File size: 6,495 Bytes
c0eac48 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 |
import torch
import torch.nn as nn
import numpy as np
import time
import math
import random
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
# from networks.layers import *
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)
# batch_size, dimension and position
# output: (batch_size, dim)
def positional_encoding(batch_size, dim, pos):
assert batch_size == pos.shape[0]
positions_enc = np.array([
[pos[j] / np.power(10000, (i-i%2)/dim) for i in range(dim)]
for j in range(batch_size)
], dtype=np.float32)
positions_enc[:, 0::2] = np.sin(positions_enc[:, 0::2])
positions_enc[:, 1::2] = np.cos(positions_enc[:, 1::2])
return torch.from_numpy(positions_enc).float()
def get_padding_mask(batch_size, seq_len, cap_lens):
cap_lens = cap_lens.data.tolist()
mask_2d = torch.ones((batch_size, seq_len, seq_len), dtype=torch.float32)
for i, cap_len in enumerate(cap_lens):
mask_2d[i, :, :cap_len] = 0
return mask_2d.bool(), 1 - mask_2d[:, :, 0].clone()
def top_k_logits(logits, k):
v, ix = torch.topk(logits, k)
out = logits.clone()
out[out < v[:, [-1]]] = -float('Inf')
return out
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=300):
super(PositionalEncoding, self).__init__()
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
# pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, pos):
return self.pe[pos]
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 MovementConvDecoder(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(MovementConvDecoder, self).__init__()
self.main = nn.Sequential(
nn.ConvTranspose1d(input_size, hidden_size, 4, 2, 1),
# nn.Dropout(0.2, inplace=True),
nn.LeakyReLU(0.2, inplace=True),
nn.ConvTranspose1d(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)
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.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_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)
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) |