Loading a Model for Inference and Transfer Learning

Linux Ascend GPU CPU Model Loading Beginner Intermediate Expert

Overview

CheckPoints which are saved locally during model training, they are used for inference and transfer training.

The following uses examples to describe how to load models from local.

Loading the local Model

After saving CheckPoint files, you can load parameters.

For Inference Validation

In inference-only scenarios, use load_checkpoint to directly load parameters to the network for subsequent inference validation.

The sample code is as follows:

resnet = ResNet50()
load_checkpoint("resnet50-2_32.ckpt", net=resnet)
dateset_eval = create_dataset(os.path.join(mnist_path, "test"), 32, 1) # define the test dataset
loss = CrossEntropyLoss()
model = Model(resnet, loss, metrics={"accuracy"})
acc = model.eval(dataset_eval)

The load_checkpoint method loads network parameters in the parameter file to the model. After the loading, parameters in the network are those saved in CheckPoints. The eval method validates the accuracy of the trained model.

For Transfer Training

In the retraining and fine-tuning scenarios for task interruption, you can load network parameters and optimizer parameters to the model.

The sample code is as follows:

# return a parameter dict for model
param_dict = load_checkpoint("resnet50-2_32.ckpt")
resnet = ResNet50()
opt = Momentum(resnet.trainable_params(), 0.01, 0.9)
# load the parameter into net
load_param_into_net(resnet, param_dict)
# load the parameter into optimizer
load_param_into_net(opt, param_dict)
loss = SoftmaxCrossEntropyWithLogits()
model = Model(resnet, loss, opt)
model.train(epoch, dataset)

The load_checkpoint method returns a parameter dictionary and then the load_param_into_net method loads parameters in the parameter dictionary to the network or optimizer.