<!DOCTYPE html> quantize_model 功能说明训练后量化接口,将输入的待量化的图结构按照给定的量化配置文件进行量化处理,在传入的图结构中插入权重量化、数据量化相关的算子,生成量化因子记录文件record_file,返回修改后的torch.nn.module校准模型。 函数原型calibration_
quantize_model 功能说明 训练后量化接口,根据用户设置的量化配置文件对图结构进行量化处理,该函数在config_file指定的层插入权重量化层,完成权重量化,并插入数据量化层,将修改后的网络存为新的模型文件。 函数原型 quantize_model(graph, modified_model_file, modifi
针对你遇到的问题“gpu is required to quantize or run quantize model”,以下是详细的解答: 确认GPU在模型量化和运行量化模型中的作用: 在深度学习领域,GPU(图形处理单元)因其强大的并行计算能力,被广泛用于加速模型训练和推理过程。在模型量化这一优化步骤中,GPU同样发挥着关键作用。量化是将模型的权重从浮点数...
import onnx # 加载已导出的ONNX模型 model_path = "matmul_model.onnx" quantized_model_path = "matmul_model_quantized.onnx" # 对模型进行动态量化 quantize_dynamic( model_path, quantized_model_path, weight_type=QuantType.QInt8 # 使用INT8进行权重量化 ) # 加载量化后的ONNX模型 session = ort....
执行quantize_torch_model.py后,pytorch模型export出的onnx模型并不是QNN模型,虽然export导出了一份json文件和onnx模型,但是有没有办法获得一个QNN量化后的int8模型(体积比原fp32模型小的)? 你platform那边选QNN平台,然后直接用SNPE_convert_dlc那个工具可以读取你的json和onnx模型转成dlc,QNN 可以用 ...
23 + func Quantize(infile, outfile string, ftype filetype) error { 24 24 cinfile := C.CString(infile) 25 25 defer C.free(unsafe.Pointer(cinfile)) 26 26 @@ -29,58 +29,10 @@ func Quantize(infile, outfile, filetype string) error { 29 29 30 30 params := C.llama_model_...
export_onnx() got multiple values for keyword argument 'quantize'根据您提供的信息，您在使用ModelSc...
An example code would be: bie_model_path = km.analysis(input_mapping, threads=4, datapath_bitwidth_mode="all int8") This would quantize GlobalAveragePool into 8-bit, which would avoid the issue of not having enough nmem.
quantization import quantize_dynamic, QuantType model_fp32 = 'yolov8.onnx' model_int8 = 'yolov8_quantized.onnx' # Quantize quantize_dynamic(model_fp32, model_int8, weight_type=QuantType.QUInt8) This uses dynamic quantization, but it's a good starting point. For static quantization, the...
from typing import Iterable import torch from torch.utils.data import DataLoader from ppq import BaseGraph, QuantizationSettingFactory, TargetPlatform from ppq.api import export_ppq_graph, quantize_onnx_model BATCHSIZE = 32 INPUT_SHAPE = [3, 224, 224] DEVICE = 'cuda' # only cuda is fully ...