self.blocks = nn.ModuleList([ ResnetBlockConv1d(c_dim, hidden_size) for _ in range(num_blocks) ])

这段代码是用 PyTorch 构建一个 ResNet 模型中的 Residual Block，其中输入的维度为 `c_dim`，隐藏层的大小为 `hidden_size`，并且一共构建 `num_blocks` 个 Residual Block。`nn.ModuleList` 是 PyTorch 中的一个模块容器，用于存储一组 PyTorch 模块，这里将 Residual Block 存储在 `self.blocks` 中。在实际使用时，可以通过对 `self.blocks` 进行迭代，来对一组输入数据进行多次 Residual Block 的操作。

相关问题

解读这段代码def __init__(self): super(Transformer, self).__init__() self.embedding = nn.Embedding(n_vocab, n_embd) self.pos_emb = nn.Parameter(torch.zeros(1, block_size, n_embd)) self.blocks = nn.ModuleList([Block(n_embd, n_heads) for _ in range(n_layers)]) self.norm = nn.LayerNorm(n_embd) self.fc = nn.Linear(n_embd, n_vocab)

这段代码是一个Transformer模型的初始化函数。首先，调用父类的初始化函数`super(Transformer, self).__init__()`来初始化Transformer类的父类。然后，定义了几个成员变量：

- `self.embedding`是一个词嵌入层，用于将输入的词索引转换为对应的词向量。
- `self.pos_emb`是位置嵌入层，用于表示输入序列中每个位置的信息。
- `self.blocks`是一个由多个`Block`模块组成的列表，每个`Block`模块都是Transformer模型中的一个编码器块。
- `self.norm`是一个层归一化层，用于对编码器块的输出进行归一化。
- `self.fc`是一个线性层，将编码器块的输出投影到词汇表大小的维度，用于生成最终的预测结果。

这段代码是Transformer模型的一部分，用于初始化模型的各个组件。

import torch import vega import torch.nn as nn class SimpleCnn(nn.Module): """Simple CNN network.""" def __init__(self): """Initialize.""" super(SimpleCnn, self).__init__() self.num_class = 11 # nas best hyperparameter self.blocks = 3 self.channel1 = 32 self.channel2 = 64 self.b1 = nn.Sequential( nn.Conv2d(1, self.channel1, padding=(0, 4), kernel_size=(1, 9), stride=(1, 1)), nn.BatchNorm2d(self.channel1), nn.ReLU(inplace=True) ) self.b2 = nn.Sequential( nn.Conv2d(self.channel1, self.channel2, padding=(0, 1), kernel_size=(2, 3), stride=(1, 2)), nn.BatchNorm2d(self.channel2), nn.ReLU(inplace=True) ) self.blocks = self._blocks(self.channel2, self.channel2) self.fc = nn.Linear(64*self.channel2, self.num_class) def _blocks(self, in_channels, out_channels): blocks = nn.ModuleList([None] * self.blocks) for i in range(self.blocks): blocks[i] = nn.Sequential( nn.Conv2d(in_channels, out_channels, padding=(0,1), kernel_size=(1,3)), nn.BatchNorm2d(out_channels), nn.ReLU(inplace=True), ) in_channels = out_channels return blocks def forward(self, x): """Forward.""" x = self.b1(x) x = self.b2(x) for block in self.blocks: x = block(x) x = x.view(x.size(0), -1) x = self.fc(x) return x if __name__ == "__main__": model = SimpleCnn() model.load_state_dict(torch.load('nas_results/model_12.pth')) model.eval()将这段代码从基于pytorch改为基于mindspore

将这段代码从基于PyTorch改为基于MindSpore，需要进行以下修改：

1. 导入MindSpore相关库：

import mindspore
import mindspore.nn as nn
from mindspore import Tensor

2. 将`nn.Module`改为`nn.Cell`，并且去掉`super`中的`self`参数：

class SimpleCnn(nn.Cell):
    """Simple CNN network."""
    def __init__(self):
        """Initialize."""
        super().__init__()
        ...

3. 将`nn.Sequential`改为`nn.SequentialCell`，并且去掉`inplace`参数：

self.b1 = nn.SequentialCell([
    nn.Conv2d(1, self.channel1, padding=(0, 4), kernel_size=(1, 9), stride=(1, 1)),
    nn.BatchNorm2d(self.channel1),
    nn.ReLU(),
])

4. 将`nn.ModuleList`改为`nn.CellList`：

def _blocks(self, in_channels, out_channels):
    blocks = nn.CellList([None] * self.blocks)
    for i in range(self.blocks):
        blocks[i] = nn.SequentialCell([
            nn.Conv2d(in_channels, out_channels, padding=(0,1), kernel_size=(1,3)),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(),
        ])
        in_channels = out_channels
    return blocks

5. 将`x.size()`改为`x.shape()`：

x = x.view(x.shape()[0], -1)

6. 将PyTorch中的Tensor改为MindSpore中的Tensor：

model.load_state_dict(Tensor.load_npz('nas_results/model_12.ckpt'))

修改后的完整代码如下：

import mindspore
import mindspore.nn as nn
from mindspore import Tensor

class SimpleCnn(nn.Cell):
    """Simple CNN network."""
    def __init__(self):
        """Initialize."""
        super().__init__()
        self.num_class = 11
        # nas best hyperparameter
        self.blocks = 3
        self.channel1 = 32
        self.channel2 = 64
        
        self.b1 = nn.SequentialCell([
            nn.Conv2d(1, self.channel1, padding=(0, 4), kernel_size=(1, 9), stride=(1, 1)),
            nn.BatchNorm2d(self.channel1),
            nn.ReLU(),
        ])
        
        self.b2 = nn.SequentialCell([
            nn.Conv2d(self.channel1, self.channel2, padding=(0, 1), kernel_size=(2, 3), stride=(1, 2)),
            nn.BatchNorm2d(self.channel2),
            nn.ReLU(),
        ])
        
        self.blocks = self._blocks(self.channel2, self.channel2)
        
        self.fc = nn.Dense(64*self.channel2, self.num_class)
        
    def _blocks(self, in_channels, out_channels):
        blocks = nn.CellList([None] * self.blocks)
        for i in range(self.blocks):
            blocks[i] = nn.SequentialCell([
                nn.Conv2d(in_channels, out_channels, padding=(0,1), kernel_size=(1,3)),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(),
            ])
            in_channels = out_channels
        return blocks
        
    def construct(self, x):
        """Forward."""
        x = self.b1(x)
        x = self.b2(x)
        for block in self.blocks:
            x = block(x)
        
        x = x.view(x.shape()[0], -1)
        x = self.fc(x)
        
        return x

if __name__ == "__main__":
    model = SimpleCnn()
    model.load_state_dict(Tensor.load_npz('nas_results/model_12.ckpt'))
    model.eval()