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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) | |