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[DOCS] Improving code snippets for quantization (#19479)

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* improve-snippets

* Apply suggestions from code review

Co-authored-by: Alexander Suslov <alexander.suslov@intel.com>

* Update docs/optimization_guide/nncf/ptq/code/ptq_tensorflow.py

Co-authored-by: Alexander Suslov <alexander.suslov@intel.com>

* update-path

* Update docs/optimization_guide/nncf/ptq/code/ptq_torch.py

---------

Co-authored-by: Alexander Suslov <alexander.suslov@intel.com>
This commit is contained in:
Sebastian Golebiewski 2023-08-30 11:52:36 +02:00 committed by GitHub
parent 3e8c0fac1b
commit 87f6e34a56
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 8 additions and 6 deletions
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3
docs/optimization_guide/nncf/ptq/code/ptq_onnx.py
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@ -16,7 +16,8 @@ calibration_dataset = nncf.Dataset(calibration_loader, transform_fn)
#! [dataset]
#! [quantization]
model = ... # onnx.ModelProto object
import onnx
model = onnx.load("model_path")
quantized_model = nncf.quantize(model, calibration_dataset)
#! [quantization]

5
docs/optimization_guide/nncf/ptq/code/ptq_openvino.py
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@ -15,14 +15,13 @@ calibration_dataset = nncf.Dataset(calibration_loader, transform_fn)
#! [dataset]
#! [quantization]
model = ... # openvino.runtime.Model object
import openvino.runtime as ov
model = ov.Core().read_model("model_path")
quantized_model = nncf.quantize(model, calibration_dataset)
#! [quantization]
#! [inference]
import openvino.runtime as ov
# compile the model to transform quantized operations to int8
model_int8 = ov.compile_model(quantized_model)

3
docs/optimization_guide/nncf/ptq/code/ptq_tensorflow.py
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@ -15,7 +15,8 @@ calibration_dataset = nncf.Dataset(calibration_loader, transform_fn)
#! [dataset]
#! [quantization]
model = ... # tensorflow.Module object
import tensorflow as tf
model = tf.saved_model.load("model_path")
quantized_model = nncf.quantize(model, calibration_dataset)
#! [quantization]

3
docs/optimization_guide/nncf/ptq/code/ptq_torch.py
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@ -15,7 +15,8 @@ calibration_dataset = nncf.Dataset(calibration_loader, transform_fn)
#! [dataset]
#! [quantization]
model = ... # torch.nn.Module object
import torchvision
model = torchvision.models.resnet50(pretrained=True)
quantized_model = nncf.quantize(model, calibration_dataset)
#! [quantization]