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import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers.activations import ACT2FN
class MaskedConv1d(nn.Conv1d):
"""A masked 1-dimensional convolution layer.
Takes the same arguments as torch.nn.Conv1D, except that the padding is set automatically.
Shape:
Input: (N, L, in_channels)
input_mask: (N, L, 1), optional
Output: (N, L, out_channels)
"""
def __init__(
self,
in_channels: int,
out_channels: int,
kernel_size: int,
stride: int = 1,
dilation: int = 1,
groups: int = 1,
bias: bool = True,
):
"""
:param in_channels: input channels
:param out_channels: output channels
:param kernel_size: the kernel width
:param stride: filter shift
:param dilation: dilation factor
:param groups: perform depth-wise convolutions
:param bias: adds learnable bias to output
"""
padding = dilation * (kernel_size - 1) // 2
super().__init__(
in_channels,
out_channels,
kernel_size,
stride=stride,
dilation=dilation,
groups=groups,
bias=bias,
padding=padding,
)
def forward(self, x, input_mask=None):
if input_mask is not None:
x = x * input_mask
return super().forward(x.transpose(1, 2)).transpose(1, 2)
class Attention1dPooling(nn.Module):
def __init__(self, hidden_size):
super().__init__()
self.layer = MaskedConv1d(hidden_size, 1, 1)
def forward(self, x, input_mask=None):
batch_szie = x.shape[0]
attn = self.layer(x)
attn = attn.view(batch_szie, -1)
if input_mask is not None:
attn = attn.masked_fill_(
~input_mask.view(batch_szie, -1).bool(), float("-inf")
)
attn = F.softmax(attn, dim=-1).view(batch_szie, -1, 1)
out = (attn * x).sum(dim=1)
return out
class Attention1dPoolingProjection(nn.Module):
def __init__(self, hidden_size, num_labels, dropout=0.25) -> None:
super(Attention1dPoolingProjection, self).__init__()
self.linear = nn.Linear(hidden_size, hidden_size)
self.dropout = nn.Dropout(dropout)
self.relu = nn.ReLU()
self.final = nn.Linear(hidden_size, num_labels)
def forward(self, x):
x = self.linear(x)
x = self.dropout(x)
x = self.relu(x)
x = self.final(x)
return x
class Attention1dPoolingHead(nn.Module):
"""Outputs of the model with the attention1d"""
def __init__(
self, hidden_size: int, num_labels: int, dropout: float = 0.25
): # [batch x sequence(751) x embedding (1280)] --> [batch x embedding] --> [batch x 1]
super(Attention1dPoolingHead, self).__init__()
self.attention1d = Attention1dPooling(hidden_size)
self.attention1d_projection = Attention1dPoolingProjection(hidden_size, num_labels, dropout)
def forward(self, x, input_mask=None):
x = self.attention1d(x, input_mask=input_mask.unsqueeze(-1))
x = self.attention1d_projection(x)
return x
class MeanPooling(nn.Module):
"""Mean Pooling for sentence-level classification tasks."""
def __init__(self):
super().__init__()
def forward(self, features, input_mask=None):
if input_mask is not None:
# Applying input_mask to zero out masked values
masked_features = features * input_mask.unsqueeze(2)
sum_features = torch.sum(masked_features, dim=1)
mean_pooled_features = sum_features / input_mask.sum(dim=1, keepdim=True)
else:
mean_pooled_features = torch.mean(features, dim=1)
return mean_pooled_features
class MeanPoolingProjection(nn.Module):
"""Mean Pooling with a projection layer for sentence-level classification tasks."""
def __init__(self, hidden_size, num_labels, dropout=0.25):
super().__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.dropout = nn.Dropout(dropout)
self.out_proj = nn.Linear(hidden_size, num_labels)
def forward(self, mean_pooled_features):
x = self.dropout(mean_pooled_features)
x = self.dense(x)
x = ACT2FN['gelu'](x)
x = self.dropout(x)
x = self.out_proj(x)
return x
class MeanPoolingHead(nn.Module):
"""Mean Pooling Head for sentence-level classification tasks."""
def __init__(self, hidden_size, num_labels, dropout=0.25):
super().__init__()
self.mean_pooling = MeanPooling()
self.mean_pooling_projection = MeanPoolingProjection(hidden_size, num_labels, dropout)
def forward(self, features, input_mask=None):
mean_pooling_features = self.mean_pooling(features, input_mask=input_mask)
x = self.mean_pooling_projection(mean_pooling_features)
return x
class LightAttentionPoolingHead(nn.Module):
def __init__(self, hidden_size=1280, num_labels=11, dropout=0.25, kernel_size=9, conv_dropout: float = 0.25):
super(LightAttentionPoolingHead, self).__init__()
self.feature_convolution = nn.Conv1d(hidden_size, hidden_size, kernel_size, stride=1,
padding=kernel_size // 2)
self.attention_convolution = nn.Conv1d(hidden_size, hidden_size, kernel_size, stride=1,
padding=kernel_size // 2)
self.softmax = nn.Softmax(dim=-1)
self.dropout = nn.Dropout(conv_dropout)
self.linear = nn.Sequential(
nn.Linear(2 * hidden_size, 32),
nn.Dropout(dropout),
nn.ReLU(),
nn.BatchNorm1d(32)
)
self.output = nn.Linear(32, num_labels)
def forward(self, x: torch.Tensor, mask, **kwargs) -> torch.Tensor:
"""
Args:
x: [batch_size, sequence_length, hidden_size] embedding tensor that should be classified
mask: [batch_size, sequence_length] mask corresponding to the zero padding used for the shorter sequecnes in the batch. All values corresponding to padding are False and the rest is True.
Returns:
classification: [batch_size,num_labels] tensor with logits
"""
x = x.permute(0, 2, 1) # [batch_size, hidden_size, sequence_length]
o = self.feature_convolution(x) # [batch_size, hidden_size, sequence_length]
o = self.dropout(o) # [batch_gsize, hidden_size, sequence_length]
attention = self.attention_convolution(x) # [batch_size, hidden_size, sequence_length]
# mask out the padding to which we do not want to pay any attention (we have the padding because the sequences have different lenghts).
# This padding is added by the dataloader when using the padded_permuted_collate function in utils/general.py
attention = attention.masked_fill(mask[:, None, :] == False, -1e9)
# code used for extracting embeddings for UMAP visualizations
# extraction = torch.sum(x * self.softmax(attention), dim=-1)
# extraction = self.id0(extraction)
o1 = torch.sum(o * self.softmax(attention), dim=-1) # [batchsize, hidden_size]
o2, _ = torch.max(o, dim=-1) # [batchsize, hidden_size]
o = torch.cat([o1, o2], dim=-1) # [batchsize, 2*hidden_size]
o = self.linear(o) # [batchsize, 32]
return self.output(o) # [batchsize, num_labels] |