昇思25天学习打卡营第7天|保存与加载

Python语言 AI框架：Mindspore

1.模型构建

class Network(nn.Cell):def __init__(self):super().__init__()self.flatten = nn.Flatten()self.dense_relu_sequential = nn.SequentialCell(nn.Dense(28*28, 512, weight_init="normal", bias_init="zeros"),nn.ReLU(),nn.Dense(512, 512, weight_init="normal", bias_init="zeros"),nn.ReLU(),nn.Dense(512, 10, weight_init="normal", bias_init="zeros"))def construct(self, x):x = self.flatten(x)logits = self.dense_relu_sequential(x)return logits

model = Network()
print(model)

在这里插入图片描述

2.模型保存

mindspore.save_checkpoint(model, "model.ckpt")

3.模型导出-mindir格式

除Checkpoint外，MindSpore提供了云侧（训练）和端侧（推理）统一的中间表示（Intermediate Representation，IR）。可使用export接口直接将模型保存为MindIR。

model = network()
inputs = Tensor(np.ones([1, 1, 28, 28]).astype(np.float32))
mindspore.export(model, inputs, file_name="model", file_format="MINDIR")

4.加载保存模型

要加载模型权重，需要先创建相同模型的实例，然后使用load_checkpoint和load_param_into_net方法加载参数。

model = network()
param_dict = mindspore.load_checkpoint("model.ckpt")
param_not_load, _ = mindspore.load_param_into_net(model, param_dict)
print(param_not_load)  # 正确输出[ ]

5.加载导出模型

nn.GraphCell仅支持图模式。

mindspore.set_context(mode=mindspore.GRAPH_MODE)graph = mindspore.load("model.mindir")
model = nn.GraphCell(graph)
outputs = model(inputs)
print(outputs.shape)