从Hub加载模型
概述
对于个人开发者来说，从零开始训练一个较好模型，需要大量的标注完备的数据、足够的计算资源和大量训练调试时间。使得模型训练非常消耗资源，提升了AI开发的门槛，针对以上问题，MindSpore Hub提供了很多训练完成的模型权重文件，可以使得开发者在拥有少量数据的情况下，只需要花费少量训练时间，即可快速训练出一个较好的模型。
本文档从推理验证和迁移学习两种用途，展示使用MindSpore Hub提供的模型，用少量数据快速完成训练得到较好的模型。
用于推理验证
mindspore_hub.load API用于加载预训练模型，可以实现一行代码完成模型的加载。主要的模型加载流程如下：
在MindSpore Hub官网上搜索感兴趣的模型。
例如，想使用GoogleNet对CIFAR-10数据集进行分类，可以在MindSpore Hub官网上使用关键词
GoogleNet进行搜索。页面将会返回与GoogleNet相关的所有模型。进入相关模型页面之后，获得详情页url。使用
url完成模型的加载，示例代码如下：import mindspore_hub as mshub import mindspore from mindspore import context, Tensor, nn, Model from mindspore import dtype as mstype import mindspore.dataset.vision.py_transforms as py_transforms context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=0) model = "mindspore/ascend/0.7/googlenet_v1_cifar10" # Initialize the number of classes based on the pre-trained model. network = mshub.load(model, num_classes=10) network.set_train(False) # ...
完成模型加载后，可以使用MindSpore进行推理，参考推理模型总览。
用于迁移学习
通过mindspore_hub.load完成模型加载后，可以增加一个额外的参数项只加载神经网络的特征提取部分，这样我们就能很容易地在之后增加一些新的层进行迁移学习。当模型开发者将额外的参数（例如 include_top）添加到模型构造中时，可以在模型的详情页中找到这个功能。include_top取值为True或者False，表示是否保留顶层的全连接网络。*
下面我们以MobileNetV2为例，说明如何加载一个基于OpenImage的预训练模型，并在特定的子任务数据集上进行迁移学习（重训练）。主要的步骤如下：
在MindSpore Hub官网上搜索感兴趣的模型，并从网站上获取特定的
url。使用
url进行MindSpore Hub模型的加载，注意：include_top参数需要模型开发者提供。import os import mindspore_hub as mshub import mindspore from mindspore import context, Tensor, nn from mindspore.nn import Momentum from mindspore.train.serialization import save_checkpoint, load_checkpoint,load_param_into_net from mindspore import ops import mindspore.dataset as ds import mindspore.dataset.transforms.c_transforms as C2 import mindspore.dataset.vision.c_transforms as C from mindspore import dtype as mstype from mindspore import Model context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=0) model = "mindspore/ascend/1.0/mobilenetv2_v1.0_openimage" network = mshub.load(model, num_classes=500, include_top=False, activation="Sigmoid") network.set_train(False)
在现有模型结构基础上，增加一个与新任务相关的分类层。
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) classification_layer.set_train(True) train_network = nn.SequentialCell([network, reducemean_flatten, classification_layer])
定义数据集加载函数。
如下所示，进行微调任务的数据集为CIFAR-10，注意此处需要下载二进制版本(
binary version)的数据。下载解压后可以通过如下所示代码加载和处理数据。dataset_path是数据集的保存路径，由用户给定。def create_cifar10dataset(dataset_path, batch_size, do_train): if do_train: usage, shuffle = "train", True else: usage, shuffle = "test", False data_set = ds.Cifar10Dataset(dataset_dir=dataset_path, usage=usage, shuffle=True) # define map operations trans = [C.Resize((256, 256))] if do_train: trans += [ C.RandomHorizontalFlip(prob=0.5), ] trans += [ C.Rescale(1.0 / 255.0, 0.0), C.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5]), C.HWC2CHW() ] type_cast_op = C2.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, do_train=True)
为模型训练选择损失函数、优化器和学习率。
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 else: lr = lr_init * 0.001 lr_each_step.append(lr) 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)
开始重训练。
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'): os.mkdir('ckpt') ckpt_path = f"./ckpt/cifar10_finetune_epoch{epoch}.ckpt" save_checkpoint(train_network, ckpt_path)
在测试集上测试模型精度。
model = "mindspore/ascend/1.0/mobilenetv2_v1.0_openimage" network = mshub.load(model, num_classes=500, pretrained=True, include_top=False, activation="Sigmoid") network.set_train(False) reducemean_flatten = ReduceMeanFlatten() classification_layer = nn.Dense(last_channel, num_classes) classification_layer.set_train(False) 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': nn.Loss(), 'Top1-Acc': nn.Top1CategoricalAccuracy(), 'Top5-Acc': nn.Top5CategoricalAccuracy()} model = Model(network, loss_fn=loss, optimizer=None, metrics=eval_metrics) metrics = model.eval(eval_dataset) print("metric: ", metrics)