nncodec-0.3.1

• Fixed issue #2 "Error : expected an indented block after 'except' statement on line 240"
• Updated version numbers and requirements_cu11.txt

nncodec-0.3.0

Improvements and bugfixes:

• Fixed a bug in PyTorch-model loading
• Improved loading and handling of TensorFlow models
• Added support for Metal Performance Shaders (MPS)
• Added requirement file for virtual python environment on mac os

nncodec-0.2.2

Fixed incorrect decoding of 1-dimensional tensors of size 1

nncodec-0.2.1

Fix:

• Removed the import of an unused python-package which caused errors

nncodec-0.2.0

Bug fixes and improvements:

• Fixed an issue, which caused a wrong return value when specifying return_decompressed_model in decompress_model
• compress_model now also accepts arrays of type uint8, uint16, int8, int16 and float16 (but internally converts them into int32 or float32)
• improved guessing of block_id and parameter_types for TensorFlow and PyTorch
• NNCodec now accepts to only specify a subset of the tensors for block_id_and_param_type

nncodec-0.1.8

Integration of minor fixes:

• The file 'imagenet_validation_files.txt' in framework/applications/datasets has not been copied during the installation process
• The folder 'tuning' is not required to be present when using ImageNet. The data used for fine tuning is specifed in 'imagenet_validation_files.txt'

nncodec-0.1.7

Aligned requirements.txt with requirements_cu11.txt and updated handling of tensorflow state dictionaries such that it compatible with the current version of the "h5py" package.

nncodec-0.1.6

Bugfix for compression of arbitrary numpy dictionaries with 'compress':
- The encoder crashed during encoding of arbitrary numpy dictionaries with the 'compress' function. This was caused by missing entries for the parameter types in the model_information dictionary.

nncodec-0.1.5

Fixed setting a qp per tensor using the encoder parameter qp_per_tensor. The encoder crashed when using this parameter.

nncodec-0.1.4

Updated the python requirement files. Now, instead of specifiying specific package versions, only minimum requirements are defined. This simplifies integration of NNCodec in existing python projects.