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 ...
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...
# 激活量化类型)print("Calibrated and quantized model saved.")if__name__=="__main__":main()...
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...
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...
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 ...
在这种情况下,所有的权重和激活会在训练的前向和后向传播中“假量化”(fake quantized)。Float值取整为相应的int8,不过,计算仍用浮点数完成,即,会让权重调整“感知到”将在训练期间量化。 AI检测代码解析 qat_model = LeNet5() qat_model.qconfig = torch.quantization.get_default_qat_qconfig('fbgemm') ...
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。
fuse_model:该步骤用来对可以融合的op进行融合,比如Conv与BN的融合、Conv与ReLU的融合、Conv与BN以及ReLU的融合、Linear与BN的融合、Linear与BN以及ReLU的融合。目前Pytorch已经内置的融合code: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 fuse_modules(model,modules_to_fuse,inplace=False,fuser_func=fuse...