summary[m_key]["nb_params"]=paramsif(notisinstance(module,nn.Sequential)andnotisinstance(module,nn.ModuleList)andnot(module==model)):hooks.append(module.register_forward_hook(hook))# multiple inputs to the network; 元组就是单输入,数组就是多输入ifisinstance(input_size,tuple):input_size=[input_...
# multiple inputs to the network ifisinstance(input_size,tuple): input_size=[input_size] # batch_size of 2 for batchnorm x=[torch.rand(2,*in_size).type(dtype).to(device=device) forin_size,dtypeinzip(input_size,dtypes)] # create properties ...
Improved visualization tool oftorchsummary. Here, it visualizes kernel size, output shape, # params, and Mult-Adds. Also the torchsummaryX can handle RNN, Recursive NN, or model with multiple inputs. Usage pip install torchsummaryXand
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nn.DataParallel, Model], name: str, use_gpu: bool, *input_shape): if isinstance(model, torch.nn.Module): proto_model: torch.nn.Module = model num_params: int = self._count_params(proto_model) elif isinstance(model, torch.nn.DataParallel): proto_model: torch.nn.Module = model.module...
Also the torchsummaryX can handle RNN, Recursive NN, or model with multiple inputs.Usagepip install torchsummaryX andfrom torchsummaryX import summary summary(your_model, torch.zeros((1, 3, 224, 224)))Args:model (Module): Model to summarize x (Tensor): Input tensor of the model with ...
Multiple Inputs w/ Different Data Types classMultipleInputNetDifferentDtypes(nn.Module):def__init__(self):super().__init__()self.fc1a=nn.Linear(300,50)self.fc1b=nn.Linear(50,10)self.fc2a=nn.Linear(300,50)self.fc2b=nn.Linear(50,10)defforward(self,x1,x2):x1=F.relu(self.fc1a(x1...
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Improved visualization tool oftorchsummary. Here, it visualizes kernel size, output shape, # params, and Mult-Adds. Also the torchsummaryX can handle RNN, Recursive NN, or model with multiple inputs. Usage pip install torchsummaryXand
Improved visualization tool oftorchsummary. Here, it visualizes kernel size, output shape, # params, and Mult-Adds. Also the torchsummaryX can handle RNN, Recursive NN, or model with multiple inputs. Usage pip install torchsummaryXand