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：

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。

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 代码解释

这段代码实现了一个多头自注意力机制（Multi-Head Self-Attention）块，可以被用于Transformer等深度学习模型中。

具体而言，输入参数包含一个张量 `input_layer` 和一个整数 `input_channel`，其中 `input_layer` 的形状为 `(batch_size, height, width, input_channel)`。

该函数首先将输入的二维张量 `input_layer` 转化为一个序列，即将形状为 `(height, width, input_channel)` 的张量转化为一个形状为 `(1, height*width, input_channel)` 的张量。

接下来，该函数对序列进行一系列操作，包括位置编码、查询、键、值的卷积以及转置等，最后得到一个形状为 `(1, height*width, input_channel)` 的张量。

最后，该函数将这个序列转化回二维形状，即将形状为 `(1, height*width, input_channel)` 的张量转化为一个形状为 `(height, width, input_channel)` 的张量，并返回该张量作为该函数的输出。

翻译：分割：将Q, K, V向量(x''∈ℝ(H×W)×C)切片为h个低阶嵌入{x_1,x_2,∙∙∙,x_h }, 其中每个单维子空间xi∈ℝh×(H×W)×(C/h)表示MHSA中的一个头；

Segmentation: Slice the Q, K, and V vectors (x'' ∈ ℝ(H×W)×C) into h low-order embeddings {x1, x2, ..., xh}, where each single subspace xi ∈ ℝh×(H×W)×(C/h) represents a head in MHSA.