nncf: NNCF2.5 When quantizing the model, an error occurred: "RuntimeError: Could not find the bias value of the node."

thanks @alexsu52

but nncf.quantize(model, calibration_dataset, preset=nncf.QuantizationPreset.MIXED,fast_bias_correction=False,subset_size=2000,advanced_parameters=nncf.AdvancedQuantizationParameters(activations_range_estimator_params=nncf.RangeEstimatorParametersSet.MINMAX)this code is throwing an error:module 'nncf' has no attribute 'RangeEstimatorParametersSet' as same as activations_range_estimator_params=RangeEstimatorParametersSet.MINMAX

And I randomly selected representative images. Below is the download link.

https://drive.google.com/file/d/1CWwR4BzJSstEow4hEkNX9DvO5lgtO5Ys/view?usp=sharing

fp32 model: https://drive.google.com/file/d/14Xh9Uj4kEjxuWmGewGyT9b9dIHGjUp8V/view?usp=drive_link

Here is the quantization code I have, which ignores the post-processing layer of quantization:

import torch
import numpy as np
import onnx
from onnx.version_converter import convert_version
from torchvision import datasets
from torchvision import transforms
from PIL import Image
import nncf

model_path = "/home/1/fp32_mainbody_onnx_bs/const_shape_pp_main_body.onnx"

class StoreOriginalSize:
    def __init__(self):
        self.sizes = []  # a list to store original sizes

    def __call__(self, img):
        # This transform does not change the image, it only stores its original size
        self.sizes.append(img.size)  # save original size (width, height)
        return img

store_original_size = StoreOriginalSize()

normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
val_dataset = datasets.ImageFolder(
    root=f"/home/1/resize_images_2000",
    transform=transforms.Compose(
        [
            store_original_size,  # this will store original sizes before resizing
            transforms.Resize((640, 640)),
            transforms.ToTensor(),
            normalize,
        ]
    ),
)

val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False)
old_model = onnx.load(model_path)

model = convert_version(old_model, target_version=13)

onnx.checker.check_model(model)

input_names = [input.name for input in model.graph.input]
input_img_name = input_names[0]
input_scale_name = input_names[1]

def transform_fn(data_item):
    images, _ = data_item
    origin_width, origin_height = store_original_size.sizes.pop(0)
    scale = np.array([640 / origin_height, 640 / origin_width], dtype='float32').reshape(1, 2)
    return {input_img_name: images.numpy(),input_scale_name: scale}
    
calibration_dataset = nncf.Dataset(val_loader, transform_fn)
onnx_quantized_model = nncf.quantize(model, calibration_dataset, preset=nncf.QuantizationPreset.MIXED
, fast_bias_correction=False,subset_size=492,
        ignored_scope=nncf.IgnoredScope(
            #types=["Reshape","Slice","Concat"],  # ignore operations
            names=[
                "p2o.Slice.1",  # in the post-processing subgraph
                "p2o.Reshape.33",
                "p2o.Reshape.32",
                "p2o.Reshape.31",
                "p2o.Concat.24",
                "p2o.Slice.0",
                "p2o.Reshape.29",
                "p2o.Reshape.30",
                "p2o.Gather.0",
                "p2o.Mul.196",
                "p2o.Gather.2",
                "p2o.NonMaxSuppression.0",
                "p2o.Reshape.28",
                "p2o.Mul.194",
                "p2o.Add.208",
                "p2o.Add.206",
                "p2o.Mul.192",
                "p2o.Reshape.12",
                "p2o.MatMul.0",
                "p2o.Reshape.11",
                "p2o.Reshape.17",
                "p2o.MatMul.2",
                "p2o.Reshape.16",
                "p2o.Reshape.22",
                "p2o.MatMul.4",
                "p2o.Reshape.21",
                "p2o.Reshape.27",
                "p2o.MatMul.6",
                "p2o.Reshape.26",
                "p2o.Reshape.10",
                "p2o.Reshape.15",
                "p2o.TopK.0",
                "p2o.ReduceMin.0",
                "p2o.Gather.8",
                "p2o.Gather.10",
                "p2o.Div.94",
            ],
        ),
    )

int8_model_path = f"/home/1/int8_nncf_quant/int8_main_body_new_branch_bs_1_2000.onnx"
onnx.save(onnx_quantized_model, int8_model_path)

@l-bat I have uploaded the model to Google Drive. Here is the link: https://drive.google.com/file/d/14Xh9Uj4kEjxuWmGewGyT9b9dIHGjUp8V/view?usp=drive_link