# 加载模型用于推理或迁移学习

`Linux` `Ascend` `GPU` `CPU` `模型加载` `初级` `中级` `高级`

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## 概述

在模型训练过程中保存在本地的CheckPoint文件，或从[MindSpore Hub](https://www.mindspore.cn/resources/hub/)下载的CheckPoint文件，都可以帮助用户进行推理或迁移学习使用。

以下通过示例来介绍如何通过本地加载或Hub加载模型，用于推理验证和迁移学习。

## 本地加载模型

### 用于推理验证

针对仅推理场景可以使用`load_checkpoint`把参数直接加载到网络中，以便进行后续的推理验证。

示例代码如下：

```python
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)
```

- `load_checkpoint`方法会把参数文件中的网络参数加载到模型中。加载后，网络中的参数就是CheckPoint保存的。
- `eval`方法会验证训练后模型的精度。

### 用于迁移学习

针对任务中断再训练及微调（Fine Tune）场景，可以加载网络参数和优化器参数到模型中。

示例代码如下：

```python
# return a parameter dict for model
param_dict = load_checkpoint("resnet50-2_32.ckpt")
resnet = ResNet50()
opt = Momentum()
# 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)
```

- `load_checkpoint`方法会返回一个参数字典。
- `load_param_into_net`会把参数字典中相应的参数加载到网络或优化器中。

## 从Hub加载模型

### 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：

1. 在[MindSpore Hub官网](https://www.mindspore.cn/resources/hub)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`。

2. 使用`url`完成模型的加载，示例代码如下：

   ```python
   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)

   # ...

   ```

3. 完成模型加载后，可以使用MindSpore进行推理，参考[推理模型总览](https://www.mindspore.cn/tutorial/inference/zh-CN/r1.2/multi_platform_inference.html)。

### 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](https://gitee.com/mindspore/mindspore/tree/r1.0/model_zoo/official/cv/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：

1. 在[MindSpore Hub官网](https://www.mindspore.cn/resources/hub/)上搜索感兴趣的模型，并从网站上获取特定的`url`。

2. 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。

   ```python
   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)
   ```

3. 在现有模型结构基础上，增加一个与新任务相关的分类层。

   ```python
   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])
   ```

4. 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](https://www.cs.toronto.edu/~kriz/cifar.html)，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。

   ```python
   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)
   ```

5. 为模型训练选择损失函数、优化器和学习率。

   ```python
   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)
   ```

6. 开始重训练。

   ```python
   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)
   ```

7. 在测试集上测试模型精度。

   ```python
   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)
   ```