如何解决将脚本化的 ScriptModule 转换为 Trace 模块
是否可以将脚本模块转换为使用虚拟输入张量进行跟踪。 用于构建模型和保存脚本模块。
import torch
from torch import nn
from torch import optim
import torch.nn.functional as F
from torchvision import datasets,transforms
import numpy as np
class TheModelClass(nn.Module):
def __init__(self):
super(TheModelClass,self).__init__()
self.conv1 = nn.Conv2d(3,6,5)
self.pool = nn.MaxPool2d(2,2)
self.conv2 = nn.Conv2d(6,16,5)
self.fc1 = nn.Linear(16 * 5 * 5,120)
self.fc2 = nn.Linear(120,84)
self.fc3 = nn.Linear(84,10)
def forward(self,x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1,16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
# Initialize model
model = TheModelClass()
# Initialize optimizer
optimizer = optim.SGD(model.parameters(),lr=0.001,momentum=0.9)
# Print model's state_dict
print("Model's state_dict:")
for param_tensor in model.state_dict():
print(param_tensor,"\t",model.state_dict()[param_tensor].size())
# Print optimizer's state_dict
print("Optimizer's state_dict:")
for var_name in optimizer.state_dict():
print(var_name,optimizer.state_dict()[var_name])
m = torch.jit.script(model)
torch.jit.save(m,"model.pt")
然后为了推理,我在我想要脚本模块跟踪的地方尝试了这段代码。
import torch
model=torch.jit.load('model.pt')
dummy=torch.rand((1,3,224,224))
traced=torch.jit.trace(model,dummy)
引发了以下警告:
UserWarning: The input to trace is already a ScriptModule,tracing it is a no-op. Returning the object as is.
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