weight=ao.quantization.observer.default_per_channel_weight_observer ) 然后单独对模型中的某一类型算子操作torch.nn.ConvTranspose2d进行设置,这个qconfig会优先匹配,优先级比整体qconfig高,具体细节可以参考_propagate_qconfig_helper这个函数。 为啥要单独配置torch.nn.ConvTranspose2d,因为torch.fx中默认对torch.nn.Con...
from torch.quantization.quantize_fx import prepare_fx, convert_fx from torch.ao.quantization.fx.graph_module import ObservedGraphModule from torch.quantization import ( get_default_qconfig, ) from torch import optim import os import time def train_model(model, train_loader, test_loader, device): ...
from torch.quantization.quantize_fx import prepare_fx, convert_fx from torch.ao.quantization.fx.graph_module import ObservedGraphModule from torch.quantization import ( get_default_qconfig, ) from torch import optim import os import time def train_model(model, train_loader, test_loader, device): ...
from torch.ao.quantization import get_default_qconfig_mapping from torch.quantization.quantize_fx import prepare_fx, convert_fx import torchvision.models as models import time qconfig_mapping = get_default_qconfig_mapping() qengine = 'x86' torch.backends.quantized.engine = qengine qconfig_mapping =...
convert_fxfromtorch.ao.quantization.fx.graph_moduleimportObservedGraphModulefromtorch.quantizationimportget_default_qconfigfromtorchimportoptimimportosimportonnximportonnxruntimeimportnumpyasnpfromonnxsimimportsimplifyimporttimedefprepare_dataloader(num_workers=8,train_batch_size=128,eval_batch_size=256):train...
importtorch.nnasnnimporttorchfromtorch.quantizationimportget_default_qconfigfromtorch.quantization.quantize_fximportprepare_fx,convert_fxclassMyModel(nn.Module):def__init__(self):super().__init__()self.conv=nn.Conv2d(in_channels=1,out_channels=64,kernel_size=(8,20),stride=(1,1),padding=0...
第一讲中,我将深度学习代码拆解成七步。到前一讲为止,这七步已经讲解完了。但这还远远不够,现在深度学习是大模型为王的时代,都是多张卡训练,有时候甚至需要集群训练模型。并且,训练过程涉及超参数优化。因此…
Tensors and Dynamic neural networks in Python with strong GPU acceleration - pytorch/torch/ao/quantization/quantization_mappings.py at main · pytorch/pytorch
def prepare(self, model: nn.Module, config: List[Dict]) -> None: # activation: use PerChannelNormObserver # use no-op placeholder weight observer model.qconfig = QConfig( activation=PerChannelNormObserver, weight=default_placeholder_observer ) # type: ignore[assignment] torch.ao.quantization....