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import torch
import torch.nn as nn
import torch.nn.functional as F
class Embed(nn.Module):
def __init__(self, input_dim, output_dim, normalize_input=False, event_level=False, activation='gelu'):
super().__init__()
self.input_bn = nn.BatchNorm1d(input_dim) if normalize_input else None
self.fc1 = nn.Linear(input_dim, output_dim)
self.fc2 = nn.Linear(output_dim, output_dim)
self.fc3 = nn.Linear(output_dim, output_dim)
self.event_level = event_level
def forward(self, x):
if self.input_bn is not None:
# x: (batch, embed_dim, seq_len)
x = self.input_bn(x)
if not self.event_level:
x = x.permute(2, 0, 1).contiguous()
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
return x
class AttBlock(nn.Module):
def __init__(self, embed_dims, linear_dims1, linear_dims2, num_heads=8, activation='relu'):
super(AttBlock, self).__init__()
self.layer_norm1 = nn.LayerNorm(embed_dims)
self.multihead_attention = nn.MultiheadAttention(embed_dims, num_heads)
self.layer_norm2 = nn.LayerNorm(embed_dims)
self.linear1 = nn.Linear(embed_dims, linear_dims1)
self.activation = nn.ReLU() if activation == 'relu' else nn.GELU()
self.layer_norm3 = nn.LayerNorm(linear_dims1)
self.linear2 = nn.Linear(linear_dims1, linear_dims2)
def forward(self, x, padding_mask=None):
# Layer normalization 1
x = self.layer_norm1(x)
if padding_mask is not None:
# Assuming mask is 0 for non-padded and 1 for padded elements,
# convert it to a boolean tensor with `True` for padded locations.
padding_mask = padding_mask.bool()
x_att, attention = self.multihead_attention(x, x, x, key_padding_mask=padding_mask, need_weights=True, average_attn_weights=True)
# Skip connection
x = x + x_att # Skip connection
# Layer normalization 2
x = self.layer_norm2(x)
# Linear layer and activation
x_linear1 = self.activation(self.linear1(x))
# Skip connection for the first linear layer
x = x + x_linear1
# Layer normalization 3
x = self.layer_norm3(x_linear1)
# Linear layer with specified output dimensions
x_linear2 = self.linear2(x)
# Skip connection for the second linear layer
x = x + x_linear2
return x, attention
class ClassBlock(nn.Module):
def __init__(self, embed_dims, linear_dims1, linear_dims2, num_heads=8, activation='relu'):
super(ClassBlock, self).__init__()
self.layer_norm1 = nn.LayerNorm(embed_dims)
self.multihead_attention = nn.MultiheadAttention(embed_dims, num_heads)
self.layer_norm2 = nn.LayerNorm(embed_dims)
self.linear1 = nn.Linear(embed_dims, linear_dims1)
self.activation = nn.ReLU() if activation == 'relu' else nn.GELU()
self.layer_norm3 = nn.LayerNorm(linear_dims1)
self.linear2 = nn.Linear(linear_dims1, linear_dims2)
def forward(self, x, class_token, padding_mask=None):
# Concatenate the class token to the input sequence along the sequence length dimension
x = torch.cat((class_token, x), dim=0) # (seq_len+1, batch, embed_dim)
# Layer normalization 1
x = self.layer_norm1(x)
# Multihead Attention
if padding_mask is not None:
# Ensure mask has the correct shape for attention
padding_mask = torch.cat((torch.zeros_like(padding_mask[:, :1]), padding_mask), dim=1)
padding_mask = padding_mask.bool()
x_att, attention = self.multihead_attention(class_token, x, x, key_padding_mask=padding_mask, need_weights=True, average_attn_weights=False)
# Layer normalization 2
x = self.layer_norm2(x_att)
x = class_token + x # Skip connection
# Linear layer and activation
x_linear1 = self.activation(self.linear1(x))
# Layer normalization 3
x_linear1 = self.layer_norm3(x_linear1)
# Linear layer with specified output dimensions
x_linear2 = self.linear2(x_linear1 )
# Skip connection for the second linear layer
x = x + x_linear2
return x, attention
class MLPHead(nn.Module):
def __init__(self, input_dim, hidden_dim1, hidden_dim2, output_dim):
super(MLPHead, self).__init__()
self.fc1 = nn.Linear(input_dim, hidden_dim1)
self.fc2 = nn.Linear(hidden_dim1, hidden_dim2)
self.fc3 = nn.Linear(hidden_dim2, output_dim)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
class AnalysisObjectTransformer(nn.Module):
def __init__(self, input_dim_obj, input_dim_event, embed_dims, linear_dims1, linear_dims2, mlp_hidden_1, mlp_hidden_2, num_heads=8):
super(AnalysisObjectTransformer, self).__init__()
self.embed_dims = embed_dims
# Embedding layer (assumed to be external)
self.embedding_layer = Embed(input_dim_obj, embed_dims)
self.embedding_layer_event_level = Embed(input_dim_event, embed_dims, event_level=True)
# Three blocks of self-attention
self.block1 = AttBlock(embed_dims, linear_dims1, linear_dims1, num_heads)
self.block2 = AttBlock(linear_dims1, linear_dims1, linear_dims1, num_heads)
self.block3 = AttBlock(linear_dims1, linear_dims2, linear_dims2, num_heads)
self.block5 = ClassBlock(linear_dims2, linear_dims1, linear_dims2, num_heads)
self.block6 = ClassBlock(linear_dims2, linear_dims1, linear_dims2, num_heads)
self.block7 = ClassBlock(linear_dims2, linear_dims1, linear_dims2, num_heads)
# Output linear layer and sigmoid activation
self.mlp = MLPHead(embed_dims + input_dim_event, mlp_hidden_1, mlp_hidden_2, output_dim=1)
self.sigmoid = nn.Sigmoid()
def forward(self, x, event_level, mask=None):
cls_token = nn.Parameter(torch.zeros(1, 1, self.embed_dims), requires_grad=True)
cls_token = nn.init.trunc_normal_(cls_token, std=.02)
# Embedding layer
x = self.embedding_layer(x)
x = x.permute(1, 0, 2)
attention_weights = []
# Three blocks of self-attention
x, attention = self.block1(x, padding_mask=mask)
attention_weights.append(attention)
x, attention = self.block2(x, padding_mask=mask)
attention_weights.append(attention)
x, attention = self.block3(x, padding_mask=mask)
attention_weights.append(attention)
cls_tokens = cls_token.expand(1, x.size(1), -1) # (1, N, C)
cls_tokens, attention = self.block5(x, cls_tokens, padding_mask=mask)
cls_tokens, attention = self.block6(x, cls_tokens, padding_mask=mask)
cls_tokens, attention = self.block7(x, cls_tokens, padding_mask=mask)
x = torch.cat((cls_tokens.squeeze(0), event_level), dim=-1)
x = self.mlp(x)
output_probabilities = self.sigmoid(x)
return output_probabilities, attention_weights
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