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T4
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import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence
def init_weight(m):
if isinstance(m, nn.Conv1d) or isinstance(m, nn.Linear) or isinstance(m, nn.ConvTranspose1d):
nn.init.xavier_normal_(m.weight)
# m.bias.data.fill_(0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
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 TextEncoderBiGRUCo(nn.Module):
def __init__(self, word_size, pos_size, hidden_size, output_size, device):
super(TextEncoderBiGRUCo, self).__init__()
self.device = device
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.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_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)
class MotionEncoderBiGRUCo(nn.Module):
def __init__(self, input_size, hidden_size, output_size, device):
super(MotionEncoderBiGRUCo, self).__init__()
self.device = device
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_embs, cap_lens, batch_first=True, enforce_sorted=False)
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)
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