(datas, 32) # 静态量化 quantize_static( model_input=model_fp32, # 输入模型 model_output=model_quant_static, # 输出模型 calibration_data_reader=data_reader, # 校准数据读取器 quant_format= QuantFormat.QDQ, # 量化格式 QDQ / QOperator activation_type=QuantType.QInt8, # 激活类型 Int8 / ...
ONNXRUNTIME提供的模型量化接口有如下三个: quantize_dynamic:动态量化quantize_static:静态量化quantize_qat:量化感知训练量化 FP16量化 首先需要安装好ONNX支持的FP16量化包,然后调用相关接口即可实现FP16量化与混合精度量化。安装FP16量化支持包命令行如下: pipinstall onnx onnxconverter-common 实现FP16量化的代码如...
from onnxruntime.quantization import CalibrationDataReader, QuantFormat, quantize_static, QuantType, CalibrationMethod from onnxruntime import InferenceSession, get_available_providers # 模型路径 model_fp32 = 'hrnet_coco_w32_256x192.onnx' model_quant_static = 'hrnet_quant.onnx' # 数据预处理 '...
量化模型将保存到给定的路径: q_static_opts = {"ActivationSymmetric":False, "WeightSymmetric":True} if torch.cuda.is_available(): q_static_opts = {"ActivationSymmetric":True, "WeightSymmetric":True} model_int8_path = 'resnet18_int8.onnx' quantized_model = quantization.quantize_static(model...
Models quantized by quantize_static or quantize_dynamic API, explained below, with quant_format=QuantFormat.QDQ. Quantization-Aware training (QAT) models converted from Tensorflow or exported from PyTorch. Quantized models converted from TFLite and other frameworks. Model optimization performs certain ope...
quantized_model = quantization.quantize_static(model_input=model_prep_path, model_output=model_int8_path, calibration_data_reader=qdr, extra_options=q_static_opts) 根据ONNX 运行时存储库, 如果模型以 GPU/TRT 为目标,则需要对称激活和权重。如果模型面向 CPU,建议使用非对称激活和对称权重,以平衡性能和...
from onnxruntime.quantization import Calibrator, QuantizationMode, quantizedynamic, QuantizationGranularity, quantize_static, QuantizationErrorMode, quantize_model, load_calibration_data, quantize_dynamic_asymmetric, quantize_dynamic_symmetric, quantize_static_asymmetric, quantize_static_symmetric, load_...
quantize_name = model_name + ".qdq.onnx" # Quantize the model to optimize it quantize_static(model_name, quantize_name, calibration_data_reader=DataReader(x, x_lengths, scales), quant_format=QuantFormat.QDQ) It seems like the reason behind the error isthis linefrom thecalibrate.pyscript....
Hi, I try static quantization on a basic model with several depthwise layers followed by a final fully-connected layer with 12 outputs. When calling quantize_static method I have this issue regarding the output layer (FC) I can not solve...
quantization import QuantType, quantize_dynamic model_in = sys.argv[1] model_out = sys.argv[2] model_quant_dynamic = quantize_dynamic( model_in, model_out, optimize_model=False, weight_type=QuantType.QUInt8 ) We have been trying static quantization today using broadly similar code...