def mhsa_block(input_layer, input_channel): # W, H = 25, 25 W, H = int(input_layer.shape[1]), int(input_layer.shape[2]) # From 2-D to Sequence: WxHxd -> WHxd (e.g., 25x25x512 -> 1x625x512) conv = Reshape((1, WH, input_channel))(input_layer) # Position Encoding: 1x625x512 -> 1x625x512 pos_encoding = Conv2D(input_channel, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv) # Element-wise Sum: 1x625x512 conv = Add()([conv, pos_encoding]) # Query: Conv1x1 --> 1x625x512 conv_q = Conv2D(input_channel, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv) # Key: Conv1x1 --> 1x625x512 conv_k = Conv2D(input_channel, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv) # Value: Conv1x1 --> 1x625x512 conv_v = Conv2D(input_channel, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv) # Transposed Key: 1x512x612 conv_k = Permute(dims=(1, 3, 2))(conv_k) # Content-content: Query * Key_T --> 1x625x625 conv = Dot(axes=(3,2))([conv_q, conv_k]) conv = Reshape((1, WH, WH))(conv) # Softmax --> 1x625x625 conv = Softmax()(conv) # Output: Dot(1x625x625, 1x625x512) --> 1x625x512 conv = Dot(axes=(3,2))([conv, conv_v]) # From Sequence to 2-D conv = Reshape((W, H, input_channel))(conv) return conv 是直接运行还是需要调用
时间: 2024-01-14 14:03:46 浏览: 25
这段代码定义了一个名为 mhsa_block 的函数。在函数中,输入参数包括 input_layer 和 input_channel,其中 input_layer 是一个输入层,input_channel 是输入层的通道数。函数的主要作用是实现一个多头自注意力机制(Multi-Head Self-Attention),用于提取输入层的特征。
该函数的输入层 input_layer 是一个二维张量,形状为 (W, H, input_channel),其中 W 和 H 分别表示输入层的宽和高。函数首先将输入层转换为一个序列,形状为 (1, WH, input_channel),其中 WH = W * H。接着,函数通过一个卷积层实现位置编码(Position Encoding),将序列中的每个元素都加上一个位置编码向量。然后,函数将输入序列分别作为查询(Query)、键(Key)和值(Value),通过卷积层将它们映射到一个新的空间。接着,函数计算 Query 和 Key 的点积,并将结果通过 Softmax 函数进行归一化,得到每个位置的权重。最后,函数将权重与 Value 相乘,得到最终的输出。最后,函数将输出重新变形为二维张量,形状与输入层相同。
该函数定义完成后,可以直接调用。
相关问题
def mhsa_block(input_layer, input_channel): # W, H = 25, 25 W, H = int(input_layer.shape[1]), int(input_layer.shape[2]) # From 2-D to Sequence: WxHxd -> W*Hxd (e.g., 25x25x512 -> 1x625x512) conv = Reshape((1, W*H, input_channel))(input_layer) # Position Encoding: 1x625x512 -> 1x625x512 pos_encoding = Conv2D(input_channel, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv) # Element-wise Sum: 1x625x512 conv = Add()([conv, pos_encoding]) # Query: Conv1x1 --> 1x625x512 conv_q = Conv2D(input_channel, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv) # Key: Conv1x1 --> 1x625x512 conv_k = Conv2D(input_channel, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv) # Value: Conv1x1 --> 1x625x512 conv_v = Conv2D(input_channel, 1, activation='relu', padding='same', kernel_initializer='he_normal')(conv) # Transposed Key: 1x512x612 conv_k = Permute(dims=(1, 3, 2))(conv_k) # Content-content: Query * Key_T --> 1x625x625 conv = Dot(axes=(3,2))([conv_q, conv_k]) conv = Reshape((1, W*H, W*H))(conv) # Softmax --> 1x625x625 conv = Softmax()(conv) # Output: Dot(1x625x625, 1x625x512) --> 1x625x512 conv = Dot(axes=(3,2))([conv, conv_v]) # From Sequence to 2-D conv = Reshape((W, H, input_channel))(conv) return conv 定义后如何调用
可以通过以下方式调用该函数:
```python
input_layer = Input(shape=(25, 25, 512))
output_layer = mhsa_block(input_layer, 512)
model = Model(inputs=input_layer, outputs=output_layer)
```
其中 `Input(shape=(25, 25, 512))` 用于定义输入特征图的形状,而 `mhsa_block(input_layer, 512)` 则是将输入特征图通过 MHSA 模块处理得到输出特征图。最后通过 `Model` 函数将输入和输出包装成一个模型,以便进行训练或者预测。
在 '__init__.py' 中找不到引用 'MHSA'
在 '__init__.py' 中找不到引用 'MHSA' 可能是因为 'MHSA' 这个引用没有被正确导入或者没有在 '__init__.py' 文件中注册。请检查一下代码,确保 'MHSA' 这个引用被正确导入并在 '__init__.py' 文件中进行了注册。如果仍然找不到引用,可能需要进一步检查代码逻辑或者查看相关文档以获取更多信息。
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