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feat: use signed weights in compile_torch_model

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jfrery
2021-12-20 11:45:20 +01:00
committed by Jordan Fréry
parent 57bbb8feab
commit 65be1b0818
4 changed files with 14 additions and 4 deletions
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10
tests/quantization/test_quantized_module.py
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@@ -80,7 +80,11 @@ N_BITS_ATOL_TUPLE_LIST = [
pytest.param(FC, (100, 32 * 32 * 3)),
],
)
def test_quantized_linear(model, input_shape, n_bits, atol, seed_torch):
@pytest.mark.parametrize(
"is_signed",
[pytest.param([False, True])],
)
def test_quantized_linear(model, input_shape, n_bits, atol, is_signed, seed_torch):
"""Test the quantized module with a post-training static quantization.
With n_bits>>0 we expect the results of the quantized module
@@ -97,7 +101,9 @@ def test_quantized_linear(model, input_shape, n_bits, atol, seed_torch):
# Predict with real model
numpy_prediction = numpy_fc_model(numpy_input)
# Quantize with post-training static method
post_training_quant = PostTrainingAffineQuantization(n_bits, numpy_fc_model)
post_training_quant = PostTrainingAffineQuantization(
n_bits, numpy_fc_model, is_signed=is_signed
)
quantized_model = post_training_quant.quantize_module(numpy_input)
# Quantize input
q_input = QuantizedArray(n_bits, numpy_input)