Torch - nn - Save/Load & STATE_DICT

Learnable parameters (i.e. W & B) of an torch.nn.Module model = model.parameters()

state_dict

1. Python dictionary object

2. maps each layer to its parameter tensor.

3. only layers with learnable parameters

4. also in Optimizer objects torch.optim

# Define model
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, "\t", optimizer.state_dict()[var_name])

output

Model's state_dict:
conv1.weight torch.Size([6, 3, 5, 5])
conv1.bias   torch.Size([6])
conv2.weight torch.Size([16, 6, 5, 5])
conv2.bias   torch.Size([16])
fc1.weight   torch.Size([120, 400])
fc1.bias     torch.Size([120])
fc2.weight   torch.Size([84, 120])
fc2.bias     torch.Size([84])
fc3.weight   torch.Size([10, 84])
fc3.bias     torch.Size([10])

Optimizer's state_dict:
state    {}
param_groups     [
    {
        'lr': 0.001, 
        'momentum': 0.9, 
        'dampening': 0, 
        'weight_decay': 0, 
        'nesterov': False, 
        'params': [4675713712, 4675713784, 4675714000, 4675714072, 4675714216, 4675714288, 4675714432, 4675714504, 4675714648, 4675714720]
    }
]

Saving the model’s learned parameters = save state_dict with the torch.save()

Save/Load state_dict (Recommended)

Save in pickle:

torch.save(model.state_dict(), PATH)

Load:

model = TheModelClass(*args, **kwargs)
model.load_state_dict(torch.load(PATH))

model.eval()

model.eval()

call model.eval() to set dropout and batch normalization layers to evaluation mode before running inference.

Drawback of save/load entire model

## Just an example, DON'T USE IT.

## save
# torch.save(model, PATH)

## load
# model = torch.load(PATH)
# model.eval()

• Pickle does not save the model class itself.

• The disadvantage of this approach is that the serialized data is bound to the specific classes and the exact directory structure used when the model is saved.