Comments (2)
Currently, BatchNorm is only supported when it's input is from the Conv or Dense. (e.g. Conv-BN, Dense-BN) and it considered the BNs will be FUSED into Conv or Dense after TFLite conversion. We don't considered standalone case for BN.
In MobileNetV3 structure, The output itself without top (backbone only) already normalized right before the output. I don't think we need additional BNs. Is there any specific reason you need them?
from model-optimization.
Yeah, you are right. It already performs normalization before the output.
I didn't consider that, and now I removed the batchnorm layer and it works well!
Thanks.
from model-optimization.
Related Issues (20)
- 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
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- 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
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- Cannot use Quantize layer and use abstract class and methods HOT 1
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from model-optimization.