Bayesian Deep Learning and Deep Reinforcement Learning for Object Shape Error Response and Correction of Manufacturing Systems

Overview
WMG

Bayesian Deep Learning for Manufacturing 2.0 (dlmfg)

Object Shape Error Response (OSER)

Digital Lifecycle Management - In Process Quality Improvement (IPQI)

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Overview

The open source Bayesian Deep Learning for Manufacturing (dlmfg) Library is built using a TensorFlow, TensorFlow Probablity and Keras back end to build:

  • Bayesian deep learning models such as Bayesian 3D Convolutional Neural Network and Bayesian 3D U-net to enable root cause analysis in Manufacturing Systems.

  • Deep reinforcement learning models such as Deep Deterministic Policy Gradients to enable control and correction in Manufacturing Systems.

The library can be used across various domains such as assembly systems, stamping, additive manufacturing and milling where the key problem is Object Shape Error Detection and Estimation. The library is build using Object Oriented Programming to enable extension and contribution from other related disciplines within the artificial intelligence community as well as the manufacturing community.

Video

A Video for the work can be found here

Dataset

Computer Aided Engineering Dataset for model training and testing using Variation Response Method (VRM) can be found here

Published Work

The published works can be found below:

Two follow up papers are currently under review are expected by April 2021

Documentation

The complete documentation and other ongoing research can be found here: Documentation and Research.

Highlights and New Additions

  1. Bayesian 3D U-Net model integrating Bayesian layers and attention blocks for uncertainty quantification and superior decoder performance leveraging the where to look capability with multi-task capabilities to estimate bot real-valued(regression) and categorical(classification) based values. The Decoder is used to obtain real-valued segmentation maps
  2. Deep Reinforcement Learning using deep deterministic policy gradient (DDPG) and a custom made multi physics manufacturing environment to build agents to correct manufacturing systems
  3. Closed Loop Sampling for faster model training and convergence using epistemic uncertainty of the Bayesian CNN models
  4. Matlab Python Integration to enable low latency connection between multi-physics manufacturing environments (Matlab) and TensorFlow based DDPG agents
  5. Multi-Physics Manufacturing System Simulations to generate custom datasets for various fault scenarios using Variation Response Method (VRM) kernel
  6. Uncertainty guided continual learning to enable life long/incremental training for multiple case studies
  7. Automated Model Selection using Keras Tuner that enables hyperparameter optimization and benchmarking for various deep learning architectures
  8. Exploratory notebooks for various case studies
  9. 3D Gradient-weighted Class Activation Maps for interpretability of deep learning models
  10. Datasets for Industrial multi-station case studies for training and benchmarking deep learning models

Installation

The library can be cloned using:

Git clone https://github.com/sumitsinha/Deep_Learning_for_Manufacturing

Dataset Download

The datasets can be download by running the download_data.py file within the downloads file. The specifics of the download can be specified in the download\config.py file.

The library consists of the following two key datasets:

  1. 3D Cloud of Point data with node deviations and process parameters for Single Part Car Halo Reinforcement – Obtained due to variations in the Measurement Station locators and Stamping Process
  2. 3D Cloud of Point data with node deviations and process parameters for Two part assembly for Car Door Inner and Hinge Reinforcement – Obtained due to variations in the Assembly System locators and joining tools.

Bayesian 3D CNN Model Architecture

Motivated by the recent development of Bayesian Deep Neural Networks Bayesian models considering parameters to be distributions have been build using TensorFlow Probability. The Aleatoric uncertainty have been modelled using Multi-variate normal distributions as outputs while the epistemic distributions have been modelled using distributions on model parameters by using Flip-out layers.

The Bayesian 3D CNN model for single station system has the following layers: Bayesian 3D CNN Model

> negloglik = lambda y, rv_y: -rv_y.log_prob(y)
> model = tf.keras.Sequential([
tf.keras.layers.InputLayer(input_shape=(voxel_dim,voxel_dim,voxel_dim,deviation_channels)), tfp.layers.Convolution3DFlipout(32, kernel_size=(5,5,5),strides=(2,2,2),activation=tf.nn.relu),
tfp.layers.Convolution3DFlipout(32, kernel_size=(4,4,4),strides=(2,2,2),activation=tf.nn.relu),
tfp.layers.Convolution3DFlipout(32, kernel_size=(3,3,3),strides=(1,1,1),activation=tf.nn.relu),
tf.keras.layers.MaxPooling3D(pool_size=[2, 2, 2]),
tf.keras.layers.Flatten(),
tfp.layers.DenseFlipout(128,activation=tf.nn.relu),
tfp.layers.DenseFlipout(64,activation=tf.nn.relu),
tfp.layers.DenseFlipout(self.output_dimension),
tfp.layers.DistributionLambda(lambda t: tfd.MultivariateNormalDiag(loc=t[..., :self.output_dimension], scale_diag=aleatoric_tensor)),])
model.compile(optimizer=tf.keras.optimizers.Adam(),loss=negloglik,metrics=[tf.keras.metrics.MeanAbsoluteError()])

Bayesian 3D U-Net Model Architecture

For scaling to multi-station systems consisting of both categorical and continuous process parameters and prediction of point-clouds (object shape error) in previous stations a 3D - Net Attention based architecture is leveraged.

Bayesian 3D CNN Model

Down-sampling Kernel Bayesian 3D CNN Model

Attention based Up-Sampling Kernel Bayesian 3D CNN Model

		def attention_block(x, g, inter_channel):

		    theta_x = Conv(inter_channel, [1,1,1], strides=[1,1,1])(x)
		    phi_g = Conv(inter_channel, [1,1,1], strides=[1,1,1])(g)
		    
		    f = Activation('relu')(add([theta_x, phi_g]))
		    psi_f = Conv(1, [1,1,1], strides=[1,1,1])(f)

		    rate = Activation('sigmoid')(psi_f)

		    att_x = multiply([x, rate])
		    return att_x

		input_size=(voxel_dim,voxel_dim,voxel_dim,deviation_channels)
		inputs = Input(input_size)
		x = inputs

		# Down sampling
		for i in range(depth):
			out_channel = 2**i * filter_root

			# Residual/Skip connection
			res = tfp.layers.Convolution3DFlipout(out_channel, kernel_size=1, kernel_divergence_fn=kl_divergence_function,padding='same', name="Identity{}_1".format(i))(x)

			# First Conv Block with Conv, BN and activation
			conv1 = tfp.layers.Convolution3DFlipout(out_channel, kernel_size=3, kernel_divergence_fn=kl_divergence_function,padding='same', name="Conv{}_1".format(i))(x)
			#if batch_norm:
				#conv1 = BatchNormalization(name="BN{}_1".format(i))(conv1)
			act1 = Activation(activation, name="Act{}_1".format(i))(conv1)

			# Second Conv block with Conv and BN only
			conv2 = tfp.layers.Convolution3DFlipout(out_channel, kernel_size=3, padding='same', kernel_divergence_fn=kl_divergence_function,name="Conv{}_2".format(i))(act1)
			#if batch_norm:
				#conv2 = BatchNormalization(name="BN{}_2".format(i))(conv2)

			resconnection = Add(name="Add{}_1".format(i))([res, conv2])

			act2 = Activation(activation, name="Act{}_2".format(i))(resconnection)

			# Max pooling
			if i < depth - 1:
				long_connection_store[str(i)] = act2
				x = MaxPooling(padding='same', name="MaxPooling{}_1".format(i))(act2)
			else:
				x = act2

		feature_vector_reg=Conv(reg_kccs, 1, padding='same', activation=final_activation, name='Process_Parameter_Reg_output')(x)
		process_parameter_reg=GlobalAveragePooling3D()(feature_vector_reg)
		
		feature_vector_cla=Conv(categorical_kccs, 1, padding='same', activation=final_activation, name='Process_Parameter_Cla_output')(x)
		process_parameter_cla=GlobalAveragePooling3D()(feature_vector_cla)
		
		#feature_categorical=Flatten()(feature_vector)
		#reg_output=tfp.layers.DenseFlipout(output_dimension,kernel_divergence_fn=kl_divergence_function)(process_parameter)
		
		#Process Parameter Outputs
		reg_distrbution=tfp.layers.DistributionLambda(lambda t:tfd.MultivariateNormalDiag(loc=t[..., :reg_kccs], scale_diag=aleatoric_tensor),name="regression_outputs")(process_parameter_reg)
		cla_distrbution=Activation('sigmoid', name="classification_outputs")(process_parameter_cla)
		#cla_distrbution=tfp.layers.DenseFlipout(categorical_kccs, kernel_divergence_fn=kl_divergence_function,activation=tf.nn.sigmoid,name="classification_outputs")(process_parameter_cla)

		# Upsampling
		for i in range(depth - 2, -1, -1):
			out_channel = 2**(i) * filter_root

			# long connection from down sampling path.
			long_connection = long_connection_store[str(i)]

			up1 = UpSampling(name="UpSampling{}_1".format(i))(x)
			up_conv1 = Conv(out_channel, 2, activation='relu', padding='same', name="upConvSam{}_1".format(i))(up1)

			attention_layer = attention_block(x=long_connection, g=up_conv1, inter_channel=out_channel // 4)
			#  Concatenate.
			
			#up_conc = Concatenate(axis=-1, name="upConcatenate{}_1".format(i))([up_conv1, long_connection])
			up_conc = Concatenate(axis=-1, name="upConcatenate{}_1".format(i))([up_conv1, attention_layer])

			#  Convolutions
			up_conv2 = Conv(out_channel, 3, padding='same', name="upConv{}_1".format(i))(up_conc)

			up_act1 = Activation(activation, name="upAct{}_1".format(i))(up_conv2)

			up_conv2 = Conv(out_channel, 3, padding='same', name="upConv{}_2".format(i))(up_act1)

			# Residual/Skip connection
			res = Conv(out_channel, kernel_size=1, padding='same', use_bias=False, name="upIdentity{}_1".format(i))(up_conc)

			resconnection = Add(name="upAdd{}_1".format(i))([res, up_conv2])

			x = Activation(activation, name="upAct{}_2".format(i))(resconnection)

Verification and Validation

Details of verification and validation of the model on an actual system can be found here: Real System Implementation

Benchmarking

Benchmarking of the model is done against various deep learning and machine learning approaches to highlight superiority.

OSER Benchmarking

Decoder Outputs

The segmentations outputs for the Bayesian 3D U-Net model enables estimation of dimensional quality of products in between stages and stations of the process.

Real-Valued Segmentation Maps

Model Interpretability

3D Grad-weighted Class Activation Maps (3D Grad-CAMS) for each level of the encoder provides insights into the working of the model and integrate a measure of trust in all estimates.

Encoder 3D Grad-CAMs

Please cite work as:
Sinha, S., Glorieux, E., Franciosa, P., & Ceglarek, D. (2019, June). 3D convolutional neural networks to estimate assembly process parameters using 3D point-clouds. In Multimodal Sensing: Technologies and Applications (Vol. 11059, p. 110590B). International Society for Optics and Photonics.

@inproceedings{Sinha2019, author = {Sinha, Sumit and Glorieux, Emile and Franciosa, Pasquale and Ceglarek, Dariusz}, booktitle = {Multimodal Sensing: Technologies and Applications}, doi = {10.1117/12.2526062}, month = {jun}, pages = {10}, publisher = {SPIE}, title = {{3D convolutional neural networks to estimate assembly process parameters using 3D point-clouds}}, year = {2019} }

WMG
Data generation has been done using a Multi-fidelity CAE simulation software known as VRM :
Franciosa, P., Palit, A., Gerbino, S., & Ceglarek, D. (2019). A novel hybrid shell element formulation (QUAD+ and TRIA+): A benchmarking and comparative study. Finite Elements in Analysis and Design, 166, 103319.
Collaboration:

Please contact Sumit Sinha, Dr Pasquale Franciosa or Prof Darek Ceglarek in case of any clarifications or collaborative work with the Digital Lifecycle Management group at WMG, University of Warwick

Comments
  • Bump numpy from 1.17.4 to 1.21.0

    Bump numpy from 1.17.4 to 1.21.0

    Bumps numpy from 1.17.4 to 1.21.0.

    Release notes

    Sourced from numpy's releases.

    v1.21.0

    NumPy 1.21.0 Release Notes

    The NumPy 1.21.0 release highlights are

    • continued SIMD work covering more functions and platforms,
    • initial work on the new dtype infrastructure and casting,
    • universal2 wheels for Python 3.8 and Python 3.9 on Mac,
    • improved documentation,
    • improved annotations,
    • new PCG64DXSM bitgenerator for random numbers.

    In addition there are the usual large number of bug fixes and other improvements.

    The Python versions supported for this release are 3.7-3.9. Official support for Python 3.10 will be added when it is released.

    :warning: Warning: there are unresolved problems compiling NumPy 1.21.0 with gcc-11.1 .

    • Optimization level -O3 results in many wrong warnings when running the tests.
    • On some hardware NumPy will hang in an infinite loop.

    New functions

    Add PCG64DXSM BitGenerator

    Uses of the PCG64 BitGenerator in a massively-parallel context have been shown to have statistical weaknesses that were not apparent at the first release in numpy 1.17. Most users will never observe this weakness and are safe to continue to use PCG64. We have introduced a new PCG64DXSM BitGenerator that will eventually become the new default BitGenerator implementation used by default_rng in future releases. PCG64DXSM solves the statistical weakness while preserving the performance and the features of PCG64.

    See upgrading-pcg64 for more details.

    (gh-18906)

    Expired deprecations

    • The shape argument numpy.unravel_index cannot be passed as dims keyword argument anymore. (Was deprecated in NumPy 1.16.)

    ... (truncated)

    Commits
    • b235f9e Merge pull request #19283 from charris/prepare-1.21.0-release
    • 34aebc2 MAINT: Update 1.21.0-notes.rst
    • 493b64b MAINT: Update 1.21.0-changelog.rst
    • 07d7e72 MAINT: Remove accidentally created directory.
    • 032fca5 Merge pull request #19280 from charris/backport-19277
    • 7d25b81 BUG: Fix refcount leak in ResultType
    • fa5754e BUG: Add missing DECREF in new path
    • 61127bb Merge pull request #19268 from charris/backport-19264
    • 143d45f Merge pull request #19269 from charris/backport-19228
    • d80e473 BUG: Removed typing for == and != in dtypes
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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 2.5.1 to 2.6.4

    Bump tensorflow from 2.5.1 to 2.6.4

    Bumps tensorflow from 2.5.1 to 2.6.4.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.6.4

    Release 2.6.4

    This releases introduces several vulnerability fixes:

    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's changelog.

    Release 2.6.4

    This releases introduces several vulnerability fixes:

    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
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    dependencies 
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  • Bump tensorflow from 2.5.1 to 2.5.3

    Bump tensorflow from 2.5.1 to 2.5.3

    Bumps tensorflow from 2.5.1 to 2.5.3.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.5.3

    Release 2.5.3

    Note: This is the last release in the 2.5 series.

    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)
    • Fixes a division by zero in FractionalMaxPool (CVE-2022-21735)
    • Fixes a number of CHECK-fails when building invalid/overflowing tensor shapes (CVE-2022-23569)
    • Fixes an undefined behavior in SparseTensorSliceDataset (CVE-2022-21736)
    • Fixes an assertion failure based denial of service via faulty bin count operations (CVE-2022-21737)
    • Fixes a reference binding to null pointer in QuantizedMaxPool (CVE-2022-21739)
    • Fixes an integer overflow leading to crash in SparseCountSparseOutput (CVE-2022-21738)
    • Fixes a heap overflow in SparseCountSparseOutput (CVE-2022-21740)
    • Fixes an FPE in BiasAndClamp in TFLite (CVE-2022-23557)
    • Fixes an FPE in depthwise convolutions in TFLite (CVE-2022-21741)
    • Fixes an integer overflow in TFLite array creation (CVE-2022-23558)
    • Fixes an integer overflow in TFLite (CVE-2022-23559)
    • Fixes a dangerous OOB write in TFLite (CVE-2022-23561)
    • Fixes a vulnerability leading to read and write outside of bounds in TFLite (CVE-2022-23560)
    • Fixes a set of vulnerabilities caused by using insecure temporary files (CVE-2022-23563)
    • Fixes an integer overflow in Range resulting in undefined behavior and OOM (CVE-2022-23562)
    • Fixes a vulnerability where missing validation causes tf.sparse.split to crash when axis is a tuple (CVE-2021-41206)
    • Fixes a CHECK-fail when decoding resource handles from proto (CVE-2022-23564)
    • Fixes a CHECK-fail with repeated AttrDef (CVE-2022-23565)
    • Fixes a heap OOB write in Grappler (CVE-2022-23566)
    • Fixes a CHECK-fail when decoding invalid tensors from proto (CVE-2022-23571)
    • Fixes an unitialized variable access in AssignOp (CVE-2022-23573)
    • Fixes an integer overflow in OpLevelCostEstimator::CalculateTensorSize (CVE-2022-23575)
    • Fixes an integer overflow in OpLevelCostEstimator::CalculateOutputSize (CVE-2022-23576)
    • Fixes a null dereference in GetInitOp (CVE-2022-23577)
    • Fixes a memory leak when a graph node is invalid (CVE-2022-23578)
    • Fixes an abort caused by allocating a vector that is too large (CVE-2022-23580)
    • Fixes multiple CHECK-failures during Grappler's IsSimplifiableReshape (CVE-2022-23581)
    • Fixes multiple CHECK-failures during Grappler's SafeToRemoveIdentity (CVE-2022-23579)
    • Fixes multiple CHECK-failures in TensorByteSize (CVE-2022-23582)
    • Fixes multiple CHECK-failures in binary ops due to type confusion (CVE-2022-23583)

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.5.3

    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)
    • Fixes a division by zero in FractionalMaxPool (CVE-2022-21735)
    • Fixes a number of CHECK-fails when building invalid/overflowing tensor shapes (CVE-2022-23569)
    • Fixes an undefined behavior in SparseTensorSliceDataset (CVE-2022-21736)
    • Fixes an assertion failure based denial of service via faulty bin count operations (CVE-2022-21737)
    • Fixes a reference binding to null pointer in QuantizedMaxPool (CVE-2022-21739)
    • Fixes an integer overflow leading to crash in SparseCountSparseOutput (CVE-2022-21738)
    • Fixes a heap overflow in SparseCountSparseOutput (CVE-2022-21740)
    • Fixes an FPE in BiasAndClamp in TFLite (CVE-2022-23557)
    • Fixes an FPE in depthwise convolutions in TFLite (CVE-2022-21741)

    ... (truncated)

    Commits
    • 959e9b2 Merge pull request #54213 from tensorflow/fix-sanity-on-r2.5
    • d05fcbc Fix sanity build
    • f2526a0 Merge pull request #54205 from tensorflow/disable-flaky-tests-on-r2.5
    • a5f94df Disable flaky test
    • 7babe52 Merge pull request #54201 from tensorflow/cherrypick-510ae18200d0a4fad797c0bf...
    • 0e5d378 Set Env Variable to override Setuptools new behavior
    • fdd4195 Merge pull request #54176 from tensorflow-jenkins/relnotes-2.5.3-6805
    • 4083165 Update RELEASE.md
    • a2bb7f1 Merge pull request #54185 from tensorflow/cherrypick-d437dec4d549fc30f9b85c75...
    • 5777ea3 Update third_party/icu/workspace.bzl
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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 2.5.1 to 2.5.2

    Bump tensorflow from 2.5.1 to 2.5.2

    Bumps tensorflow from 2.5.1 to 2.5.2.

    Release notes

    Sourced from tensorflow'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.
    Changelog

    Sourced from tensorflow's changelog.

    Release 2.5.2

    This release introduces several vulnerability fixes:

    ... (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
  • installvrm.m

    installvrm.m

    Hi sumitsinha,

    I found there might be something wrong with the file installvrm.m. The file "(3) KINEMATICS (src)" is missing in the folder caesimulations. Maybe I understand it in the wrong way. But could you please have a look at the file to ensure the file is working?

    Thank you in advance!

    best

    opened by YifanQie 1
  • Bump tensorflow from 2.5.1 to 2.7.2

    Bump tensorflow from 2.5.1 to 2.7.2

    Bumps tensorflow from 2.5.1 to 2.7.2.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.7.2

    Release 2.7.2

    This releases introduces several vulnerability fixes:

    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's changelog.

    Release 2.7.2

    This releases introduces several vulnerability fixes:

    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
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 0
  • Bump tensorflow from 2.5.0 to 2.5.1

    Bump tensorflow from 2.5.0 to 2.5.1

    Bumps tensorflow from 2.5.0 to 2.5.1.

    Release notes

    Sourced from tensorflow'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'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
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 0
  • Bump tensorflow from 2.4.0 to 2.5.0

    Bump tensorflow from 2.4.0 to 2.5.0

    Bumps tensorflow from 2.4.0 to 2.5.0.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.5.0

    Release 2.5.0

    Major Features and Improvements

    • Support for Python3.9 has been added.
    • tf.data:
      • tf.data service now supports strict round-robin reads, which is useful for synchronous training workloads where example sizes vary. With strict round robin reads, users can guarantee that consumers get similar-sized examples in the same step.
      • tf.data service now supports optional compression. Previously data would always be compressed, but now you can disable compression by passing compression=None to tf.data.experimental.service.distribute(...).
      • tf.data.Dataset.batch() now supports num_parallel_calls and deterministic arguments. num_parallel_calls is used to indicate that multiple input batches should be computed in parallel. With num_parallel_calls set, deterministic is used to indicate that outputs can be obtained in the non-deterministic order.
      • Options returned by tf.data.Dataset.options() are no longer mutable.
      • tf.data input pipelines can now be executed in debug mode, which disables any asynchrony, parallelism, or non-determinism and forces Python execution (as opposed to trace-compiled graph execution) of user-defined functions passed into transformations such as map. The debug mode can be enabled through tf.data.experimental.enable_debug_mode().
    • tf.lite
      • Enabled the new MLIR-based quantization backend by default
        • The new backend is used for 8 bits full integer post-training quantization
        • The new backend removes the redundant rescales and fixes some bugs (shared weight/bias, extremely small scales, etc)
        • Set experimental_new_quantizer in tf.lite.TFLiteConverter to False to disable this change
    • tf.keras
      • tf.keras.metrics.AUC now support logit predictions.
      • Enabled a new supported input type in Model.fit, tf.keras.utils.experimental.DatasetCreator, which takes a callable, dataset_fn. DatasetCreator is intended to work across all tf.distribute strategies, and is the only input type supported for Parameter Server strategy.
    • tf.distribute
      • tf.distribute.experimental.ParameterServerStrategy now supports training with Keras Model.fit when used with DatasetCreator.
      • Creating tf.random.Generator under tf.distribute.Strategy scopes is now allowed (except for tf.distribute.experimental.CentralStorageStrategy and tf.distribute.experimental.ParameterServerStrategy). Different replicas will get different random-number streams.
    • TPU embedding support
      • Added profile_data_directory to EmbeddingConfigSpec in _tpu_estimator_embedding.py. This allows embedding lookup statistics gathered at runtime to be used in embedding layer partitioning decisions.
    • PluggableDevice
    • oneAPI Deep Neural Network Library (oneDNN) CPU performance optimizations from Intel-optimized TensorFlow are now available in the official x86-64 Linux and Windows builds.
      • They are off by default. Enable them by setting the environment variable TF_ENABLE_ONEDNN_OPTS=1.
      • We do not recommend using them in GPU systems, as they have not been sufficiently tested with GPUs yet.
    • TensorFlow pip packages are now built with CUDA11.2 and cuDNN 8.1.0

    Breaking Changes

    • The TF_CPP_MIN_VLOG_LEVEL environment variable has been renamed to to TF_CPP_MAX_VLOG_LEVEL which correctly describes its effect.

    Bug Fixes and Other Changes

    • tf.keras:
      • Preprocessing layers API consistency changes:
        • StringLookup added output_mode, sparse, and pad_to_max_tokens arguments with same semantics as TextVectorization.
        • IntegerLookup added output_mode, sparse, and pad_to_max_tokens arguments with same semantics as TextVectorization. Renamed max_values, oov_value and mask_value to max_tokens, oov_token and mask_token to align with StringLookup and TextVectorization.
        • TextVectorization default for pad_to_max_tokens switched to False.
        • CategoryEncoding no longer supports adapt, IntegerLookup now supports equivalent functionality. max_tokens argument renamed to num_tokens.
        • Discretization added num_bins argument for learning bins boundaries through calling adapt on a dataset. Renamed bins argument to bin_boundaries for specifying bins without adapt.
      • Improvements to model saving/loading:
        • model.load_weights now accepts paths to saved models.

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.5.0

    Breaking Changes

    • The TF_CPP_MIN_VLOG_LEVEL environment variable has been renamed to to TF_CPP_MAX_VLOG_LEVEL which correctly describes its effect.

    Known Caveats

    Major Features and Improvements

    • TPU embedding support

      • Added profile_data_directory to EmbeddingConfigSpec in _tpu_estimator_embedding.py. This allows embedding lookup statistics gathered at runtime to be used in embedding layer partitioning decisions.
    • tf.keras.metrics.AUC now support logit predictions.

    • Creating tf.random.Generator under tf.distribute.Strategy scopes is now allowed (except for tf.distribute.experimental.CentralStorageStrategy and tf.distribute.experimental.ParameterServerStrategy). Different replicas will get different random-number streams.

    • tf.data:

      • tf.data service now supports strict round-robin reads, which is useful for synchronous training workloads where example sizes vary. With strict round robin reads, users can guarantee that consumers get similar-sized examples in the same step.
      • tf.data service now supports optional compression. Previously data would always be compressed, but now you can disable compression by passing compression=None to tf.data.experimental.service.distribute(...).
      • tf.data.Dataset.batch() now supports num_parallel_calls and deterministic arguments. num_parallel_calls is used to indicate that multiple input batches should be computed in parallel. With num_parallel_calls set, deterministic is used to indicate that outputs can be obtained in the non-deterministic order.
      • Options returned by tf.data.Dataset.options() are no longer mutable.
      • tf.data input pipelines can now be executed in debug mode, which disables any asynchrony, parallelism, or non-determinism and forces Python execution (as opposed to trace-compiled graph execution) of user-defined functions passed into transformations such as map. The debug mode can be enabled through tf.data.experimental.enable_debug_mode().
    • tf.lite

      • Enabled the new MLIR-based quantization backend by default
        • The new backend is used for 8 bits full integer post-training quantization
        • The new backend removes the redundant rescales and fixes some bugs (shared weight/bias, extremely small scales, etc)

    ... (truncated)

    Commits
    • a4dfb8d Merge pull request #49124 from tensorflow/mm-cherrypick-tf-data-segfault-fix-...
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    • 16b8139 Update snapshot_dataset_op.cc
    • 86a0d86 Merge pull request #49126 from geetachavan1/cherrypicks_X9ZNY
    • 9436ae6 Merge pull request #49128 from geetachavan1/cherrypicks_D73J5
    • 6b2bf99 Validate that a and b are proper sparse tensors
    • c03ad1a Ensure validation sticks in banded_triangular_solve_op
    • 12a6ead Merge pull request #49120 from geetachavan1/cherrypicks_KJ5M9
    • b67f5b8 Merge pull request #49118 from geetachavan1/cherrypicks_BIDTR
    • a13c0ad [tf.data][cherrypick] Fix snapshot segfault when using repeat and prefecth
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    dependencies 
    opened by dependabot[bot] 0
  • Bump tensorflow from 2.1.0 to 2.1.2

    Bump tensorflow from 2.1.0 to 2.1.2

    ⚠️ Dependabot is rebasing this PR ⚠️

    If you make any changes to it yourself then they will take precedence over the rebase.


    Bumps tensorflow from 2.1.0 to 2.1.2.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.1.2

    Release 2.1.2

    Bug Fixes and Other Changes

    TensorFlow 2.1.1

    Release 2.1.1

    Bug Fixes and Other Changes

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.1.2

    Bug Fixes and Other Changes

    Release 2.0.3

    Bug Fixes and Other Changes

    • Fixes an undefined behavior causing a segfault in tf.raw_ops.Switch

    ... (truncated)

    Commits
    • ab35f2b Disable a bunch of tests on Windows GPU
    • f6efeba Merge pull request #43444 from tensorflow-jenkins/version-numbers-2.1.2-2858
    • d5ba28c Update version numbers to 2.1.2
    • 3360254 Merge pull request #43439 from tensorflow-jenkins/relnotes-2.1.2-27543
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    • 9178a24 Add missing import
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    dependencies 
    opened by dependabot[bot] 0
  • Bump tensorflow from 2.7.2 to 2.9.3

    Bump tensorflow from 2.7.2 to 2.9.3

    Bumps tensorflow from 2.7.2 to 2.9.3.

    Release notes

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    TensorFlow 2.9.3

    Release 2.9.3

    This release introduces several vulnerability fixes:

    TensorFlow 2.9.2

    Release 2.9.2

    This releases introduces several vulnerability fixes:

    ... (truncated)

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    Release 2.9.3

    This release introduces several vulnerability fixes:

    Release 2.8.4

    This release introduces several vulnerability fixes:

    ... (truncated)

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    • bc72c39 Merge pull request #58482 from tensorflow-jenkins/relnotes-2.9.3-25695
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    • 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
  • Bump numpy from 1.17.4 to 1.22.0

    Bump numpy from 1.17.4 to 1.22.0

    Bumps numpy from 1.17.4 to 1.22.0.

    Release notes

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    v1.22.0

    NumPy 1.22.0 Release Notes

    NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

    • Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.
    • A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.
    • NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.
    • New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.
    • A new configurable allocator for use by downstream projects.

    These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

    The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

    Expired deprecations

    Deprecated numeric style dtype strings have been removed

    Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

    (gh-19539)

    Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

    numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

    (gh-19615)

    ... (truncated)

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    dependencies 
    opened by dependabot[bot] 0
  • Unrecognized function or variable 'modelSamplePlacement'.

    Unrecognized function or variable 'modelSamplePlacement'.

    Hi, Thank you for sharing the code. Thank you for answering me about my last question. There is a problem when I want to run the code halo_debug.m: (line 142) Unrecognized function or variable 'modelSamplePlacement'. and in line126, the input parameter should be changed maybe? NO: model.database=modelBuildPart(model.database,'import'); it might be: model.database=modelBuildPart(model.database); What is the difference of the five logic options?

    Thank you in advance!

    Best regards, Yifan

    opened by YifanQie 0
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