🙄 Difficult algorithm, Simple code.

Related tags

Deep Learning machine-learning deep-neural-networks reinforcement-learning deep-learning tensorflow linear-regression gan image-classification resnet object-detection vgg16 pix2pix convolutional-neural-network tensorflow-examples dcgan-tensorflow yolov3 unet-image-segmentation fcn8s tensorflow2
Overview

🎉TensorFlow2.0-Examples🎉!

"Talk is cheap, show me the code." ----- Linus Torvalds

Branch Stars Forks Awesome Awesome


This tutorial was designed for easily diving into TensorFlow2.0. it includes both notebooks and source codes with explanation. It will be continuously updated ! 🐍 🐍 🐍 🐍 🐍 🐍

Contents

1 - Introduction

  • Hello World (notebook) (code). Very simple example to learn how to print "hello world" using TensorFlow.
  • Variable (notebook) (code). Learn to use variable in tensorflow.
  • Basical operation (notebook) (code). A simple example that covers TensorFlow basic operations.
  • Activation (notebook) (code). Start to know some activation functions in tensorflow.
  • GradientTape (notebook) (code). Introduce a key technique for automatic differentiation

2 - Basical Models

  • Linear Regression (notebook) (code). Implement a Linear Regression with TensorFlow.
  • Logistic Regression (notebook) (code). Implement a Logistic Regression with TensorFlow.
  • Multilayer Perceptron Layer (notebook) (code). Implement Multi-Layer Perceptron Model with TensorFlow.
  • CNN (notebook) (code). Implement CNN Model with TensorFlow.

3 - Neural Network Architecture

  • VGG16 (notebook) (code)(paper). VGG16: Very Deep Convolutional Networks for Large-Scale Image Recognition.
  • Resnet (notebook) (code)(paper). Resnet: Deep Residual Learning for Image Recognition. 🔥 🔥 🔥
  • AutoEncoder (notebook) (code)(paper). AutoEncoder: Reducing the Dimensionality of Data with Neural Networks.

4 - Object Detection

  • RPN (notebook) (code)(paper). RPN: a Region Proposal Network 🔥 🔥 🔥 🔥 🔥

  • MTCNN (notebook) (code)(paper). MTCNN: Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks. (Face detection and Alignment) 🔥 🔥

  • YOLOv3 (notebook) (code)(paper). YOLOv3: An Incremental Improvement. 🔥 🔥 🔥 🔥

  • SSD (notebook) (code)(paper). SSD: Single Shot MultiBox Detector. 🔥 🔥 🔥 🔥 【TO DO】

  • Faster R-CNN (notebook) (code)(paper). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. 🔥 🔥 🔥 🔥 【TO DO】

5 - Image Segmentation

  • FCN (notebook) (code)(paper). FCN: Fully Convolutional Networks for Semantic Segmentation. 🔥 🔥 🔥 🔥 🔥

  • Unet (notebook) (code)(paper). U-Net: Convolutional Networks for Biomedical Image Segmentation. 🔥 🔥

6 - Generative Adversarial Networks

  • DCGAN (notebook) (code)(paper). Deep Convolutional Generative Adversarial Network.
  • Pix2Pix (notebook) (code)(paper). Image-to-Image Translation with Conditional Adversarial Networks.

7 - Utils

  • Multiple GPU Training (notebook)(code). Use multiple GPU to train your model.
Comments
  • How to save model to file pb extension in yolov3 example?

    How to save model to file pb extension in yolov3 example?

    Hi, Can you help, how to save model to file pb extension I add this code model.save('/saved_model/yolov3_mnist') after loop training epoch, but the pb file is not complete. I use Netron to view graph of that file, but It is not true graph when compared to other pb file

    opened by huanbd 4
  • TensorRT

    TensorRT

    Did anyone try to create the inference with TensorRT & TF2 for YOLOv3 by any chance, I cant get the trt.TrtGraphConverter() to convert for whatever reason....

    opened by Pidem 4
  • StartAbort Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above

    StartAbort Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above

    Traceback (most recent call last): File "video_demo.py", line 59, in pred_bbox = model.predict_on_batch(image_data) File "/home/qw/.local/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py", line 1157, in predict_on_batch outputs = self.predict_function(inputs) File "/home/qw/.local/lib/python3.7/site-packages/tensorflow_core/python/keras/backend.py", line 3740, in call outputs = self._graph_fn(*converted_inputs) File "/home/qw/.local/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 1081, in call return self._call_impl(args, kwargs) File "/home/qw/.local/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 1121, in _call_impl return self._call_flat(args, self.captured_inputs, cancellation_manager) File "/home/qw/.local/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 1224, in _call_flat ctx, args, cancellation_manager=cancellation_manager) File "/home/qw/.local/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py", line 511, in call ctx=ctx) File "/home/qw/.local/lib/python3.7/site-packages/tensorflow_core/python/eager/execute.py", line 67, in quick_execute six.raise_from(core._status_to_exception(e.code, message), None) File "", line 3, in raise_from tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[node conv2d/Conv2D (defined at /home/qw/.local/lib/python3.7/site-packages/tensorflow_core/python/framework/ops.py:1751) ]] [Op:__inference_keras_scratch_graph_13297]

    Function call stack: keras_scratch_graph

    opened by clemente0420 3
  • 请教 FCN训练时出错

    请教 FCN训练时出错

    Epoch 1/30 Traceback (most recent call last): File "/home/hua/PycharmProjects/test/5ImageSegmentation/1FCN/train.py", line 28, in model.fit_generator(TrainSet, steps_per_epoch=6000, epochs=30, callbacks=[callback]) File "/home/hua/.local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 324, in new_func return func(*args, **kwargs) File "/home/hua/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 1479, in fit_generator initial_epoch=initial_epoch) File "/home/hua/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 66, in _method_wrapper return method(self, *args, **kwargs) File "/home/hua/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py", line 848, in fit tmp_logs = train_function(iterator) File "/home/hua/.local/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py", line 580, in call result = self._call(*args, **kwds) File "/home/hua/.local/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py", line 627, in _call self._initialize(args, kwds, add_initializers_to=initializers) File "/home/hua/.local/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py", line 506, in _initialize *args, **kwds)) File "/home/hua/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 2446, in _get_concrete_function_internal_garbage_collected graph_function, _, _ = self._maybe_define_function(args, kwargs) File "/home/hua/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 2777, in _maybe_define_function graph_function = self._create_graph_function(args, kwargs) File "/home/hua/.local/lib/python3.6/site-packages/tensorflow/python/eager/function.py", line 2667, in _create_graph_function capture_by_value=self._capture_by_value), File "/home/hua/.local/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py", line 981, in func_graph_from_py_func func_outputs = python_func(*func_args, **func_kwargs) File "/home/hua/.local/lib/python3.6/site-packages/tensorflow/python/eager/def_function.py", line 441, in wrapped_fn return weak_wrapped_fn().wrapped(*args, **kwds) File "/home/hua/.local/lib/python3.6/site-packages/tensorflow/python/framework/func_graph.py", line 968, in wrapper raise e.ag_error_metadata.to_exception(e) TypeError: in user code:

    /home/hua/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training.py:571 train_function  *
    outputs = self.distribute_strategy.run(
/home/hua/PycharmProjects/test/5ImageSegmentation/1FCN/fcn8s.py:106 call  *
    h = h[:, 5:5 + upscore2.shape[1], 5:5 + upscore2.shape[2], :]  # channel last

TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'

    Process finished with exit code 1

    opened by huihua7882 3
  • 有关YOLOv3中Dataset类确定真值在哪个prior中的疑问。

    有关YOLOv3中Dataset类确定真值在哪个prior中的疑问。

    在Dataset类中，函数preprocess_true_boxes中需要计算iou来判定将目标放在哪一个prior中。其中有一个判定条件是iou_mask = iou_scale > 0.3。似乎只要是iou大于0.3的prior都会被赋值。这样可能会出现只有一个目标，但却被赋值到多个prior的情况，请问为何要这样设计？有什么好处呢？

    dataset.py

    opened by tsycnh 3
  • yolov3模型定义方法与其他检测模型不同

    yolov3模型定义方法与其他检测模型不同

    各位好 关于模型库中的检测模型，我有一个问题想要咨询一下 为什么RPN，MTCNN，SSD等模型采用的是定义模型类的方法，而yolov3采用了函数API的方法呢？如下

    1- With the “functional API”, where you start from Input, you chain layer calls to specify the model’s forward pass, and finally you create your model from inputs and outputs:

    import tensorflow as tf
inputs = tf.keras.Input(shape=(3,))
outputs = tf.keras.layers.Dense(4, activation='relu')(inputs)
model = tf.keras.Model(inputs=inputs, outputs=outputs)

    2- 第二种，类似于自己写一个subclass Model in that case, you should define your layers in init and you should implement the model’s forward pass in call.

    class MyModel(tf.keras.Model):
    def __init__(self):
        super(MyModel, self).__init__()
        self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
    def call(self, inputs):
        x = self.dense1(inputs)
        return x
model = MyModel()

    感谢

    opened by hnuhchen 2
  • Bump tensorflow from 2.0.0 to 2.7.2 in /4-Object_Detection/YOLOV3/docs

    Bump tensorflow from 2.0.0 to 2.7.2 in /4-Object_Detection/YOLOV3/docs

    Bumps tensorflow from 2.0.0 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
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 2.0.0 to 2.6.4 in /4-Object_Detection/YOLOV3/docs

    Bump tensorflow from 2.0.0 to 2.6.4 in /4-Object_Detection/YOLOV3/docs

    Bumps tensorflow from 2.0.0 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 pillow from 6.2.0 to 9.0.1 in /4-Object_Detection/YOLOV3/docs

    Bump pillow from 6.2.0 to 9.0.1 in /4-Object_Detection/YOLOV3/docs

    Bumps pillow from 6.2.0 to 9.0.1.

    Release notes

    Sourced from pillow's releases.

    9.0.1

    https://pillow.readthedocs.io/en/stable/releasenotes/9.0.1.html

    Changes

    • In show_file, use os.remove to remove temporary images. CVE-2022-24303 #6010 [@​radarhere, @​hugovk]
    • Restrict builtins within lambdas for ImageMath.eval. CVE-2022-22817 #6009 [radarhere]

    9.0.0

    https://pillow.readthedocs.io/en/stable/releasenotes/9.0.0.html

    Changes

    ... (truncated)

    Changelog

    Sourced from pillow's changelog.

    9.0.1 (2022-02-03)

    • In show_file, use os.remove to remove temporary images. CVE-2022-24303 #6010 [radarhere, hugovk]

    • Restrict builtins within lambdas for ImageMath.eval. CVE-2022-22817 #6009 [radarhere]

    9.0.0 (2022-01-02)

    • Restrict builtins for ImageMath.eval(). CVE-2022-22817 #5923 [radarhere]

    • Ensure JpegImagePlugin stops at the end of a truncated file #5921 [radarhere]

    • Fixed ImagePath.Path array handling. CVE-2022-22815, CVE-2022-22816 #5920 [radarhere]

    • Remove consecutive duplicate tiles that only differ by their offset #5919 [radarhere]

    • Improved I;16 operations on big endian #5901 [radarhere]

    • Limit quantized palette to number of colors #5879 [radarhere]

    • Fixed palette index for zeroed color in FASTOCTREE quantize #5869 [radarhere]

    • When saving RGBA to GIF, make use of first transparent palette entry #5859 [radarhere]

    • Pass SAMPLEFORMAT to libtiff #5848 [radarhere]

    • Added rounding when converting P and PA #5824 [radarhere]

    • Improved putdata() documentation and data handling #5910 [radarhere]

    • Exclude carriage return in PDF regex to help prevent ReDoS #5912 [hugovk]

    • Fixed freeing pointer in ImageDraw.Outline.transform #5909 [radarhere]

    ... (truncated)

    Commits
    • 6deac9e 9.0.1 version bump
    • c04d812 Update CHANGES.rst [ci skip]
    • 4fabec3 Added release notes for 9.0.1
    • 02affaa Added delay after opening image with xdg-open
    • ca0b585 Updated formatting
    • 427221e In show_file, use os.remove to remove temporary images
    • c930be0 Restrict builtins within lambdas for ImageMath.eval
    • 75b69dd Dont need to pin for GHA
    • cd938a7 Autolink CWE numbers with sphinx-issues
    • 2e9c461 Add CVE IDs
    • Additional commits viewable in compare view

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    dependencies 
    opened by dependabot[bot] 1
  • Bump tensorflow from 2.0.0 to 2.5.3 in /4-Object_Detection/YOLOV3/docs

    Bump tensorflow from 2.0.0 to 2.5.3 in /4-Object_Detection/YOLOV3/docs

    Bumps tensorflow from 2.0.0 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 pillow from 6.2.0 to 9.0.0 in /4-Object_Detection/YOLOV3/docs

    Bump pillow from 6.2.0 to 9.0.0 in /4-Object_Detection/YOLOV3/docs

    Bumps pillow from 6.2.0 to 9.0.0.

    Release notes

    Sourced from pillow's releases.

    9.0.0

    https://pillow.readthedocs.io/en/stable/releasenotes/9.0.0.html

    Changes

    ... (truncated)

    Changelog

    Sourced from pillow's changelog.

    9.0.0 (2022-01-02)

    • Restrict builtins for ImageMath.eval(). CVE-2022-22817 #5923 [radarhere]

    • Ensure JpegImagePlugin stops at the end of a truncated file #5921 [radarhere]

    • Fixed ImagePath.Path array handling. CVE-2022-22815, CVE-2022-22816 #5920 [radarhere]

    • Remove consecutive duplicate tiles that only differ by their offset #5919 [radarhere]

    • Improved I;16 operations on big endian #5901 [radarhere]

    • Limit quantized palette to number of colors #5879 [radarhere]

    • Fixed palette index for zeroed color in FASTOCTREE quantize #5869 [radarhere]

    • When saving RGBA to GIF, make use of first transparent palette entry #5859 [radarhere]

    • Pass SAMPLEFORMAT to libtiff #5848 [radarhere]

    • Added rounding when converting P and PA #5824 [radarhere]

    • Improved putdata() documentation and data handling #5910 [radarhere]

    • Exclude carriage return in PDF regex to help prevent ReDoS #5912 [hugovk]

    • Fixed freeing pointer in ImageDraw.Outline.transform #5909 [radarhere]

    • Added ImageShow support for xdg-open #5897 [m-shinder, radarhere]

    • Support 16-bit grayscale ImageQt conversion #5856 [cmbruns, radarhere]

    • Convert subsequent GIF frames to RGB or RGBA #5857 [radarhere]

    ... (truncated)

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    dependencies 
    opened by dependabot[bot] 1
  • Bump pillow from 6.2.0 to 9.3.0 in /4-Object_Detection/YOLOV3/docs

    Bump pillow from 6.2.0 to 9.3.0 in /4-Object_Detection/YOLOV3/docs

    Bumps pillow from 6.2.0 to 9.3.0.

    Release notes

    Sourced from pillow's releases.

    9.3.0

    https://pillow.readthedocs.io/en/stable/releasenotes/9.3.0.html

    Changes

    ... (truncated)

    Changelog

    Sourced from pillow's changelog.

    9.3.0 (2022-10-29)

    • Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

    • Initialize libtiff buffer when saving #6699 [radarhere]

    • Inline fname2char to fix memory leak #6329 [nulano]

    • Fix memory leaks related to text features #6330 [nulano]

    • Use double quotes for version check on old CPython on Windows #6695 [hugovk]

    • Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

    • Fixed set_variation_by_name offset #6445 [radarhere]

    • Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

    • Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

    • Added ExifTags enums #6630 [radarhere]

    • Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

    • Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

    • Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

    • Added GPS TIFF tag info #6661 [radarhere]

    • Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

    • Do not attempt normalization if mode is already normal #6644 [radarhere]

    ... (truncated)

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    dependencies 
    opened by dependabot[bot] 0
  • Bump tensorflow from 2.0.0 to 2.9.3 in /4-Object_Detection/YOLOV3/docs

    Bump tensorflow from 2.0.0 to 2.9.3 in /4-Object_Detection/YOLOV3/docs

    Bumps tensorflow from 2.0.0 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
  • Error when training with my own dataset using train.py: Failed to get convolution algorithm.

    Error when training with my own dataset using train.py: Failed to get convolution algorithm.

    Has any one encountered this issue when training with your own dataset?

    Traceback (most recent call last): File "train.py", line 93, in train_step(image_data, target) File "train.py", line 52, in train_step pred_result = model(image_data, training=True) File "/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py", line 985, in call outputs = call_fn(inputs, *args, **kwargs) File "/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py", line 386, in call inputs, training=training, mask=mask) File "/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/functional.py", line 508, in _run_internal_graph outputs = node.layer(*args, **kwargs) File "/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py", line 985, in call outputs = call_fn(inputs, *args, **kwargs) File "/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/keras/layers/convolutional.py", line 247, in call outputs = self._convolution_op(inputs, self.kernel) File "/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py", line 201, in wrapper return target(*args, **kwargs) File "/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 1018, in convolution_v2 name=name) File "/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 1148, in convolution_internal name=name) File "/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/ops/nn_ops.py", line 2592, in _conv2d_expanded_batch name=name) File "/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 938, in conv2d _ops.raise_from_not_ok_status(e, name) File "/home/ubuntu/.local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 6843, in raise_from_not_ok_status six.raise_from(core._status_to_exception(e.code, message), None) File "", line 3, in raise_from tensorflow.python.framework.errors_impl.UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [Op:Conv2D]

    opened by Guanzhao-hub 0
  • 运行video_demo.py时，只有第一帧预测正确后面都是nan，也就是后面没有预测框

    运行video_demo.py时，只有第一帧预测正确后面都是nan，也就是后面没有预测框

    有没有朋友遇到同样问题？我在运行video_demo.py时，只有第一帧预测正确后面都是nan。我怀疑是utils.load_weights(model, "./yolov3.weights")这一句的问题，于是把这一句放到了while true里，如代码所示，结果预测正确，但视频速度非常慢了，因为每次都要加载一次权重，我不知道该怎么修改def load_weights(model, weights_file)。求助！

    model = tf.keras.Model(input_layer, bbox_tensors) utils.load_weights(model, "./yolov3.weights") #model.summary() vid = cv2.VideoCapture(video_path) while True: #utils.load_weights(model, "./yolov3.weights") return_value, frame = vid.read() if return_value: frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) else: raise ValueError("No image!")

    opened by mastergao57 1
  • Problem about ResNet.

    Problem about ResNet.

    Meet a problem when applying model by compile-build-fit route.

    In 'resent.py' ,row 112: out = tf.reshape(out, (out.shape[0], -1))

    By using out.shape, it will return a tuple rather than a tensor, which will be a problem when training the model by the 'model.fit' method. Although it can be fine when setting run_fucntions_eagerly as True or just using Gradient Tape as in the 'main.py', it will cause unnecessary extra time when training.

    It can be fixed when using Keras.layers.Faltten() or out = tf.reshape(out, (tf.shape(out)[0], -1)) instead. About 25% faster in my computer.

    opened by chenfangqiu 0
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PaddleRobotics is an open-source algorithm library for robots based on Paddle, including open-source parts such as human-robot interaction, complex motion control, environment perception, SLAM positioning, and navigation.

简体中文 | English PaddleRobotics paddleRobotics是基于paddle的机器人开源算法库集，包括人机交互、复杂运动控制、环境感知、slam定位导航等开源算法部分。 人机交互 主动多模交互技术TFVT-HRI 主动多模交互技术是通过视觉、语音、触摸传感器等输入机器人

null 185 Dec 26, 2022
Sequential Model-based Algorithm Configuration

SMAC v3 Project Copyright (C) 2016-2018 AutoML Group Attention: This package is a reimplementation of the original SMAC tool (see reference below). Ho

AutoML-Freiburg-Hannover 778 Jan 5, 2023
Simulating Sycamore quantum circuits classically using tensor network algorithm.

Simulating the Sycamore quantum supremacy circuit This repo contains data we have obtained in simulating the Sycamore quantum supremacy circuits with

Feng Pan 46 Nov 17, 2022
An automated algorithm to extract the linear blend skinning (LBS) from a set of example poses

Dem Bones This repository contains an implementation of Smooth Skinning Decomposition with Rigid Bones, an automated algorithm to extract the Linear B

Electronic Arts 684 Dec 26, 2022
A pytorch reprelication of the model-based reinforcement learning algorithm MBPO

Overview This is a re-implementation of the model-based RL algorithm MBPO in pytorch as described in the following paper: When to Trust Your Model: Mo

Xingyu Lin 93 Jan 5, 2023
PyTorch implementation of Algorithm 1 of "On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models"

Code for On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models This repository will reproduce the main results from our pape

Mitch Hill 32 Nov 25, 2022
PlenOctree Extraction algorithm

PlenOctrees_NeRF-SH This is an implementation of the Paper PlenOctrees for Real-time Rendering of Neural Radiance Fields. Not only the code provides t

null 49 Nov 5, 2022