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import copy
import torch
import torch.nn as nn
from typing import Optional
def _get_clones(mod, n):
return nn.ModuleList([copy.deepcopy(mod) for _ in range(n)])
class SPOTERTransformerDecoderLayer(nn.TransformerDecoderLayer):
"""
Edited TransformerDecoderLayer implementation omitting the redundant self-attention operation as opposed to the
standard implementation.
"""
def __init__(self, d_model, nhead, dim_feedforward, dropout, activation):
super(SPOTERTransformerDecoderLayer, self).__init__(d_model, nhead, dim_feedforward, dropout, activation)
del self.self_attn
def forward(self, tgt: torch.Tensor, memory: torch.Tensor, tgt_mask: Optional[torch.Tensor] = None,
memory_mask: Optional[torch.Tensor] = None, tgt_key_padding_mask: Optional[torch.Tensor] = None,
memory_key_padding_mask: Optional[torch.Tensor] = None) -> torch.Tensor:
tgt = tgt + self.dropout1(tgt)
tgt = self.norm1(tgt)
tgt2 = self.multihead_attn(tgt, memory, memory, attn_mask=memory_mask,
key_padding_mask=memory_key_padding_mask)[0]
tgt = tgt + self.dropout2(tgt2)
tgt = self.norm2(tgt)
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
tgt = tgt + self.dropout3(tgt2)
tgt = self.norm3(tgt)
return tgt
class SPOTER(nn.Module):
"""
Implementation of the SPOTER (Sign POse-based TransformER) architecture for sign language recognition from sequence
of skeletal data.
"""
def __init__(self, num_classes, hidden_dim=55):
super().__init__()
self.row_embed = nn.Parameter(torch.rand(50, hidden_dim))
self.pos = nn.Parameter(torch.cat([self.row_embed[0].unsqueeze(0).repeat(1, 1, 1)], dim=-1).flatten(0, 1).unsqueeze(0))
self.class_query = nn.Parameter(torch.rand(1, hidden_dim))
self.transformer = nn.Transformer(hidden_dim, 9, 6, 6)
self.linear_class = nn.Linear(hidden_dim, num_classes)
# Deactivate the initial attention decoder mechanism
custom_decoder_layer = SPOTERTransformerDecoderLayer(self.transformer.d_model, self.transformer.nhead, 2048,
0.1, "relu")
self.transformer.decoder.layers = _get_clones(custom_decoder_layer, self.transformer.decoder.num_layers)
def forward(self, inputs):
h = torch.unsqueeze(inputs.flatten(start_dim=1), 1).float()
h = self.transformer(self.pos + h, self.class_query.unsqueeze(0)).transpose(0, 1)
res = self.linear_class(h)
return res
if __name__ == "__main__":
pass
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