(x_ft)) print(f'torchscript cpu: {np.mean([timer(script_cell,x_ft) for _ in range(10)])}') # TorchScript gpu version script_cell_gpu = torch.jit.script(model_ft_gpu, (x_ft_gpu)) print(f'
然后我们再用ResNet做一个测试。 importtorchvisionimporttorchfromtimeimportperf_counterimportnumpyasnpdeftimer(f,*args):start=perf_counter()f(*args)return(1000*(perf_counter()-start))# Pytorch cpu versionmodel_ft=torchvision.models.resnet18(pretrained=True)model_ft.eval()x_ft=torch.rand(1,3,22...
native_model = BertModel.from_pretrained("bert-base-uncased") # huggingface的API中,使用torchscript=True参数可以直接加载TorchScript model script_model = BertModel.from_pretrained("bert-base-uncased", torchscript=True) script_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', torchscript=...
jit.trace(model, example) 将Torch Script序列化为文件 使用ScriptModule中的save方法序列化模型: traced_script_module.save("traced_resnet_model.pt") 将模型保存在工作目录下。如果想读取模型,使用torch.jit.load方法读取。 在C++中加载模型 创建example-app.cpp文件,内容如下: #include <torch/script.h> ...
model = tv.models.detection.maskrcnn_resnet50_fpn( pretrained=False, progress=True, num_classes=num_classes, pretrained_backbone=True) im = torch.zeros(1,3, *(1333,800)).to("cpu") model.load_state_dict(torch.load("D:/gaobao_model.pth")) ...
1. Model由之前继承 nn.Model 改为继承torch.jit.ScriptModule 2. forward函数前加@torch.jit.script_method 3. 其他需要调用的函数前加@torch.jit.script 踩过的坑&&解决方法: A. torch script默认函数或方法的参数都是Tensor类型的,如果不是需要说明,不然调用非Tensor参数时会报类型不符的编译错误。
: GenuineIntel CPU family: 6 Model: 151 Model name: 12th Gen Intel(R) Core(TM) i9-12900K Stepping: 2 CPU MHz: 2568.291 CPU max MHz: 6700.0000 CPU min MHz: 800.0000 BogoMIPS: 6374.40 Virtualization: VT-x L1d cache: 384 KiB L1i cache: 256 KiB L2 cache: 10 MiB NUMA node0 CPU(...
importtorchimportioimportjsonclassMyModule(torch.nn.Module):def__init__(self,*args,**kwargs)->None:super().__init__(*args,**kwargs)self.stride=32self.names=['a','b','c']defforward(self,im):x=im.shape[0]returnx+10model=MyModule()im=torch.randn(32,3,224...
🐛 Describe the bug When applying quantization to a CNN - ReLu - Batch Norm model, and converting it to Torchscript format, the inference fails. Reproductible code: import torch.nn as nn import torch from torch.quantization import get_def...
1.如果model中有DataParallel的子模块,或者model中有将tensors转换为numpy arrays,或者调用了opencv的函数等,这种情况下,model不是一个正确的在单个设备上、正确连接的graph,这种情况下,不管是使用torch.jit.script还是torch.jit.trace都不能trace出正确的TorchScript来。