| commit | 43b8f6e71001be7bacee4559e3021965ae67cf33 | [log] [tgz] |
|---|---|---|
| author | Peng Sun <peng.sun@arm.com> | Wed Apr 22 12:28:06 2020 -0700 |
| committer | TensorFlower Gardener <gardener@tensorflow.org> | Wed Apr 22 12:33:46 2020 -0700 |
| tree | b88ed714bc51ee6997e2705e7fcc5085a7201905 | |
| parent | ea0535f29636c6f0487e033915c84e96fad516b4 [diff] |
PR #34903: TransposeConv with Bias Imported from GitHub PR https://github.com/tensorflow/tensorflow/pull/34903 ### Description of issue: For generating .tflite file with TFLiteConverter, when model contains Conv2DTranspose layers, bias cannot fold into Operator TRANSPOSECONV. It will result with extra Op ADD following Op TRANSPOSECONV. But with other CONV-like layers (Conv2D, DepthwiseConv2D), bias will be fold into CONV layer. (check detailed TF issue: https://github.com/tensorflow/tensorflow/issues/34622) ### PR try to resolve it: So we try to resolve this issue by enable TransposeConv with bias for TFLite: - Update TFLite graph_transform features with: fill TransposeConv bias with zero if there is no bias fuse bias add into preceding TransposeConv(TEST added) - Update TransposeConv with bias: add bias input to TransposeConv add optional bias to TransposeConv kernels ### example of the results: TRANSPOSE_CONV inputs: 1. output_shape 2. weights 3. activation 4. bias  ### Need to discuss: ~~currently this PR only update reference kernel for transpose_conv, optimised kernal is commented out.~~ ~~several TEST need to be added as well, but~~ further suggestions are needed for adding additional test. Copybara import of the project: -- 1c6eb9c98229a9e8248dc1fe913a20cc6dd89332 by Peng Sun <peng.sun@arm.com>: Fuse TransposeConv with Bias For generating .tflite file with TFLiteConverter, when model contains Conv2DTranspose layers, bias cannot fold into Operator TRANSPOSECONV. It will result with extra Op ADD following Op TRANSPOSECONV. But with other CONV-like layers (Conv2D, DepthwiseConv2D), bias will be fold into CONV layer. (check TF issue: https://github.com/tensorflow/tensorflow/issues/34622) So we try to resolve this issue by enable TransposeConv with bias for TFLite: Update TFLite graph_transform features with: fill TransposeConv bias with zero if there is no bias fuse bias add into preceding TransposeConv(TEST added) Update TransposeConv with bias: add bias input to TransposeConv add optional bias to TransposeConv kernels(TEST added) -- 22611b880c94eb753c88a0a3e2977200e55ebd2c by Peng Sun <peng.sun@arm.com>: clang-format with google style. COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/34903 from psunn:TransposeConvWithBias 22611b880c94eb753c88a0a3e2977200e55ebd2c PiperOrigin-RevId: 307872447 Change-Id: I367fcd65f2662f4c7846d37bc69dc43670c83961
Documentation |
|---|
 |
TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.
TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.
Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org. See all the mailing lists.
See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.
To install the current release, which includes support for CUDA-enabled GPU cards (Ubuntu and Windows):
$ pip install tensorflow
A smaller CPU-only package is also available:
$ pip install tensorflow-cpu
To update TensorFlow to the latest version, add --upgrade flag to the above commands.
Nightly binaries are available for testing using the tf-nightly and tf-nightly-cpu packages on PyPi.
$ python
>>> import tensorflow as tf >>> tf.add(1, 2).numpy() 3 >>> hello = tf.constant('Hello, TensorFlow!') >>> hello.numpy() b'Hello, TensorFlow!'
For more examples, see the TensorFlow tutorials.
If you want to contribute to TensorFlow, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.
We use GitHub issues for tracking requests and bugs, please see TensorFlow Discuss for general questions and discussion, and please direct specific questions to Stack Overflow.
The TensorFlow project strives to abide by generally accepted best practices in open-source software development:
| Build Type | Status | Artifacts |
|---|---|---|
| Linux CPU |  | PyPI |
| Linux GPU |  | PyPI |
| Linux XLA |  | TBA |
| macOS |  | PyPI |
| Windows CPU |  | PyPI |
| Windows GPU |  | PyPI |
| Android |  |  |
| Raspberry Pi 0 and 1 |   | Py2 Py3 |
| Raspberry Pi 2 and 3 |   | Py2 Py3 |
| Build Type | Status | Artifacts |
|---|---|---|
| Linux AMD ROCm GPU Nightly | Nightly | |
| Linux AMD ROCm GPU Stable Release | Release 1.15 / 2.x | |
| Linux s390x Nightly | Nightly | |
| Linux s390x CPU Stable Release | Release | |
| Linux ppc64le CPU Nightly | Nightly | |
| Linux ppc64le CPU Stable Release | Release 1.15 / 2.x | |
| Linux ppc64le GPU Nightly | Nightly | |
| Linux ppc64le GPU Stable Release | Release 1.15 / 2.x | |
| Linux CPU with Intel® MKL-DNN Nightly | Nightly | |
| Linux CPU with Intel® MKL-DNN Stable Release | Release 1.15 / 2.x | |
| Red Hat® Enterprise Linux® 7.6 CPU & GPU Python 2.7, 3.6 | 1.13.1 PyPI |
Learn more about the TensorFlow community and how to contribute.