File size: 3,864 Bytes
4409449 |
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 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
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, hidden_size, output_size):
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)
# self.main.apply(init_weight)
# self.out_net.apply(init_weight)
def forward(self, inputs):
inputs = inputs.permute(0, 2, 1)
outputs = self.main(inputs).permute(0, 2, 1)
# print(outputs.shape)
return self.out_net(outputs)
class MotionEncoderBiGRUCo(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
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.input_emb.apply(init_weight)
# self.output_net.apply(init_weight)
self.hidden_size = hidden_size
self.hidden = nn.Parameter(
torch.randn((2, 1, self.hidden_size), requires_grad=True)
)
# input(batch_size, seq_len, dim)
def forward(self, inputs, m_lens):
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=input_embs, lengths=cap_lens, batch_first=True)
emb = input_embs
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)
class TextEncoderBiGRUCo(nn.Module):
def __init__(self, word_size, pos_size, hidden_size, output_size):
super(TextEncoderBiGRUCo, self).__init__()
self.pos_emb = nn.Linear(pos_size, word_size)
self.input_emb = nn.Linear(word_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.input_emb.apply(init_weight)
# self.pos_emb.apply(init_weight)
# self.output_net.apply(init_weight)
# self.linear2.apply(init_weight)
# self.batch_size = batch_size
self.hidden_size = hidden_size
self.hidden = nn.Parameter(
torch.randn((2, 1, self.hidden_size), requires_grad=True)
)
# input(batch_size, seq_len, dim)
def forward(self, word_embs, pos_onehot, cap_lens):
num_samples = word_embs.shape[0]
pos_embs = self.pos_emb(pos_onehot)
inputs = word_embs + pos_embs
input_embs = self.input_emb(inputs)
hidden = self.hidden.repeat(1, num_samples, 1)
cap_lens = cap_lens.data.tolist()
emb = pack_padded_sequence(input=input_embs, lengths=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)
|