dependencies
opened by dependabot[bot] 1
Bump tensorflow-gpu from 2.4.1 to 2.5.1
Bumps tensorflow-gpu from 2.4.1 to 2.5.1.

Release notes

Sourced from tensorflow-gpu's releases.

TensorFlow 2.5.1

Release 2.5.1

This release introduces several vulnerability fixes:

Fixes a heap out of bounds access in sparse reduction operations (CVE-2021-37635)

Fixes a floating point exception in SparseDenseCwiseDiv (CVE-2021-37636)

Fixes a null pointer dereference in CompressElement (CVE-2021-37637)

Fixes a null pointer dereference in RaggedTensorToTensor (CVE-2021-37638)

Fixes a null pointer dereference and a heap OOB read arising from operations restoring tensors (CVE-2021-37639)

Fixes an integer division by 0 in sparse reshaping (CVE-2021-37640)

Fixes a division by 0 in ResourceScatterDiv (CVE-2021-37642)

Fixes a heap OOB in RaggedGather (CVE-2021-37641)

Fixes a std::abort raised from TensorListReserve (CVE-2021-37644)

Fixes a null pointer dereference in MatrixDiagPartOp (CVE-2021-37643)

Fixes an integer overflow due to conversion to unsigned (CVE-2021-37645)

Fixes a bad allocation error in StringNGrams caused by integer conversion (CVE-2021-37646)

Fixes a null pointer dereference in SparseTensorSliceDataset (CVE-2021-37647)

Fixes an incorrect validation of SaveV2 inputs (CVE-2021-37648)

Fixes a null pointer dereference in UncompressElement (CVE-2021-37649)

Fixes a segfault and a heap buffer overflow in {Experimental,}DatasetToTFRecord (CVE-2021-37650)

Fixes a heap buffer overflow in FractionalAvgPoolGrad (CVE-2021-37651)

Fixes a use after free in boosted trees creation (CVE-2021-37652)

Fixes a division by 0 in ResourceGather (CVE-2021-37653)

Fixes a heap OOB and a CHECK fail in ResourceGather (CVE-2021-37654)

Fixes a heap OOB in ResourceScatterUpdate (CVE-2021-37655)

Fixes an undefined behavior arising from reference binding to nullptr in RaggedTensorToSparse (CVE-2021-37656)

Fixes an undefined behavior arising from reference binding to nullptr in MatrixDiagV* ops (CVE-2021-37657)

Fixes an undefined behavior arising from reference binding to nullptr in MatrixSetDiagV* ops (CVE-2021-37658)

Fixes an undefined behavior arising from reference binding to nullptr and heap OOB in binary cwise ops (CVE-2021-37659)

Fixes a division by 0 in inplace operations (CVE-2021-37660)

Fixes a crash caused by integer conversion to unsigned (CVE-2021-37661)

Fixes an undefined behavior arising from reference binding to nullptr in boosted trees (CVE-2021-37662)

Fixes a heap OOB in boosted trees (CVE-2021-37664)

Fixes vulnerabilities arising from incomplete validation in QuantizeV2 (CVE-2021-37663)

Fixes vulnerabilities arising from incomplete validation in MKL requantization (CVE-2021-37665)

Fixes an undefined behavior arising from reference binding to nullptr in RaggedTensorToVariant (CVE-2021-37666)

Fixes an undefined behavior arising from reference binding to nullptr in unicode encoding (CVE-2021-37667)

Fixes an FPE in tf.raw_ops.UnravelIndex (CVE-2021-37668)

Fixes a crash in NMS ops caused by integer conversion to unsigned (CVE-2021-37669)

Fixes a heap OOB in UpperBound and LowerBound (CVE-2021-37670)

Fixes an undefined behavior arising from reference binding to nullptr in map operations (CVE-2021-37671)

Fixes a heap OOB in SdcaOptimizerV2 (CVE-2021-37672)

Fixes a CHECK-fail in MapStage (CVE-2021-37673)

Fixes a vulnerability arising from incomplete validation in MaxPoolGrad (CVE-2021-37674)

Fixes an undefined behavior arising from reference binding to nullptr in shape inference (CVE-2021-37676)

Fixes a division by 0 in most convolution operators (CVE-2021-37675)

Fixes vulnerabilities arising from missing validation in shape inference for Dequantize (CVE-2021-37677)

Fixes an arbitrary code execution due to YAML deserialization (CVE-2021-37678)

Fixes a heap OOB in nested tf.map_fn with RaggedTensors (CVE-2021-37679)

... (truncated)

Changelog

Sourced from tensorflow-gpu's changelog.

Release 2.5.1

This release introduces several vulnerability fixes:

Fixes a heap out of bounds access in sparse reduction operations (CVE-2021-37635)

Fixes a floating point exception in SparseDenseCwiseDiv (CVE-2021-37636)

Fixes a null pointer dereference in CompressElement (CVE-2021-37637)

Fixes a null pointer dereference in RaggedTensorToTensor (CVE-2021-37638)

Fixes a null pointer dereference and a heap OOB read arising from operations restoring tensors (CVE-2021-37639)

Fixes an integer division by 0 in sparse reshaping (CVE-2021-37640)

Fixes a division by 0 in ResourceScatterDiv (CVE-2021-37642)

Fixes a heap OOB in RaggedGather (CVE-2021-37641)

Fixes a std::abort raised from TensorListReserve (CVE-2021-37644)

Fixes a null pointer dereference in MatrixDiagPartOp (CVE-2021-37643)

Fixes an integer overflow due to conversion to unsigned (CVE-2021-37645)

Fixes a bad allocation error in StringNGrams caused by integer conversion (CVE-2021-37646)

Fixes a null pointer dereference in SparseTensorSliceDataset (CVE-2021-37647)

Fixes an incorrect validation of SaveV2 inputs (CVE-2021-37648)

Fixes a null pointer dereference in UncompressElement (CVE-2021-37649)

Fixes a segfault and a heap buffer overflow in {Experimental,}DatasetToTFRecord (CVE-2021-37650)

Fixes a heap buffer overflow in FractionalAvgPoolGrad (CVE-2021-37651)

Fixes a use after free in boosted trees creation (CVE-2021-37652)

Fixes a division by 0 in ResourceGather (CVE-2021-37653)

Fixes a heap OOB and a CHECK fail in ResourceGather (CVE-2021-37654)

Fixes a heap OOB in ResourceScatterUpdate (CVE-2021-37655)

Fixes an undefined behavior arising from reference binding to nullptr in RaggedTensorToSparse

... (truncated)

Commits

8222c1c Merge pull request #51381 from tensorflow/mm-fix-r2.5-build

d584260 Disable broken/flaky test

f6c6ce3 Merge pull request #51367 from tensorflow-jenkins/version-numbers-2.5.1-17468

3ca7812 Update version numbers to 2.5.1

4fdf683 Merge pull request #51361 from tensorflow/mm-update-relnotes-on-r2.5

05fc01a Put CVE numbers for fixes in parentheses

bee1dc4 Update release notes for the new patch release

47beb4c Merge pull request #50597 from kruglov-dmitry/v2.5.0-sync-abseil-cmake-bazel

6f39597 Merge pull request #49383 from ashahab/abin-load-segfault-r2.5

0539b34 Merge pull request #48979 from liufengdb/r2.5-cherrypick

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dependencies
opened by dependabot[bot] 1
How to train the model on the full image recognition after the training on the samples

Hello, I found your code is amazing and give best results for the handwriting document recognition ,but when I work on it I found difficulties to train the model on the full page after the manually labeled text-line images in the samples?

Please can you give me a samples for "how to train the model on full images and the .tru file of it"?

because I am trying to run it on a different data set for my research and your paper is the state-of-the-art solution. and I study the config.py but I don't find the way to add full image after the train on lines image

opened by engreememad 1
Training Data and `No checkpoint found`

The README says

To help run the system, sample images from ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset are added.

I tried reproducing the results of your work by running python3 compute_probs.py and python3 test.py both of which yielded No checkpoint found after the script reads images successfully. Reading ./samples/Images//000033/READ17.000033_01_18_pg_line_1486644440417_797.png Reading ./samples/Images//000033/READ17.000033_01_19_pg_line_1486644445527_798.png Reading ./samples/Images//000033/READ17.000033_01_20_pg_line_1486644453911_799.png Initializing... 2021-04-06 08:41:30.589865: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorF low binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.

Would you mind sharing the checkpoint file?

Can the ICDAR2017 Competition on Handwritten Text Recognition on the READ Dataset be used to train the model? If yes, please provide details on it's implementation. The link to the dataset in the README is also broken, is this the same dataset?

Please Advise.

Thank you for your time.

opened by ArunLukeDsouza 1
Bump tensorflow-gpu from 1.13.1 to 2.4.0
Bumps tensorflow-gpu from 1.13.1 to 2.4.0.

Release notes

Sourced from tensorflow-gpu's releases.

TensorFlow 2.4.0

Release 2.4.0

Major Features and Improvements

tf.distribute introduces experimental support for asynchronous training of models via the tf.distribute.experimental.ParameterServerStrategy API. Please see the tutorial to learn more.

MultiWorkerMirroredStrategy is now a stable API and is no longer considered experimental. Some of the major improvements involve handling peer failure and many bug fixes. Please check out the detailed tutorial on Multi-worker training with Keras.

Introduces experimental support for a new module named tf.experimental.numpy which is a NumPy-compatible API for writing TF programs. See the detailed guide to learn more. Additional details below.

Adds Support for TensorFloat-32 on Ampere based GPUs. TensorFloat-32, or TF32 for short, is a math mode for NVIDIA Ampere based GPUs and is enabled by default.

A major refactoring of the internals of the Keras Functional API has been completed, that should improve the reliability, stability, and performance of constructing Functional models.

Keras mixed precision API tf.keras.mixed_precision is no longer experimental and allows the use of 16-bit floating point formats during training, improving performance by up to 3x on GPUs and 60% on TPUs. Please see below for additional details.

TensorFlow Profiler now supports profiling MultiWorkerMirroredStrategy and tracing multiple workers using the sampling mode API.

TFLite Profiler for Android is available. See the detailed guide to learn more.

TensorFlow pip packages are now built with CUDA11 and cuDNN 8.0.2.

Breaking Changes

TF Core:

Certain float32 ops run in lower precsion on Ampere based GPUs, including matmuls and convolutions, due to the use of TensorFloat-32. Specifically, inputs to such ops are rounded from 23 bits of precision to 10 bits of precision. This is unlikely to cause issues in practice for deep learning models. In some cases, TensorFloat-32 is also used for complex64 ops. TensorFloat-32 can be disabled by running tf.config.experimental.enable_tensor_float_32_execution(False).

The byte layout for string tensors across the C-API has been updated to match TF Core/C++; i.e., a contiguous array of tensorflow::tstring/TF_TStrings.

C-API functions TF_StringDecode, TF_StringEncode, and TF_StringEncodedSize are no longer relevant and have been removed; see core/platform/ctstring.h for string access/modification in C.

tensorflow.python, tensorflow.core and tensorflow.compiler modules are now hidden. These modules are not part of TensorFlow public API.

tf.raw_ops.Max and tf.raw_ops.Min no longer accept inputs of type tf.complex64 or tf.complex128, because the behavior of these ops is not well defined for complex types.

XLA:CPU and XLA:GPU devices are no longer registered by default. Use TF_XLA_FLAGS=--tf_xla_enable_xla_devices if you really need them, but this flag will eventually be removed in subsequent releases.

tf.keras:

The steps_per_execution argument in model.compile() is no longer experimental; if you were passing experimental_steps_per_execution, rename it to steps_per_execution in your code. This argument controls the number of batches to run during each tf.function call when calling model.fit(). Running multiple batches inside a single tf.function call can greatly improve performance on TPUs or small models with a large Python overhead.

A major refactoring of the internals of the Keras Functional API may affect code that is relying on certain internal details:

Code that uses isinstance(x, tf.Tensor) instead of tf.is_tensor when checking Keras symbolic inputs/outputs should switch to using tf.is_tensor.

Code that is overly dependent on the exact names attached to symbolic tensors (e.g. assumes there will be ":0" at the end of the inputs, treats names as unique identifiers instead of using tensor.ref(), etc.) may break.

Code that uses full path for get_concrete_function to trace Keras symbolic inputs directly should switch to building matching tf.TensorSpecs directly and tracing the TensorSpec objects.

Code that relies on the exact number and names of the op layers that TensorFlow operations were converted into may have changed.

Code that uses tf.map_fn/tf.cond/tf.while_loop/control flow as op layers and happens to work before TF 2.4. These will explicitly be unsupported now. Converting these ops to Functional API op layers was unreliable before TF 2.4, and prone to erroring incomprehensibly or being silently buggy.

Code that directly asserts on a Keras symbolic value in cases where ops like tf.rank used to return a static or symbolic value depending on if the input had a fully static shape or not. Now these ops always return symbolic values.

Code already susceptible to leaking tensors outside of graphs becomes slightly more likely to do so now.

Code that tries directly getting gradients with respect to symbolic Keras inputs/outputs. Use GradientTape on the actual Tensors passed to the already-constructed model instead.

Code that requires very tricky shape manipulation via converted op layers in order to work, where the Keras symbolic shape inference proves insufficient.

Code that tries manually walking a tf.keras.Model layer by layer and assumes layers only ever have one positional argument. This assumption doesn't hold true before TF 2.4 either, but is more likely to cause issues now.

... (truncated)

Changelog

Sourced from tensorflow-gpu's changelog.

Release 2.4.0

Major Features and Improvements

tf.distribute introduces experimental support for asynchronous training of models via the [tf.distribute.experimental.ParameterServerStrategy] (https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/ParameterServerStrategy) API. Please see the tutorial to learn more.

MultiWorkerMirroredStrategy is now a stable API and is no longer considered experimental. Some of the major improvements involve handling peer failure and many bug fixes. Please check out the detailed tutorial on [Multi-worker training with Keras] (https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras).

Introduces experimental support for a new module named [tf.experimental.numpy] (https://www.tensorflow.org/api_docs/python/tf/experimental/numpy) which is a NumPy-compatible API for writing TF programs. See the [detailed guide] (https://www.tensorflow.org/guide/tf_numpy) to learn more. Additional details below.

Adds Support for TensorFloat-32 on Ampere based GPUs. TensorFloat-32, or TF32 for short, is a math mode for NVIDIA Ampere based GPUs and is enabled by default.

A major refactoring of the internals of the Keras Functional API has been completed, that should improve the reliability, stability, and performance of constructing Functional models.

Keras mixed precision API [tf.keras.mixed_precision] (https://www.tensorflow.org/api_docs/python/tf/keras/mixed_precision?version=nightly) is no longer experimental and allows the use of 16-bit floating point formats during training, improving performance by up to 3x on GPUs and 60% on TPUs. Please see below for additional details.

TensorFlow Profiler now supports profiling MultiWorkerMirroredStrategy and tracing multiple workers using the [sampling mode API] (https://www.tensorflow.org/guide/profiler#profiling_apis).

TFLite Profiler for Android is available. See the detailed [guide] (https://www.tensorflow.org/lite/performance/measurement#trace_tensorflow_lite_internals_in_android) to learn more.

TensorFlow pip packages are now built with CUDA11 and cuDNN 8.0.2.

Breaking Changes

TF Core:

Certain float32 ops run in lower precision on Ampere based GPUs, including

... (truncated)

Commits

582c8d2 Merge pull request #44220 from tensorflow-jenkins/relnotes-2.4.0rc0-18048

c16387f Update RELEASE.md

4cf406c Update RELEASE.md

3f35ef2 Update RELEASE.md

3647e8e Update RELEASE.md

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opened by dependabot[bot] 1
Bump tensorflow-gpu from 2.5.3 to 2.9.3
Bumps tensorflow-gpu from 2.5.3 to 2.9.3.

Release notes

Sourced from tensorflow-gpu's releases.

TensorFlow 2.9.3

Release 2.9.3

This release introduces several vulnerability fixes:

Fixes an overflow in tf.keras.losses.poisson (CVE-2022-41887)

Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)

Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)

Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)

Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)

Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)

Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)

Fixes a CHECK fail in BCast (CVE-2022-41890)

Fixes a segfault in TensorListConcat (CVE-2022-41891)

Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)

Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)

Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)

Fixes a crash in Mfcc (CVE-2022-41896)

Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)

Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)

Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)

Fixes a heap OOB in FractionalAvgPool and FractionalMaxPool(CVE-2022-41900)

Fixes a CHECK_EQ in SparseMatrixNNZ (CVE-2022-41901)

Fixes an OOB write in grappler (CVE-2022-41902)

Fixes a overflow in ResizeNearestNeighborGrad (CVE-2022-41907)

Fixes a CHECK fail in PyFunc (CVE-2022-41908)

Fixes a segfault in CompositeTensorVariantToComponents (CVE-2022-41909)

Fixes a invalid char to bool conversion in printing a tensor (CVE-2022-41911)

Fixes a heap overflow in QuantizeAndDequantizeV2 (CVE-2022-41910)

Fixes a CHECK failure in SobolSample via missing validation (CVE-2022-35935)

Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode (CVE-2022-35935)

TensorFlow 2.9.2

Release 2.9.2

This releases introduces several vulnerability fixes:

Fixes a CHECK failure in tf.reshape caused by overflows (CVE-2022-35934)

Fixes a CHECK failure in SobolSample caused by missing validation (CVE-2022-35935)

Fixes an OOB read in Gather_nd op in TF Lite (CVE-2022-35937)

Fixes a CHECK failure in TensorListReserve caused by missing validation (CVE-2022-35960)

Fixes an OOB write in Scatter_nd op in TF Lite (CVE-2022-35939)

Fixes an integer overflow in RaggedRangeOp (CVE-2022-35940)

Fixes a CHECK failure in AvgPoolOp (CVE-2022-35941)

Fixes a CHECK failures in UnbatchGradOp (CVE-2022-35952)

Fixes a segfault TFLite converter on per-channel quantized transposed convolutions (CVE-2022-36027)

Fixes a CHECK failures in AvgPool3DGrad (CVE-2022-35959)

Fixes a CHECK failures in FractionalAvgPoolGrad (CVE-2022-35963)

Fixes a segfault in BlockLSTMGradV2 (CVE-2022-35964)

Fixes a segfault in LowerBound and UpperBound (CVE-2022-35965)

... (truncated)

Changelog

Sourced from tensorflow-gpu's changelog.

Release 2.9.3

This release introduces several vulnerability fixes:

Fixes an overflow in tf.keras.losses.poisson (CVE-2022-41887)

Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)

Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)

Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)

Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)

Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)

Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)

Fixes a CHECK fail in BCast (CVE-2022-41890)

Fixes a segfault in TensorListConcat (CVE-2022-41891)

Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)

Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)

Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)

Fixes a crash in Mfcc (CVE-2022-41896)

Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)

Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)

Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)

Fixes a heap OOB in FractionalAvgPool and FractionalMaxPool(CVE-2022-41900)

Fixes a CHECK_EQ in SparseMatrixNNZ (CVE-2022-41901)

Fixes an OOB write in grappler (CVE-2022-41902)

Fixes a overflow in ResizeNearestNeighborGrad (CVE-2022-41907)

Fixes a CHECK fail in PyFunc (CVE-2022-41908)

Fixes a segfault in CompositeTensorVariantToComponents (CVE-2022-41909)

Fixes a invalid char to bool conversion in printing a tensor (CVE-2022-41911)

Fixes a heap overflow in QuantizeAndDequantizeV2 (CVE-2022-41910)

Fixes a CHECK failure in SobolSample via missing validation (CVE-2022-35935)

Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode (CVE-2022-35935)

Release 2.8.4

This release introduces several vulnerability fixes:

Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)

Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)

Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)

Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)

Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)

Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)

Fixes a CHECK fail in BCast (CVE-2022-41890)

Fixes a segfault in TensorListConcat (CVE-2022-41891)

Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)

Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)

Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)

Fixes a crash in Mfcc (CVE-2022-41896)

Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)

Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)

Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)

... (truncated)

Commits

a5ed5f3 Merge pull request #58584 from tensorflow/vinila21-patch-2

258f9a1 Update py_func.cc

cd27cfb Merge pull request #58580 from tensorflow-jenkins/version-numbers-2.9.3-24474

3e75385 Update version numbers to 2.9.3

bc72c39 Merge pull request #58482 from tensorflow-jenkins/relnotes-2.9.3-25695

3506c90 Update RELEASE.md

8dcb48e Update RELEASE.md

4f34ec8 Merge pull request #58576 from pak-laura/c2.99f03a9d3bafe902c1e6beb105b2f2417...

6fc67e4 Replace CHECK with returning an InternalError on failing to create python tuple

5dbe90a Merge pull request #58570 from tensorflow/r2.9-7b174a0f2e4

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dependencies
opened by dependabot[bot] 0