Update tensorflow-model-optimization requirement from ~=0.7.2 to ~=0.7.3

[dependabot[bot]]

Updates the requirements on tensorflow-model-optimization to permit the latest version.

Release notes

Sourced from tensorflow-model-optimization's releases.

TensorFlow Model Optimization 0.7.3

TFMOT 0.7.3 add remove_input_range method that removes input range after apply quantize.

Changelog

Sourced from tensorflow-model-optimization's changelog.

TensorFlow Model Optimization 0.7.3

TFMOT 0.7.3 add remove_input_range method that removes input range after apply quantize.

TensorFlow Model Optimization 0.7.2

TFMOT 0.7.2 removes support for PeepholeLSTMCell that was removed in Keras.

TensorFlow Model Optimization 0.7.1

TFMOT 0.7.1 fixes a bug in tensor_encoding in 9e4c106267a4a7f61e0d90b0848db15fd063b80e.

TensorFlow Model Optimization 0.7.0

TFMOT 0.7.0 adds updates for Quantization Aware Training (QAT) and Pruning API. Adds support for structured (MxN) pruning. QAT now also has support for layers with swish activations and ability to disable per-axis quantization in the default8_bit scheme. Adds support for combining pruning, QAT and weight clustering.

Keras Quantization API: Tested against TensorFlow 2.6.0, 2.5.1 and nightly with Python 3.

• Added QuantizeWrapperV2 class which preserves order of weights is the default for quantize_apply.
• Added a flag to disable per-axis quantizers in default8_bit scheme.
• Added swish as supported activation.

Keras pruning API: Tested against TensorFlow 2.6.0, 2.5.1 and nightly with Python 3.

• Added structural pruning with MxN sparsity.

Keras clustering API:

• Added support for RNNSimple, LSTM, GRU, StackedRNNCells, PeepholeLSTMCell, and Bidirectional layers.
• Updated and fixed sparsity-preserving clustering.
• Added an experimental quantization schemes for Quantization Aware Training for collaborative model.optimization:
  • Pruning-Clustering-preserving QAT: pruned and clustered model can be QAT trained with preserved sparsity and the number of clusters.
• Updated Clustering initialization default to KMEANS_PLUS_PLUS.

TensorFlow Model Optimization 0.6.0

TFMOT 0.6.0 adds some additional features for Quantization Aware Training (QAT) and Pruning API. Adds support for overriding and subclassing default quantization schemes. Adds input quantizer for annotated quantized layers without annotated input layers. QAT now also has support for Conv2DTranspose and tanh layers. For Pruning API, added pruning policy for pruning registries targeting specific hardware.

Keras quantization API: Tested against TensorFlow 2.4.2, 2.5.0 and nightly with Python 3.

Keras pruning API:

... (truncated)

Commits

• 38ca8c9 Update release notes and version for TFMOT release 0.7.3.
• c35be72 set seed to ensure consistency of ci test
• 9dc53f6 Merge pull request #986 from jamwar01:patch_issue_979
• 24603dd Make the utils module public available.
• 322587c Adds faster variant of EPR.
• cbc0629 Remove unused import
• d31c3fc Fixes for GitHub Issue #979
• 98e2f5e Add the option to enable adding tensorflow dependency control to the randomiz...
• c8fc87f Merge pull request #946 from miykael:patch-1
• b67c88f Add remove_input_range method that removes input range after apply quantize.
• Additional commits viewable in compare view

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

---

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

• @dependabot rebase will rebase this PR
• @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
• @dependabot merge will merge this PR after your CI passes on it
• @dependabot squash and merge will squash and merge this PR after your CI passes on it
• @dependabot cancel merge will cancel a previously requested merge and block automerging
• @dependabot reopen will reopen this PR if it is closed
• @dependabot close will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually
• @dependabot ignore this major version will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this minor version will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself)
• @dependabot ignore this dependency will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)