Fuse the model in place rather manually.fused_model=torch.quantization.fuse_modules(fused_model, [["conv1","bn1","relu"]],inplace=True)formodule_name,moduleinfused_model.named_children():if"layer"inmodule_name:forbasic_block_name,basic_blockinmodule.named_children():torch.quantization.fuse_...
为此,PTSQ会对特定的module和activation进行混合(fuse),如以下代码: model_fp32_fused=torch.quantization.fuse_modules(model_fp32,[['linear','relu']])print(f'fused model: {model_fp32_fused}')'''fused model: M((quant): QuantStub()(linear): LinearReLU((0): Linear(in_features=1, out_feat...
model_fp32_fused = torch.quantization.fuse_modules(model_fp32, [['conv', 'relu']]) 情形二:将conv、bn和relu相融合 bn指:self.bn model_fp32_fused = torch.quantization.fuse_modules(model_fp32, [['conv', 'bn', 'relu']])
📚 The doc issue For files __init__.py _correct_bias.py _equalize.py _learnable_fake_quantize.py backend_config experimental fake_quantize.py fuse_modules.py fuser_method_mappings.py Correct the following __init__.py:1 at module level: D1...
Then, the optimizer fuses layers to create quantized operations that operate on INT8 inputs and use INT8 math pipelines. For example,QuantizeLayercan fuse withConvolutionLayer. Finally, the TensorRT auto-tuner optimizer searches for the fastest implementation of each layer that als...
# 步骤1:fuse BN model = fuse(model) # 步骤2:寻找适合equalize的卷积对 paired_modules_list = _find_module_pairs(model) name_to_module = {} name_set = {nameforpairinpaired_modules_listfornameinpair} forname, moduleinmodel.named_modules(): ...
# Next, we'll "fuse modules"; this can both make the model faster by saving on memory access # while also improving numerical accuracy. While this can be used with any model, this is # especially common with quantized models. print('\n Inverted Residual Block: Before fusion \n\n', fl...
To detect ellipses in the OC and OD regions, we use two modules, one of which focuses the OD region on the OC. On the REFUGE dataset, the suggested technique obtains a CDR of 0.047. For the mutual segmentation of optic disk (OD) and optic cup (OC), Munazza et al. [333] proposed...
二、量化模型 1、 模型融合 model_fuse=torch.quantization.fuse_modules(net_model,modules_to_fuse=[['conv','relu']],inplace=False)model_fuse---NetModule((conv):ConvReLU2d((0):Conv2d(3,6,kernel_size=(3,3),stride=(1,1))(1):ReLU())(relu):Identity()(flatten):Flatten(start_dim=1,...
模块融合:fuse_modules 加载量化配置(和训练后量化不同):这里使用torch.quantization.get_default_qat_qconfig('fbgemm') 准备量化模型(和训练后量化不同):插入观测:torch.quantization.prepare_qat(qnet, inplace=True) 模型校准:train(qnet, trainloader, cuda=False) 模型转换:torch.quantization.convert(qnet,...