model_int8_path = 'resnet18_int8.onnx' 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 ...
使用onnxruntime 对 onnx 模型进行 PTQ 动态量化 import time import onnxruntime from onnxruntime.quantization import QuantFormat, QuantType, quantize_static, quantize_dynamic import numpy as np # 动态量化 quantized_model = quantize_dynamic( model_input='fsrcnn_sim.onnx', model_output='fsrcnn_dy...
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_input=model_prep_path, mode...
torch.onnx.errors.SymbolicValueError: ONNX symbolic expected the output of `%z0_p2 : Tensor = onnx::Slice(%10534, %10537, %10538, %10536, %10540), scope: MODEL_NAME:: # model_path:149:16 ` to be a quantized tensor. Is this likely due to missing support for quantized `onnx::S...
export_ppq_graph(graph=quantized, platform=PLATFORM, graph_save_to='Output/QDQ.onnx', config_save_to='Output/QDQ.json') 如果PLATFORM = TargetPlatform.QNN_DSP_INT8, quantize_torch_model.py会export出一个 .json 和 .onnx(看起来和fp32原模型一样);如果PLATFORM = TargetPlatform.ONNXRUNTIME,会...
def forward(self, x): x = self.fc(x) return x # create a model instance model_fp32 = M() # create a quantized model instance model_int8 = torch.quantization.quantize_dynamic( model_fp32, # the original model {torch.nn.Linear}, # a set of layers to dynamically quantize ...
ONNX 是目前模型部署中最重要的中间表示之一。学懂了 ONNX 的技术细节，就能规避大量的模型部署问题。
上一步中获得的 model 为 openvino.runtime.Model 类型,可以直接被 NNCF 工具加载 calibration_dataset = nncf.Dataset(data_source, transform_fn)# Quantize the model. By specifying model_type, we specify additionaltransformer patterns in the model.quantized_model = nncf.quantize(model, calibration_dataset...
🏆模型转换:支持Caffe、TensorFlow、TensorFlow Lite、ONNX、DarkNet、PyTorch等模型转为RKNN模型,并支持 RKNN 模型导入导出,RKNN 模型能够在 Rockchip NPU 平台上加载使用。 🎽量 化功能 : 支 持将 浮点模型量化为定点模型 , 目前支持的量化方法为非对称量化 , 并支持混合 量化 功能。asymmetric_quantized-16 ...
stripped_model = nncf.strip(quantized_model) ONNX import onnx import nncf import torch from torchvision import datasets # Instantiate your uncompressed model onnx_model = onnx.load_model("/model_path") # Provide validation part of the dataset to collect statistics needed for the compression al...