总的来说,fuse_model函数的作用是优化模型结构,提高模型的运行效率和速度,从而使模型更加高效,更加适用于实际生产环境中的应用。 2.2 fuse_model函数的参数: 在PyTorch Module类中,fuse_model函数接受多个参数以实现模型融合的操作。下面是fuse_model函数常用的参数及其作用: 1. modules:融合的模块列表。这个参数接受一...
if len(tmp_fuse_ops) > 1: fuse_ops.append(tmp_fuse_ops) tmp_fuse_ops = [n] elif m.__module__ == 'torch.nn.modules.batchnorm': tmp_fuse_ops.append(n) elif m.__module__ == 'torch.nn.modules.activation' and m._get_name()=='ReLU': tmp_fuse_ops.append(n) if len(tmp_...
Pytorch的静态量化一把包含五个步骤: 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: fuse_modules(model, modules_to_fuse, inplace=False, fuser_func=fuse_known_module...
fuse_modules(model, modules_to_fuse, inplace=False, fuser_func=fuse_known_modules, fuse_custom_config_dict=None) 比如给fuse_modules传递下面的参数就会合并网络中的conv1、bn1、relu1: torch.quantization.fuse_modules(gemfield_model, [['conv1', 'bn1', 'relu1']], inplace=True) 一旦合并成功,那...
算子折叠由torch.quantization.fuse_modules()实现。目前存在的比较遗憾的2点: 算子折叠不能自动完成,需要手工指定。 能折叠的算子组合有限。目前支持的算子组合为:ConV + BN、ConV + BN + ReLU、Conv + ReLU、Linear + ReLU、BN + ReLU。如果尝试折叠ConvTranspose2d、ReLU6等不支持的算子则会报错。 ### # ...
# fuse the activations to preceding layers, where applicable # this needs to be done manually depending on the model architecture model_fp32_fused = torch.quantization.fuse_modules(model_fp32, [['conv','bn','relu']]) # Prepare the model for QAT. This inserts observers and fake_quants in...
add.add(identity, conv2) return output def fuse_model(self): fuse_modules(self, ['conv1', 'relu'], inplace=True) class QuantizableEDSR(EDSR): def __init__(self, *args, **kwargs): super(QuantizableEDSR, self).__init__(*args, **kwargs) self.quant = QuantStub() self.dequant...
model = resnet18(num_classes=num_classes, pretrained=False) model.to(cpu_device) print(model) # Make a copy of the model for layer fusion fused_model = copy.deepcopy(model) model.train() # The model has to be switched to training mode be...
model_fp32_fused=torch.quantization.fuse_modules(model_fp32,[['conv','bn','relu']])# Prepare the modelforQAT.This inserts observers and fake_quantsin# the model that will observe weight and activation tensors during calibration.model_fp32_prepared=torch.quantization.prepare_qat(model_fp32_fu...
model_fp32_fused = torch.quantization.fuse_modules(model_fp32, [['conv', 'relu']]) model_fp32_prepared = torch.quantization.prepare(model_fp32_fused) input_fp32 = torch.randn(4, 1, 4, 4) model_fp32_prepared(input_fp32) model_int8 = torch.quantization.convert(model_fp32_prepared)...