Chainer implementation of recent GAN variants

Related tags

Deep Learning deep-learning generative-adversarial-network dcgan wgan-gp
Overview

Chainer-GAN-lib

This repository collects chainer implementation of state-of-the-art GAN algorithms.
These codes are evaluated with the inception score on Cifar-10 dataset.
Note that our codes are not faithful re-implementation of the original paper.

How to use

Install the requirements first:

pip install -r requirements.txt

This implementation has been tested with the following versions.

python 3.5.2
chainer 4.0.0
+ https://github.com/chainer/chainer/pull/3615
+ https://github.com/chainer/chainer/pull/3581
cupy 3.0.0
tensorflow 1.2.0 # only for downloading inception model
numpy 1.11.1

Download the inception score module forked from https://github.com/hvy/chainer-inception-score.

git submodule update -i

Download the inception model.

cd common/inception
python download.py --outfile inception_score.model

You can start training with train.py.

python train.py --gpu 0 --algorithm dcgan --out result_dcgan

Please see example.sh to train other algorithms.

Quantitative evaluation

Inception Inception (Official) FID
Real data 12.0 11.24 3.2 (train vs test)
Progressive 8.5 8.8 19.1
SN-DCGAN 7.5 7.41 23.6
WGAN-GP 6.8 7.86 (ResNet) 28.2
DFM 7.3 7.72 30.1
Cramer GAN 6.4 - 32.7
DRAGAN 7.1 6.90 31.5
DCGAN-vanilla 6.7 6.16 [WGAN2] 6.99 [DRAGAN] 34.3
minibatch discrimination 7.0 6.86 (-L+HA) 31.3
BEGAN 5.4 5.62 84.0

Inception scores are calculated by average of 10 evaluation with 5000 samples.

FIDs are calculated with 50000 train dataset and 10000 generated samples.

Generated images

  • Progressive

progressive

  • SN-DCGAN

sndcagn

  • WGAN-GP

wgangp

  • DFM

dfm

  • Cramer GAN

cramer

  • DRAGAN

dragan

  • DCGAN

dcgan

  • Minibatch discrimination

minibatch_dis

  • BEGAN

began

License

MIT License. Please see the LICENSE file for details.

Comments
  • chainer-gan-lib/common/evaluation.py

    chainer-gan-lib/common/evaluation.py", line 142: 'numpy.ndarray' object has no attribute 'get'

    Hello, I tried "python train.py --gpu -1 --algorithm dcgan --out result_dcgan " with chainerv5.0.0a1&iDeep, I saw following error: File "/home/mingxiao/Documents/chainer-gan-lib/common/evaluation.py", line 175, in evaluation mean, cov = get_mean_cov(model, np.asarray(xs).reshape((-1, 3, h, w))) File "/home/mingxiao/Documents/chainer-gan-lib/common/evaluation.py", line 142, in get_mean_cov mean = xp.mean(ys, axis=0).get() AttributeError: 'numpy.ndarray' object has no attribute 'get'

    Have you ever seen this before? Thanks in advance.

    opened by mingxiaoh 6
  • loss_gp calculation in DRAGAN updater

    loss_gp calculation in DRAGAN updater

    There appears to be an error in how the loss_gp term is computed. In the code snippet, x_perturb is used to calculate y_mid. However, y_mid is only used for the shape to create an array of ones, instead of the actual output from the discriminator. The values of x_perturb do not seem to be used to compute the loss_gp.

    https://github.com/pfnet-research/chainer-gan-lib/blob/18b7c7dae445d5bee82010c4176ab173c6b97732/dragan/updater.py#L40-L43

    opened by BradMcDanel 4
  • ValueError: volatile argument is not supported anymore. Use chainer.using_config

    ValueError: volatile argument is not supported anymore. Use chainer.using_config

    Traceback (most recent call last): File "/media/scs4450/52d95cdd-64a0-4c37-9480-dc43778ffc5f/next/gan/chainer-gan-lib-master/train.py", line 191, in main() File "/media/scs4450/52d95cdd-64a0-4c37-9480-dc43778ffc5f/next/gan/chainer-gan-lib-master/train.py", line 187, in main trainer.run() File "/usr/local/lib/python3.4/dist-packages/chainer/training/trainer.py", line 313, in run six.reraise(*sys.exc_info()) File "/usr/local/lib/python3.4/dist-packages/six.py", line 693, in reraise raise value File "/usr/local/lib/python3.4/dist-packages/chainer/training/trainer.py", line 302, in run entry.extension(self) File "/media/scs4450/52d95cdd-64a0-4c37-9480-dc43778ffc5f/next/gan/chainer-gan-lib-master/common/evaluation.py", line 104, in evaluation mean, std = inception_score(model, ims) File "/media/scs4450/52d95cdd-64a0-4c37-9480-dc43778ffc5f/next/gan/chainer-gan-lib-master/common/inception/inception_score.py", line 42, in inception_score ims_batch = Variable(ims_batch, volatile=False) File "/usr/local/lib/python3.4/dist-packages/chainer/variable.py", line 443, in init kwargs, volatile='volatile argument is not supported anymore. ' File "/usr/local/lib/python3.4/dist-packages/chainer/utils/argument.py", line 4, in check_unexpected_kwargs raise ValueError(message) ValueError: volatile argument is not supported anymore. Use chainer.using_config

    opened by zhengziqiang 3
  • RuntimeError: cannot twice-differentiate an old style Function

    RuntimeError: cannot twice-differentiate an old style Function "sqrt"

    Thanks for your interesting works. I tried chainer-gan-lib/progressive, but it does not work well,

    [Environments] chainer-4.0.0a1, cupy-3.0.0a1, python 3.5.2, numpy 1.11.1, tensorflow 1.2.0.

    The error message said, "RuntimeError: cannot twice-differentiate an old style Function "sqrt"". It seems some trouble happened on twice-differentiate of backward(). However, I confirmed that chainer-gan-lib/wgan_gp, which also uses twice-differentiate, works for me.

    All error messages are as follows:

    /home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/cupy/core/fusion.py:659: FutureWarning: cupy.core.fusion is experimental. The interface can change in the future.
  util.experimental('cupy.core.fusion')
Exception in main training loop: cannot twice-differentiate an old style Function "sqrt"
Traceback (most recent call last):
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/training/trainer.py", line 302, in run
    entry.extension(self)
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/contextlib.py", line 77, in __exit__
    self.gen.throw(type, value, traceback)
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/reporter.py", line 98, in scope
    yield
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/training/trainer.py", line 299, in run
    update()
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/training/updater.py", line 223, in update
    self.update_core()
  File "/project/nakamura-lab07/Work/seitaro-s/chainer-gan-lib/progressive/updater.py", line 85, in update_core
    loss_dis_total.backward()
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/variable.py", line 878, in backward
    self._backward_main(retain_grad)
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/contextlib.py", line 77, in __exit__
    self.gen.throw(type, value, traceback)
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/configuration.py", line 128, in using_config
    yield
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/variable.py", line 878, in backward
    self._backward_main(retain_grad)
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/variable.py", line 971, in _backward_main
    x._check_old_style_gradient()
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/variable.py", line 396, in _check_old_style_gradient
    self._old_style_grad_generator)
Will finalize trainer extensions and updater before reraising the exception.
Traceback (most recent call last):
  File "train.py", line 135, in <module>
    main()
  File "train.py", line 131, in main
    trainer.run()
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/training/trainer.py", line 313, in run
    six.reraise(*sys.exc_info())
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/six.py", line 693, in reraise
    raise value
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/training/trainer.py", line 302, in run
    entry.extension(self)
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/contextlib.py", line 77, in __exit__
    self.gen.throw(type, value, traceback)
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/reporter.py", line 98, in scope
    yield
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/training/trainer.py", line 299, in run
    update()
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/training/updater.py", line 223, in update
    self.update_core()
  File "/project/nakamura-lab07/Work/seitaro-s/chainer-gan-lib/progressive/updater.py", line 85, in update_core
    loss_dis_total.backward()
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/variable.py", line 878, in backward
    self._backward_main(retain_grad)
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/contextlib.py", line 77, in __exit__
    self.gen.throw(type, value, traceback)
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/configuration.py", line 128, in using_config
    yield
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/variable.py", line 878, in backward
    self._backward_main(retain_grad)
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/variable.py", line 971, in _backward_main
    x._check_old_style_gradient()
  File "/home/is/seitaro-s/.pyenv/versions/anaconda3-2.1.0/envs/chainerv4/lib/python3.5/site-packages/chainer/variable.py", line 396, in _check_old_style_gradient
    self._old_style_grad_generator)
RuntimeError: cannot twice-differentiate an old style Function "sqrt"
    opened by SeitaroShinagawa 3
  • Bump numpy from 1.11.1 to 1.21.0

    Bump numpy from 1.11.1 to 1.21.0

    Bumps numpy from 1.11.1 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
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 1.2.0 to 2.7.2

    Bump tensorflow from 1.2.0 to 2.7.2

    Bumps tensorflow from 1.2.0 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
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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 1.2.0 to 2.6.4

    Bump tensorflow from 1.2.0 to 2.6.4

    Bumps tensorflow from 1.2.0 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
    • a6526a3 Update version numbers to 2.6.4
    • cb1a481 Update RELEASE.md
    • 4da550f Insert release notes place-fill
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 1.2.0 to 2.5.3

    Bump tensorflow from 1.2.0 to 2.5.3

    Bumps tensorflow from 1.2.0 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
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 1.2.0 to 2.5.1

    Bump tensorflow from 1.2.0 to 2.5.1

    Bumps tensorflow from 1.2.0 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] 1
  • Bump tensorflow from 1.2.0 to 2.5.0

    Bump tensorflow from 1.2.0 to 2.5.0

    Bumps tensorflow from 1.2.0 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-...
    • 2107b1d Merge pull request #49116 from tensorflow-jenkins/version-numbers-2.5.0-17609
    • 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
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 1.2.0 to 2.3.1

    Bump tensorflow from 1.2.0 to 2.3.1

    Bumps tensorflow from 1.2.0 to 2.3.1.

    Release notes

    Sourced from tensorflow's releases.

    TensorFlow 2.3.1

    Release 2.3.1

    Bug Fixes and Other Changes

    TensorFlow 2.3.0

    Release 2.3.0

    Major Features and Improvements

    • tf.data adds two new mechanisms to solve input pipeline bottlenecks and save resources:

    In addition checkout the detailed guide for analyzing input pipeline performance with TF Profiler.

    • tf.distribute.TPUStrategy is now a stable API and no longer considered experimental for TensorFlow. (earlier tf.distribute.experimental.TPUStrategy).

    • TF Profiler introduces two new tools: a memory profiler to visualize your model’s memory usage over time and a python tracer which allows you to trace python function calls in your model. Usability improvements include better diagnostic messages and profile options to customize the host and device trace verbosity level.

    • Introduces experimental support for Keras Preprocessing Layers API (tf.keras.layers.experimental.preprocessing.*) to handle data preprocessing operations, with support for composite tensor inputs. Please see below for additional details on these layers.

    • TFLite now properly supports dynamic shapes during conversion and inference. We’ve also added opt-in support on Android and iOS for XNNPACK, a highly optimized set of CPU kernels, as well as opt-in support for executing quantized models on the GPU.

    • Libtensorflow packages are available in GCS starting this release. We have also started to release a nightly version of these packages.

    • The experimental Python API tf.debugging.experimental.enable_dump_debug_info() now allows you to instrument a TensorFlow program and dump debugging information to a directory on the file system. The directory can be read and visualized by a new interactive dashboard in TensorBoard 2.3 called Debugger V2, which reveals the details of the TensorFlow program including graph structures, history of op executions at the Python (eager) and intra-graph levels, the runtime dtype, shape, and numerical composistion of tensors, as well as their code locations.

    Breaking Changes

    • Increases the minimum bazel version required to build TF to 3.1.0.
    • tf.data
      • Makes the following (breaking) changes to the tf.data.
      • C++ API: - IteratorBase::RestoreInternal, IteratorBase::SaveInternal, and DatasetBase::CheckExternalState become pure-virtual and subclasses are now expected to provide an implementation.
      • The deprecated DatasetBase::IsStateful method is removed in favor of DatasetBase::CheckExternalState.
      • Deprecated overrides of DatasetBase::MakeIterator and MakeIteratorFromInputElement are removed.

    ... (truncated)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.3.1

    Bug Fixes and Other Changes

    Release 2.2.1

    ... (truncated)

    Commits
    • fcc4b96 Merge pull request #43446 from tensorflow-jenkins/version-numbers-2.3.1-16251
    • 4cf2230 Update version numbers to 2.3.1
    • eee8224 Merge pull request #43441 from tensorflow-jenkins/relnotes-2.3.1-24672
    • 0d41b1d Update RELEASE.md
    • d99bd63 Insert release notes place-fill
    • d71d3ce Merge pull request #43414 from tensorflow/mihaimaruseac-patch-1-1
    • 9c91596 Fix missing import
    • f9f12f6 Merge pull request #43391 from tensorflow/mihaimaruseac-patch-4
    • 3ed271b Solve leftover from merge conflict
    • 9cf3773 Merge pull request #43358 from tensorflow/mm-patch-r2.3
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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 1.2.0 to 2.9.3

    Bump tensorflow from 1.2.0 to 2.9.3

    Bumps tensorflow from 1.2.0 to 2.9.3.

    Release notes

    Sourced from tensorflow's releases.

    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)

    Changelog

    Sourced from tensorflow's changelog.

    Release 2.9.3

    This release introduces several vulnerability fixes:

    Release 2.8.4

    This release introduces several vulnerability fixes:

    ... (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
  • Bump numpy from 1.11.1 to 1.22.0

    Bump numpy from 1.11.1 to 1.22.0

    Bumps numpy from 1.11.1 to 1.22.0.

    Release notes

    Sourced from numpy's releases.

    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
  • inception module import error

    inception module import error

    after download inception module on common/inception/, I got following error

    from inception.inception_score import inception_score, Inception
ModuleNotFoundError: No module named 'inception.inception_score'

    do I edited evaluation.py.

    from inception.inception_score

    to

    from common.inception.inception_score

    is working on root directory of this repository and exec train.py is correct ?

    opened by atsukoba 0
  • reading data from cifar-10-fid.npz file buggy

    reading data from cifar-10-fid.npz file buggy

    I downloaded your cifar-10-fid.npz file and when trying to load the data using fid = FID(stat["mean"], stat["cov"], mean, cov) then get following error: KeyError: 'mean is not a file in the archive'

    Is there a discrepancy between the code and the file?

    opened by heikir 0
  • why only consider GPU case?

    why only consider GPU case?

    hello, would you please also consider CPU(especially iDeep enbaled) case? In some files, we can see that you did "model.to_gpu()" or "chainer.cuda.to_cpu(**)" without checking directly. Thanks.

    opened by mingxiaoh 0
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