def forward(self, x, mask=None, temporal=False): """ Args: x: input features with shape of (num_windows*B, N, C) mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None """ B_, N, C = x.shape qkv = self.qkv(x).reshape(B_, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4) q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) q = q * self.scale attn = (q @ k.transpose(-2, -1)) if temporal: relative_pos_bias = self.temporal_position_bias_table[self.t_relative_coords].view(self.num_ttokens, self.num_ttokens, -1).permute(2, 0, 1).contiguous() attn = attn + relative_pos_bias.unsqueeze(0) attn = self.softmax(attn) else: relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view( self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1) # Wh*Ww,Wh*Ww,nH relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww attn = attn + relative_position_bias.unsqueeze(0) if mask is not None: nW = mask.shape[0] attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0) attn = attn.view(-1, self.num_heads, N, N) attn = self.softmax(attn) else: attn = self.softmax(attn) attn = self.attn_drop(attn) x = (attn @ v).transpose(1, 2).reshape(B_, N, C) x = self.proj(x) x = self.proj_drop(x) return x

这段代码是一个自注意力机制的前向传播函数，用于计算输入特征的相对于自身的注意力权重，然后将这些权重应用于值向量，最后通过一个投影层输出新的特征表示。其中，输入特征x的形状为(num_windows*B, N, C)，其中num_windows表示窗口数，B表示批次大小，N表示序列长度，C表示每个序列元素的特征维度。函数中涉及的主要操作包括：

- 利用多头注意力机制，将输入特征x分别映射到查询向量q，键向量k和值向量v，并计算它们的点积注意力矩阵attn=(q@k^T)；
- 如果temporal为True，则说明输入特征是时间序列，并且需要考虑时间维度上的相对位置关系，此时会使用一个临时的位置偏置表（temporal_position_bias_table）来计算注意力矩阵；
- 如果temporal为False，则说明输入特征是二维图像，并且需要考虑空间维度上的相对位置关系，此时会使用一个固定的位置偏置表（relative_position_bias_table）来计算注意力矩阵；
- 如果mask不为None，则说明需要对注意力矩阵进行掩码处理，以避免模型在未来时刻预测当前时刻的信息，此时会将掩码矩阵mask与注意力矩阵attn相加，然后再通过softmax函数归一化；
- 最后将注意力权重与值向量做加权平均，并通过一个投影层得到新的特征表示x。在这个过程中，还会进行一些dropout操作以防止过拟合。

相关问题

class TemporalModel(nn.Module): def __init__( self, in_channels, receptive_field, input_shape, start_out_channels=64, extra_in_channels=0, n_spatial_layers_between_temporal_layers=0, use_pyramid_pooling=True): super().__init__() self.receptive_field = receptive_field n_temporal_layers = receptive_field - 1 h, w = input_shape modules = [] block_in_channels = in_channels block_out_channels = start_out_channels for _ in range(n_temporal_layers): if use_pyramid_pooling: use_pyramid_pooling = True pool_sizes = [(2, h, w)] else: use_pyramid_pooling = False pool_sizes = None temporal = TemporalBlock( block_in_channels, block_out_channels, use_pyramid_pooling=use_pyramid_pooling, pool_sizes=pool_sizes, ) spatial = [ Bottleneck3D(block_out_channels, block_out_channels, kernel_size=(1, 3, 3)) for _ in range(n_spatial_layers_between_temporal_layers) ] temporal_spatial_layers = nn.Sequential(temporal, *spatial) modules.extend(temporal_spatial_layers) block_in_channels = block_out_channels block_out_channels += extra_in_channels self.out_channels = block_in_channels self.model = nn.Sequential(*modules) def forward(self, x): # Reshape input tensor to (batch, C, time, H, W) x = x.permute(0, 2, 1, 3, 4) x = self.model(x) x = x.permute(0, 2, 1, 3, 4).contiguous() return x[:, (self.receptive_field - 1):]是如何一步步前向传播的？

首先，输入张量x的形状为(batch_size, in_channels, sequence_length, height, width)。

然后，我们将x的维度从(sequence_length, batch_size, in_channels, height, width)改变为(batch_size, sequence_length, in_channels, height, width)。

接下来，我们将模型应用于输入，并将结果从(batch_size, sequence_length, out_channels, height, width)改变为(batch_size, out_channels, sequence_length, height, width)，使其与输入张量的形状匹配。

最后，我们将输出张量的维度从(batch_size, out_channels, sequence_length, height, width)改变为(batch_size, sequence_length-receptive_field+1, out_channels, height, width)。其中，sequence_length-receptive_field+1是因为我们使用的是膨胀卷积，其输出序列长度会比输入短receptive_field-1，因此我们需要将其调整为对齐原始输入序列的长度。

Input 0 of layer "conv1d" is incompatible with the layer: expected min_ndim=3, found ndim=2. Full shape received: (None, 5)

The error message suggests that the input shape provided to the `Conv1D` layer is incompatible. The `Conv1D` layer expects input tensors with a minimum of 3 dimensions, but the received input tensor has only 2 dimensions.

To fix this issue, you need to reshape your input tensor to have a shape of (batch_size, length, channels). In the case of `Conv1D`, the length dimension corresponds to the temporal dimension of the data, and the channels dimension represents the number of input channels.

Here's an example of how you can reshape your input data to make it compatible with the `Conv1D` layer:

from tensorflow.keras.layers import Conv1D
from tensorflow.keras.models import Sequential

model = Sequential()
model.add(Conv1D(16, kernel_size=3, input_shape=(5, 1)))  # Assuming input has 5 time steps and 1 channel

# Reshape your input data to match the required shape
input_data = input_data.reshape((-1, 5, 1))

# Now you can pass your reshaped input data to the model for further processing
output = model.predict(input_data)

In this example, we assume that your input data has 5 time steps and 1 channel. We reshape the input data using the `reshape` function to have a shape of (-1, 5, 1), where -1 signifies that the batch size can be variable. This reshaped data can then be passed to the `Conv1D` layer without any shape compatibility issues.