Loading the Model from Hub

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For individual developers, training a better model from scratch requires a lot of well-labeled data, sufficient computational resources, and a lot of training and debugging time. It makes model training very resource-consuming and raises the threshold of AI development. To solve the above problems, MindSpore Hub provides a lot of model weight files with completed training, which can enable developers to quickly train a better model with a small amount of data and only a small amount of training time.

This document demonstrates the use of the models provided by MindSpore Hub for both inference verification and migration learning, and shows how to quickly complete training with a small amount of data to get a better model.

For Inference Validation

mindspore_hub.load API is used to load the pre-trained model in a single line of code. The main process of model loading is as follows:

  1. Search the model of interest on MindSpore Hub Website.

    For example, if you aim to perform image classification on CIFAR-10 dataset using GoogleNet, please search on MindSpore Hub Website with the keyword GoogleNet. Then all related models will be returned. Once you enter into the related model page, you can find the Usage. Notices: if the model page doesn’t have Usage, it means that the current model does not support loading with MindSpore Hub temporarily.

  2. Complete the task of loading model according to the Usage , as shown in the example below:

    import mindspore_hub as mshub
    import mindspore
    from mindspore import Tensor, nn, Model, set_context, GRAPH_MODE
    from mindspore import dtype as mstype
    import mindspore.dataset.vision as vision
    model = "mindspore/1.6/googlenet_cifar10"
    # Initialize the number of classes based on the pre-trained model.
    network = mshub.load(model, num_classes=10)
    # ...
  3. After loading the model, you can use MindSpore to do inference. You can refer to Multi-Platform Inference Overview.

For Transfer Training

When loading a model with mindspore_hub.load API, we can add an extra argument to load the feature extraction part of the model only. So we can easily add new layers to perform transfer learning. This feature can be found in the related model page when an extra argument (e.g., include_top) has been integrated into the model construction by the model developer. The value of include_top is True or False, indicating whether to keep the top layer in the fully-connected network.

We use MobileNetV2 as an example to illustrate how to load a model trained on the ImageNet dataset and then perform transfer learning (re-training) on a specific sub-task dataset. The main steps are listed below:

  1. Search the model of interest on MindSpore Hub Website and find the corresponding Usage.

  2. Load the model from MindSpore Hub using the Usage. Note that the parameter include_top is provided by the model developer.

    import os
    import mindspore_hub as mshub
    import mindspore
    from mindspore import Tensor, nn, set_context, GRAPH_MODE, train
    from mindspore.nn import Momentum
    from mindspore import save_checkpoint, load_checkpoint,load_param_into_net
    from mindspore import ops
    import mindspore.dataset as ds
    import mindspore.dataset.transforms as transforms
    import mindspore.dataset.vision as vision
    from mindspore import dtype as mstype
    from mindspore import Model
    set_context(mode=GRAPH_MODE, device_target="Ascend", device_id=0)
    model = "mindspore/1.6/mobilenetv2_imagenet2012"
    network = mshub.load(model, num_classes=500, include_top=False, activation="Sigmoid")
  3. Add a new classification layer into current model architecture.

    class ReduceMeanFlatten(nn.Cell):
          def __init__(self):
             super(ReduceMeanFlatten, self).__init__()
             self.mean = ops.ReduceMean(keep_dims=True)
             self.flatten = nn.Flatten()
          def construct(self, x):
             x = self.mean(x, (2, 3))
             x = self.flatten(x)
             return x
    # Check MindSpore Hub website to conclude that the last output shape is 1280.
    last_channel = 1280
    # The number of classes in target task is 10.
    num_classes = 10
    reducemean_flatten = ReduceMeanFlatten()
    classification_layer = nn.Dense(last_channel, num_classes)
    train_network = nn.SequentialCell([network, reducemean_flatten, classification_layer])
  4. Define dataset_loader.

    As shown below, the new dataset used for fine-tuning is the CIFAR-10. It is noted here we need to download the binary version dataset. After downloading and decompression, the following code can be used for data loading and processing. It is noted the dataset_path is the path to the dataset and should be given by the user.

    def create_cifar10dataset(dataset_path, batch_size, usage='train', shuffle=True):
        data_set = ds.Cifar10Dataset(dataset_dir=dataset_path, usage=usage, shuffle=shuffle)
        # define map operations
        trans = [
            vision.Resize((256, 256)),
            vision.Rescale(1.0 / 255.0, 0.0),
            vision.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]),
        type_cast_op = transforms.TypeCast(mstype.int32)
        data_set = data_set.map(operations=type_cast_op, input_columns="label", num_parallel_workers=8)
        data_set = data_set.map(operations=trans, input_columns="image", num_parallel_workers=8)
        # apply batch operations
        data_set = data_set.batch(batch_size, drop_remainder=True)
        return data_set
    # Create Dataset
    dataset_path = "/path_to_dataset/cifar-10-batches-bin"
    dataset = create_cifar10dataset(dataset_path, batch_size=32, usage='train', shuffle=True)
  5. Define loss, optimizer and learning rate.

    def generate_steps_lr(lr_init, steps_per_epoch, total_epochs):
        total_steps = total_epochs * steps_per_epoch
        decay_epoch_index = [0.3*total_steps, 0.6*total_steps, 0.8*total_steps]
        lr_each_step = []
        for i in range(total_steps):
            if i < decay_epoch_index[0]:
                lr = lr_init
            elif i < decay_epoch_index[1]:
                lr = lr_init * 0.1
            elif i < decay_epoch_index[2]:
                lr = lr_init * 0.01
                lr = lr_init * 0.001
        return lr_each_step
    # Set epoch size
    epoch_size = 60
    # Wrap the backbone network with loss.
    loss_fn = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
    loss_net = nn.WithLossCell(train_network, loss_fn)
    steps_per_epoch = dataset.get_dataset_size()
    lr = generate_steps_lr(lr_init=0.01, steps_per_epoch=steps_per_epoch, total_epochs=epoch_size)
    # Create an optimizer.
    optim = Momentum(filter(lambda x: x.requires_grad, classification_layer.get_parameters()), Tensor(lr, mindspore.float32), 0.9, 4e-5)
    train_net = nn.TrainOneStepCell(loss_net, optim)
  6. Start fine-tuning.

    for epoch in range(epoch_size):
        for i, items in enumerate(dataset):
            data, label = items
            data = mindspore.Tensor(data)
            label = mindspore.Tensor(label)
            loss = train_net(data, label)
            print(f"epoch: {epoch}/{epoch_size}, loss: {loss}")
        # Save the ckpt file for each epoch.
        if not os.path.exists('ckpt'):
        ckpt_path = f"./ckpt/cifar10_finetune_epoch{epoch}.ckpt"
        save_checkpoint(train_network, ckpt_path)
  7. Eval on test set.

    model = "mindspore/1.6/mobilenetv2_imagenet2012"
    network = mshub.load(model, num_classes=500, pretrained=True, include_top=False, activation="Sigmoid")
    reducemean_flatten = ReduceMeanFlatten()
    classification_layer = nn.Dense(last_channel, num_classes)
    softmax = nn.Softmax()
    network = nn.SequentialCell([network, reducemean_flatten, classification_layer, softmax])
    # Load a pre-trained ckpt file.
    ckpt_path = "./ckpt/cifar10_finetune_epoch59.ckpt"
    trained_ckpt = load_checkpoint(ckpt_path)
    load_param_into_net(classification_layer, trained_ckpt)
    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
    # Define loss and create model.
    eval_dataset = create_cifar10dataset(dataset_path, batch_size=32, do_train=False)
    eval_metrics = {'Loss': train.Loss(),
                     'Top1-Acc': train.Top1CategoricalAccuracy(),
                     'Top5-Acc': train.Top5CategoricalAccuracy()}
    model = Model(network, loss_fn=loss, optimizer=None, metrics=eval_metrics)
    metrics = model.eval(eval_dataset)
    print("metric: ", metrics)