File size: 2,084 Bytes
eb339cb |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence
class MovementConvEncoder(nn.Module):
def __init__(self, input_size: int, hidden_size: int, output_size: int) -> None:
super(MovementConvEncoder, self).__init__()
self.main = nn.Sequential(
nn.Conv1d(input_size, hidden_size, 4, 2, 1),
nn.Dropout(0.2, inplace=True),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv1d(hidden_size, output_size, 4, 2, 1),
nn.Dropout(0.2, inplace=True),
nn.LeakyReLU(0.2, inplace=True))
self.out_net = nn.Linear(output_size, output_size)
def forward(self, inputs: torch.Tensor) -> torch.Tensor:
inputs = inputs.permute(0, 2, 1)
outputs = self.main(inputs).permute(0, 2, 1)
return self.out_net(outputs)
class MotionEncoderBiGRUCo(nn.Module):
def __init__(self, input_size: int, hidden_size: int, output_size: int) -> None:
super(MotionEncoderBiGRUCo, self).__init__()
self.input_emb = nn.Linear(input_size, hidden_size)
self.gru = nn.GRU(
hidden_size, hidden_size, batch_first=True, bidirectional=True
)
self.output_net = nn.Sequential(
nn.Linear(hidden_size * 2, hidden_size),
nn.LayerNorm(hidden_size),
nn.LeakyReLU(0.2, inplace=True),
nn.Linear(hidden_size, output_size),
)
self.hidden_size = hidden_size
self.hidden = nn.Parameter(
torch.randn((2, 1, self.hidden_size), requires_grad=True)
)
def forward(self, inputs: torch.Tensor, m_lens: torch.Tensor) -> torch.Tensor:
num_samples = inputs.shape[0]
input_embs = self.input_emb(inputs)
hidden = self.hidden.repeat(1, num_samples, 1)
cap_lens = m_lens.data.tolist()
emb = pack_padded_sequence(input_embs, cap_lens, batch_first=True)
gru_seq, gru_last = self.gru(emb, hidden)
gru_last = torch.cat([gru_last[0], gru_last[1]], dim=-1)
return self.output_net(gru_last)
|