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[POT] Fixed non-zero zero point value in symmetric quantized weights (#13013)

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* Fixed non-zero zero point val in symmquantized weights

* Update references

* Add tests
This commit is contained in:
Liubov Talamanova 2022-10-04 09:00:16 +01:00 committed by GitHub
parent 43db45a4fb
commit fcfc5c963b
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3 changed files with 7 additions and 4 deletions
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3
tools/pot/openvino/tools/pot/api/samples/classification/classification_sample.py
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@ -212,7 +212,8 @@ def optimize_model(args):
# Step 7 (Optional): Compress model weights quantized precision
# in order to reduce the size of final .bin file.
compress_model_weights(compressed_model)
if not args.keep_uncompressed_weights:
compress_model_weights(compressed_model)
return compressed_model, pipeline

4
tools/pot/openvino/tools/pot/graph/passes.py
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@ -11,7 +11,7 @@ from typing import List, Set
import numpy as np
from openvino.tools.mo.back.ForceStrictPrecision import ForceStrictPrecision
from openvino.tools.mo.back.compress_quantized_weights import CompressQuantizeWeights
from openvino.tools.mo.back.compress_quantized_weights import CompressQuantizeWeights, ZeroPointOptimizer
from openvino.tools.mo.ops.elementwise import Add
from openvino.tools.mo.ops.Cast import Cast
from openvino.tools.mo.ops.fakequantize import FakeQuantize
@ -1013,6 +1013,8 @@ def compress_weights(model: Graph):
add_removed_converts(model)
CompressQuantizeWeights().find_and_replace_pattern(model)
model.clean_up()
ZeroPointOptimizer().find_and_replace_pattern(model)
model.clean_up()
ForceStrictPrecision().find_and_replace_pattern(model)
model.clean_up()

4
tools/pot/tests/test_sanity.py
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@ -27,7 +27,7 @@ TEST_MODELS = [
'accuracy@top5': 0.907},
{}, 'CPU'),
('mobilenet-v2-pytorch', 'pytorch', 'DefaultQuantization', 'mixed', 300, {'accuracy@top1': 0.734,
('mobilenet-v2-pytorch', 'pytorch', 'DefaultQuantization', 'mixed', 300, {'accuracy@top1': 0.737,
'accuracy@top5': 0.91},
{}, 'CPU'),
@ -173,7 +173,7 @@ SIMPLIFIED_TEST_MODELS = [
('mobilenet-v2-pytorch', 'pytorch', 'DefaultQuantization', 'performance',
{'accuracy@top1': 0.707, 'accuracy@top5': 0.91}, []),
('mobilenet-v2-pytorch', 'pytorch', 'DefaultQuantization', 'performance',
{'accuracy@top1': 0.709, 'accuracy@top5': 0.904}, ['--input_shape=[1,3,?,?]'])
{'accuracy@top1': 0.706, 'accuracy@top5': 0.904}, ['--input_shape=[1,3,?,?]'])
]