조마조마

Pytorch provides two main ways of doing this:

• The less recommended way is to save the entire model object as follows:

torch.save(model, PATH_TO_MODEL)

And then, the saved model can be later read as follows:

model = torch.load(PATH_TO_MODEL)

Although this approach looks the most straightforward, this can be problematic in some cases. This is because we are not only saving the model parameters, but also the model classes and directory structure used in our source code. If our class signatures or directory structures change later, loading the model will fail in potentially unfixable ways.

• The second and more recommended way is to only save the model parameters as follows:

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

Later, when we need to restore the model, first we instantiate an empty model object and then load the model parameters into that model object as follows:

model = Net()
model.load_state_dict(torch.load(PATH_TO_MODEL)

We will use the morte recommended way to save the model as shown in the following code:

PATH_TO_MODEL = "./convnet.pth"
torch.save(model.state_dict(), PATH_TO_MODEL)

The convnet.pth file is essentially a pickle file containing model parameters.

※ reference: Mastering Pytorch

COLORS = np.random.randint(0, 255, size=(len(classes), 3), dtype="uint8")

# draw loop
	color = [int(c) for c in COLORS[idx]]

※ reference: pytorch computer vision codebook

define an Adam optmizer object with a learning rate of 1e-4:

from torch import optim
opt = optim.Adam(model_resnet18.parameters(), lr=1e-4)

we can read the current value of the learning rate using the following function:

def get_lr(opt):
	for param_group in opt.param_groups:
    	return param_group['lr']
        
current_lr = get_lr(opt)
print('current  lr = {}'.format(current_lr)

define a learning scheduler using the CosineAnnealingLR method:

from torch.optim.lr_scheduler import CosineAnnealingLR
lr_schedular = CosineAnnealingLR(opt, T_max=2, eta_min=1e-5)

※ reference: pytorch computer vision codebook