- #ZEN 2.3 LITE REQUIREMENTS INSTALL#
- #ZEN 2.3 LITE REQUIREMENTS UPDATE#
- #ZEN 2.3 LITE REQUIREMENTS LICENSE#
Remove memmap option from CziFile.asarray (breaking).Ĭhange spline interpolation order to 0 (breaking). 2017.7.11 Add ‘out’ parameter to CziFile.asarray. 2017.7.13 Add function to convert CZI file to memory-mappable TIFF file. Remove bgr2rgb options.ĭecode JpegXR directly from byte arrays. Return timestamps, focus positions, events, and luts as tuple or ndarray 2017.7.21 Use multi-threading in CziFile.asarray to decode and copy segment data.Īlways convert BGR to RGB. Return metadata as XML unicode string or dict, not etree. Make Segment.SID and DimensionEntryDV1.dimension str types. 2018.6.18 Save CZI metadata to TIFF description in czi2tif. Require imagecodecs package for decoding JpegXrFile, JpgFile, and LZW. 2018.8.29 Move czifile.py and related modules into zisraw package. 2018.10.18 Rename zisraw package to czifile. Make scipy an optional dependency fallback on ndimage or fail on zoom(). Use imagecodecs_lite as a fallback for imagecodecs. 2019.5.22 Fix czi2tif conversion when CZI metadata contain non-ASCII characters. 2019.6.18 Add package main function to view CZI files.įix czi2tif conversion on Python 2. Ownership or maintainship is open to transfer or close if there were any issue.Revisions 2019.7.2 Require tifffile 2019.7.2. Maintainer of this package had tried to contact TensorFlow maintainers for licensing issues, but received no reply. The schema.fbs is obtained from TensorFlow directly.
#ZEN 2.3 LITE REQUIREMENTS LICENSE#
The maintainer will take the responsibility to upload change to PyPI when merged.Īpache License Version 2.0 as TensorFlow's.Push your change and open Pull Request.Don't forget to re-install the newly built tflite package before testing it. Build and Test (simply pytest) around.
#ZEN 2.3 LITE REQUIREMENTS UPDATE#
#ZEN 2.3 LITE REQUIREMENTS INSTALL#
And install flatbuffer compiler (you may need to manually build it). Install additional depdendency via pip install -r requirements.txt.This is pretty simple, instructions as below. If you notice that the package is out of date, please feel free to contribute new versions. (): maintains API compability in 2.4.0, see this issue.Īs the operator definition may change across different TensorFlow versions, this package needs to be updated accordingly.TensorFlow sometimes leaves compability hanlding of the TFLite model to the users.Īs these are API breaking change that can be easily fixed, we do this in the tflite package. tflite.BUILTIN_OPCODE2NAME: a dict that maps the opcode to name of all the builtin operators.tflite.opcode2name(): get the type name of given opcode.Builtin opcode helper: The opcode is encoded as digits which is hard to parse for human.Easy import: You don't need to import every classes and funtions in tflite ( example), but instead with a sigle import tflite ( example).The generated python package is friendly to use sometimes. It's recommended to install the version that same as the TensorFlow that generates the TFLite model.
Install the package and use it like what you build from the TensorFlow codebase. For background, please refer to Introducing TFLite Parser Python Package. This tflite package parses TensorFlow Lite (TFLite) models ( *.tflite), which are built by TFLite converter.
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