Comments (2)
alanchiao
commented on May 16, 2024
Hi @jusonn
In general, TFMOT is working to make our tools backend-agnostic (meaning support TensorFlow, TFLite, and other ones also).
As you can see in this issue, we are working to make latency improvements for both TF and TFLite via pruning.
We will be launching a tool for quantization-aware training soon. TensorRT mentions its predecessor, contrib.quantize, in its docs.
from model-optimization.
alanchiao
commented on May 16, 2024
For a while, the post-training quantization tools will be available only through TFLite. Again, we working to generalize it.
Closing and feel free to reopen and if you don't think your question is fully answered.
from model-optimization.
Related Issues (20)
- QAT aware training for mobilenetV2 not working HOT 1
- Determinism is not yet supported in GPU implementation of FakeQuantWithMinMaxVarsGradient HOT 2
- batch norm layer quantization error HOT 2
- 16x8 Quantization fails for RNN model - Max and min for dynamic tensors should be recorded during calibration HOT 4
- float16 quantization runs out of memory for LSTM model HOT 3
- float16 quantization runs out of memory for LSTM model HOT 1
- Add a default PruningPolicy that filters out any layers not supported by the API HOT 1
- Add batch norm to default_n_bit_quantize_registry and default_8_bit_quantize_registry HOT 2
- Quant aware training in tensorflow model optimization HOT 4
- [COLAB] No module named 'tensorflow_model_optimization' HOT 1
- Module Import Error HOT 2
- A error about quantization aware training HOT 2
- MobileNetV3 QAT TFLite Conversion Issue HOT 4
- Error in MovingAverageQuantizer with per_axis=True due to missing parameters in _add_range_weights
- strange behavior when quantizing a model. HOT 2
- Support for Recurrent layers for Quantization Aware Training. HOT 1
- Can't use TFMOT version 0.8.0 due to missing dependency HOT 1
- Any plans to support keras3? HOT 3
- Cannot use Quantize layer and use abstract class and methods HOT 1
- Custom layer with Concat afterwards causes an error during QAT modeling HOT 3
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