Online Inference with Checkpoint

Ascend Inference Application

Use the model.eval interface for model validation

Local Storage

When the pre-trained models are saved in local, the steps of performing inference on validation dataset are as follows: firstly creating a model, then loading the model and parameters using load_checkpoint and load_param_into_net in mindspore module, and finally performing inference on the validation dataset once being created. The method of processing the validation dataset is the same as that of the training dataset.

network = LeNet5(cfg.num_classes)
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})

print("============== Starting Testing ==============")
param_dict = load_checkpoint(args.ckpt_path)
load_param_into_net(network, param_dict)
dataset = create_dataset(os.path.join(args.data_path, "test"),
                            cfg.batch_size,
                            1)
acc = model.eval(dataset, dataset_sink_mode=args.dataset_sink_mode)
print("============== {} ==============".format(acc))

In the preceding information:
model.eval is an API for model validation. For details about the API, see https://www.mindspore.cn/docs/api/en/r1.5/api_python/mindspore.html#mindspore.Model.eval.

Inference sample code: https://gitee.com/mindspore/models/blob/r1.5/official/cv/lenet/eval.py.

Remote Storage

When the pre-trained models are saved remotely, the steps of performing inference on the validation dataset are as follows: firstly determining which model to be used, then loading the model and parameters using mindspore_hub.load, and finally performing inference on the validation dataset once being created. The method of processing the validation dataset is the same as that of the training dataset.

model_uid = "mindspore/ascend/0.7/googlenet_v1_cifar10"  # using GoogleNet as an example.
network = mindspore_hub.load(model_uid, num_classes=10)
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})

print("============== Starting Testing ==============")
dataset = create_dataset(os.path.join(args.data_path, "test"),
                            cfg.batch_size,
                            1)
acc = model.eval(dataset, dataset_sink_mode=args.dataset_sink_mode)
print("============== {} ==============".format(acc))

In the preceding information:

mindspore_hub.load is an API for loading model parameters. Please check the details in https://www.mindspore.cn/hub/api/en/r1.5/index.html#module-mindspore_hub.

Use the model.predict API to perform inference

model.predict(input_data)

In the preceding information:
model.predict is an API for inference. For details about the API, see https://www.mindspore.cn/docs/api/en/r1.5/api_python/mindspore.html#mindspore.Model.predict.