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import torch
import torch.nn as nn
from torch.distributions import MultivariateNormal
import math
import numpy as np
from helper import gaussian_2d
from config.GlobalVariables import *
class SynthesisNetwork(nn.Module):
def __init__(self, weight_dim=512, num_layers=3, scale_sd=1, clamp_mdn=0, sentence_loss=True, word_loss=True, segment_loss=True, TYPE_A=True, TYPE_B=True, TYPE_C=True, TYPE_D=True, ORIGINAL=True, REC=True):
super(SynthesisNetwork, self).__init__()
self.num_mixtures = 20
self.num_layers = num_layers
self.weight_dim = weight_dim
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.sentence_loss = sentence_loss
self.word_loss = word_loss
self.segment_loss = segment_loss
self.ORIGINAL = ORIGINAL
self.TYPE_A = TYPE_A
self.TYPE_B = TYPE_B
self.TYPE_C = TYPE_C
self.TYPE_D = TYPE_D
self.REC = REC
self.magic_lstm = nn.LSTM(self.weight_dim, self.weight_dim, batch_first=True, num_layers=self.num_layers)
self.char_vec_fc_1 = nn.Linear(len(CHARACTERS), self.weight_dim)
self.char_vec_relu_1 = nn.LeakyReLU(negative_slope=0.1)
self.char_lstm_1 = nn.LSTM(self.weight_dim, self.weight_dim, batch_first=True, num_layers=self.num_layers)
self.char_vec_fc2_1 = nn.Linear(self.weight_dim, self.weight_dim * self.weight_dim)
# inference
self.inf_state_fc1 = nn.Linear(3, self.weight_dim)
self.inf_state_relu = nn.LeakyReLU(negative_slope=0.1)
self.inf_state_lstm = nn.LSTM(self.weight_dim, self.weight_dim, batch_first=True, num_layers=self.num_layers)
self.W_lstm = nn.LSTM(self.weight_dim, self.weight_dim, batch_first=True, num_layers=self.num_layers)
# generation
self.gen_state_fc1 = nn.Linear(3, self.weight_dim)
self.gen_state_relu = nn.LeakyReLU(negative_slope=0.1)
self.gen_state_lstm1 = nn.LSTM(self.weight_dim, self.weight_dim, batch_first=True, num_layers=self.num_layers)
self.gen_state_lstm2 = nn.LSTM(self.weight_dim * 2, self.weight_dim * 2, batch_first=True, num_layers=self.num_layers)
self.gen_state_fc2 = nn.Linear(self.weight_dim * 2, self.num_mixtures * 6 + 1)
self.term_fc1 = nn.Linear(self.weight_dim * 2, self.weight_dim)
self.term_relu1 = nn.LeakyReLU(negative_slope=0.1)
self.term_fc2 = nn.Linear(self.weight_dim, self.weight_dim)
self.term_relu2 = nn.LeakyReLU(negative_slope=0.1)
self.term_fc3 = nn.Linear(self.weight_dim, 1)
self.term_sigmoid = nn.Sigmoid()
self.mdn_sigmoid = nn.Sigmoid()
self.mdn_tanh = nn.Tanh()
self.mdn_softmax = nn.Softmax(dim=1)
self.scale_sd = scale_sd # how much to scale the standard deviation of the gaussians
self.clamp_mdn = clamp_mdn # total percent of disrubution to allow sampling from
self.mdn_bce_loss = nn.BCEWithLogitsLoss()
self.term_bce_loss = nn.BCEWithLogitsLoss()
def forward(self, inputs):
[sentence_level_stroke_in, sentence_level_stroke_out, sentence_level_stroke_length, sentence_level_term, sentence_level_char, sentence_level_char_length, word_level_stroke_in, word_level_stroke_out, word_level_stroke_length, word_level_term, word_level_char, word_level_char_length, segment_level_stroke_in, segment_level_stroke_out, segment_level_stroke_length, segment_level_term, segment_level_char, segment_level_char_length] = inputs
ALL_sentence_W_consistency_loss = []
ALL_ORIGINAL_sentence_termination_loss = []
ALL_ORIGINAL_sentence_loc_reconstruct_loss = []
ALL_ORIGINAL_sentence_touch_reconstruct_loss = []
ALL_TYPE_A_sentence_termination_loss = []
ALL_TYPE_A_sentence_loc_reconstruct_loss = []
ALL_TYPE_A_sentence_touch_reconstruct_loss = []
ALL_TYPE_A_sentence_WC_reconstruct_loss = []
ALL_TYPE_B_sentence_termination_loss = []
ALL_TYPE_B_sentence_loc_reconstruct_loss = []
ALL_TYPE_B_sentence_touch_reconstruct_loss = []
ALL_TYPE_B_sentence_WC_reconstruct_loss = []
ALL_word_W_consistency_loss = []
ALL_ORIGINAL_word_termination_loss = []
ALL_ORIGINAL_word_loc_reconstruct_loss = []
ALL_ORIGINAL_word_touch_reconstruct_loss = []
ALL_TYPE_A_word_termination_loss = []
ALL_TYPE_A_word_loc_reconstruct_loss = []
ALL_TYPE_A_word_touch_reconstruct_loss = []
ALL_TYPE_A_word_WC_reconstruct_loss = []
ALL_TYPE_B_word_termination_loss = []
ALL_TYPE_B_word_loc_reconstruct_loss = []
ALL_TYPE_B_word_touch_reconstruct_loss = []
ALL_TYPE_B_word_WC_reconstruct_loss = []
ALL_TYPE_C_word_termination_loss = []
ALL_TYPE_C_word_loc_reconstruct_loss = []
ALL_TYPE_C_word_touch_reconstruct_loss = []
ALL_TYPE_C_word_WC_reconstruct_loss = []
ALL_TYPE_D_word_termination_loss = []
ALL_TYPE_D_word_loc_reconstruct_loss = []
ALL_TYPE_D_word_touch_reconstruct_loss = []
ALL_TYPE_D_word_WC_reconstruct_loss = []
ALL_word_Wcs_reconstruct_TYPE_A = []
ALL_word_Wcs_reconstruct_TYPE_B = []
ALL_word_Wcs_reconstruct_TYPE_C = []
ALL_word_Wcs_reconstruct_TYPE_D = []
SUPER_ALL_segment_W_consistency_loss = []
SUPER_ALL_ORIGINAL_segment_termination_loss = []
SUPER_ALL_ORIGINAL_segment_loc_reconstruct_loss = []
SUPER_ALL_ORIGINAL_segment_touch_reconstruct_loss = []
SUPER_ALL_TYPE_A_segment_termination_loss = []
SUPER_ALL_TYPE_A_segment_loc_reconstruct_loss = []
SUPER_ALL_TYPE_A_segment_touch_reconstruct_loss = []
SUPER_ALL_TYPE_A_segment_WC_reconstruct_loss = []
SUPER_ALL_TYPE_B_segment_termination_loss = []
SUPER_ALL_TYPE_B_segment_loc_reconstruct_loss = []
SUPER_ALL_TYPE_B_segment_touch_reconstruct_loss = []
SUPER_ALL_TYPE_B_segment_WC_reconstruct_loss = []
SUPER_ALL_segment_Wcs_reconstruct_TYPE_A = []
SUPER_ALL_segment_Wcs_reconstruct_TYPE_B = []
# if self.sentece_loss:
for uid in range(len(sentence_level_stroke_in)):
if self.sentence_loss:
user_sentence_level_stroke_in = sentence_level_stroke_in[uid]
user_sentence_level_stroke_out = sentence_level_stroke_out[uid]
user_sentence_level_stroke_length = sentence_level_stroke_length[uid]
user_sentence_level_term = sentence_level_term[uid]
user_sentence_level_char = sentence_level_char[uid]
user_sentence_level_char_length = sentence_level_char_length[uid]
sentence_batch_size = len(user_sentence_level_stroke_in)
sentence_inf_state_out = self.inf_state_fc1(user_sentence_level_stroke_out)
sentence_inf_state_out = self.inf_state_relu(sentence_inf_state_out)
sentence_inf_state_out, (c,h) = self.inf_state_lstm(sentence_inf_state_out)
sentence_gen_state_out = self.gen_state_fc1(user_sentence_level_stroke_in)
sentence_gen_state_out = self.gen_state_relu(sentence_gen_state_out)
sentence_gen_state_out, (c,h) = self.gen_state_lstm1(sentence_gen_state_out)
sentence_Ws = []
sentence_Wc_rec_TYPE_ = []
sentence_SPLITS = []
sentence_Cs_1 = []
sentence_unique_char_matrices_1 = []
for sentence_batch_id in range(sentence_batch_size):
curr_seq_len = user_sentence_level_stroke_length[sentence_batch_id][0]
curr_char_len = user_sentence_level_char_length[sentence_batch_id][0]
char_vector = torch.eye(len(CHARACTERS))[user_sentence_level_char[sentence_batch_id][:curr_char_len]].to(self.device)
current_term = user_sentence_level_term[sentence_batch_id][:curr_seq_len].unsqueeze(-1)
split_ids = torch.nonzero(current_term)[:,0]
char_vector_1 = self.char_vec_fc_1(char_vector)
char_vector_1 = self.char_vec_relu_1(char_vector_1)
unique_char_matrices_1 = []
for cid in range(len(char_vector)):
# Tower 1
unique_char_vector_1 = char_vector_1[cid:cid+1]
unique_char_input_1 = unique_char_vector_1.unsqueeze(0)
unique_char_out_1, (c,h) = self.char_lstm_1(unique_char_input_1)
unique_char_out_1 = unique_char_out_1.squeeze(0)
unique_char_out_1 = self.char_vec_fc2_1(unique_char_out_1)
unique_char_matrix_1 = unique_char_out_1.view([-1,1,self.weight_dim,self.weight_dim])
unique_char_matrix_1 = unique_char_matrix_1.squeeze(1)
unique_char_matrices_1.append(unique_char_matrix_1)
# Tower 1
char_out_1 = char_vector_1.unsqueeze(0)
char_out_1, (c,h) = self.char_lstm_1(char_out_1)
char_out_1 = char_out_1.squeeze(0)
char_out_1 = self.char_vec_fc2_1(char_out_1)
char_matrix_1 = char_out_1.view([-1,1,self.weight_dim,self.weight_dim])
char_matrix_1 = char_matrix_1.squeeze(1)
char_matrix_inv_1 = torch.inverse(char_matrix_1)
W_c_t = sentence_inf_state_out[sentence_batch_id][:curr_seq_len]
W_c = torch.stack([W_c_t[i] for i in split_ids])
# W = torch.bmm(char_matrix_inv, W_c.unsqueeze(2)).squeeze(-1)
# C1C2C3W = Wc
# W = C3-1 C2-1 C1-1 Wc
W = torch.bmm(char_matrix_inv_1,
W_c.unsqueeze(2)).squeeze(-1)
sentence_Ws.append(W)
sentence_Wc_rec_TYPE_.append(W_c)
sentence_Cs_1.append(char_matrix_1)
sentence_SPLITS.append(split_ids)
sentence_unique_char_matrices_1.append(unique_char_matrices_1)
sentence_Ws_stacked = torch.cat(sentence_Ws, 0)
sentence_Ws_reshaped = sentence_Ws_stacked.view([-1,self.weight_dim])
sentence_W_mean = sentence_Ws_reshaped.mean(0)
sentence_W_mean_repeat = sentence_W_mean.repeat(sentence_Ws_reshaped.size(0),1)
sentence_Ws_consistency_loss = torch.mean(torch.mean(torch.mul(sentence_W_mean_repeat - sentence_Ws_reshaped, sentence_W_mean_repeat - sentence_Ws_reshaped), -1))
ALL_sentence_W_consistency_loss.append(sentence_Ws_consistency_loss)
ORIGINAL_sentence_termination_loss = []
ORIGINAL_sentence_loc_reconstruct_loss = []
ORIGINAL_sentence_touch_reconstruct_loss = []
TYPE_A_sentence_termination_loss = []
TYPE_A_sentence_loc_reconstruct_loss = []
TYPE_A_sentence_touch_reconstruct_loss = []
TYPE_B_sentence_termination_loss = []
TYPE_B_sentence_loc_reconstruct_loss = []
TYPE_B_sentence_touch_reconstruct_loss = []
sentence_Wcs_reconstruct_TYPE_A = []
sentence_Wcs_reconstruct_TYPE_B = []
for sentence_batch_id in range(sentence_batch_size):
sentence_level_gen_encoded = sentence_gen_state_out[sentence_batch_id][:user_sentence_level_stroke_length[sentence_batch_id][0]]
sentence_level_target_eos = user_sentence_level_stroke_out[sentence_batch_id][:user_sentence_level_stroke_length[sentence_batch_id][0]][:,2]
sentence_level_target_x = user_sentence_level_stroke_out[sentence_batch_id][:user_sentence_level_stroke_length[sentence_batch_id][0]][:,0:1]
sentence_level_target_y = user_sentence_level_stroke_out[sentence_batch_id][:user_sentence_level_stroke_length[sentence_batch_id][0]][:,1:2]
sentence_level_target_term = user_sentence_level_term[sentence_batch_id][:user_sentence_level_stroke_length[sentence_batch_id][0]]
# ORIGINAL
if self.ORIGINAL:
sentence_W_lstm_in_ORIGINAL = []
curr_id = 0
for i in range(user_sentence_level_stroke_length[sentence_batch_id][0]):
sentence_W_lstm_in_ORIGINAL.append(sentence_Wc_rec_TYPE_[sentence_batch_id][curr_id])
if i in sentence_SPLITS[sentence_batch_id]:
curr_id += 1
sentence_W_lstm_in_ORIGINAL = torch.stack(sentence_W_lstm_in_ORIGINAL)
sentence_Wc_t_ORIGINAL = sentence_W_lstm_in_ORIGINAL
sentence_gen_lstm2_in_ORIGINAL = torch.cat([sentence_level_gen_encoded, sentence_Wc_t_ORIGINAL], -1)
sentence_gen_lstm2_in_ORIGINAL = sentence_gen_lstm2_in_ORIGINAL.unsqueeze(0)
sentence_gen_out_ORIGINAL,(c,h) = self.gen_state_lstm2(sentence_gen_lstm2_in_ORIGINAL)
sentence_gen_out_ORIGINAL = sentence_gen_out_ORIGINAL.squeeze(0)
mdn_out_ORIGINAL = self.gen_state_fc2(sentence_gen_out_ORIGINAL)
eos_ORIGINAL = mdn_out_ORIGINAL[:,0:1]
[mu1_ORIGINAL, mu2_ORIGINAL, sig1_ORIGINAL, sig2_ORIGINAL, rho_ORIGINAL, pi_ORIGINAL] = torch.split(mdn_out_ORIGINAL[:,1:], self.num_mixtures, 1)
sig1_ORIGINAL = sig1_ORIGINAL.exp() + 1e-3
sig2_ORIGINAL = sig2_ORIGINAL.exp() + 1e-3
rho_ORIGINAL = self.mdn_tanh(rho_ORIGINAL)
pi_ORIGINAL = self.mdn_softmax(pi_ORIGINAL)
term_out_ORIGINAL = self.term_fc1(sentence_gen_out_ORIGINAL)
term_out_ORIGINAL = self.term_relu1(term_out_ORIGINAL)
term_out_ORIGINAL = self.term_fc2(term_out_ORIGINAL)
term_out_ORIGINAL = self.term_relu2(term_out_ORIGINAL)
term_out_ORIGINAL = self.term_fc3(term_out_ORIGINAL)
term_pred_ORIGINAL = self.term_sigmoid(term_out_ORIGINAL)
gaussian_ORIGINAL = gaussian_2d(sentence_level_target_x, sentence_level_target_y, mu1_ORIGINAL, mu2_ORIGINAL, sig1_ORIGINAL, sig2_ORIGINAL, rho_ORIGINAL)
loss_gaussian_ORIGINAL = - torch.log(torch.sum(pi_ORIGINAL*gaussian_ORIGINAL, dim=1) + 1e-5)
ORIGINAL_sentence_term_loss = self.term_bce_loss(term_out_ORIGINAL.squeeze(1), sentence_level_target_term)
ORIGINAL_sentence_loc_loss = torch.mean(loss_gaussian_ORIGINAL)
ORIGINAL_sentence_touch_loss = self.mdn_bce_loss(eos_ORIGINAL.squeeze(1), sentence_level_target_eos)
ORIGINAL_sentence_termination_loss.append(ORIGINAL_sentence_term_loss)
ORIGINAL_sentence_loc_reconstruct_loss.append(ORIGINAL_sentence_loc_loss)
ORIGINAL_sentence_touch_reconstruct_loss.append(ORIGINAL_sentence_touch_loss)
# TYPE A
if self.TYPE_A:
sentence_C1 = sentence_Cs_1[sentence_batch_id]
# sentence_Wc_rec_TYPE_A = torch.bmm(sentence_Cs[sentence_batch_id], sentence_W_mean.repeat(sentence_Cs[sentence_batch_id].size(0),1).unsqueeze(2)).squeeze(-1)
sentence_Wc_rec_TYPE_A = torch.bmm(sentence_C1, \
sentence_W_mean.repeat(sentence_C1.size(0),1).unsqueeze(2)).squeeze(-1)
sentence_Wcs_reconstruct_TYPE_A.append(sentence_Wc_rec_TYPE_A)
sentence_W_lstm_in_TYPE_A = []
curr_id = 0
for i in range(user_sentence_level_stroke_length[sentence_batch_id][0]):
sentence_W_lstm_in_TYPE_A.append(sentence_Wc_rec_TYPE_A[curr_id])
if i in sentence_SPLITS[sentence_batch_id]:
curr_id += 1
sentence_Wc_t_rec_TYPE_A = torch.stack(sentence_W_lstm_in_TYPE_A)
sentence_gen_lstm2_in_TYPE_A = torch.cat([sentence_level_gen_encoded, sentence_Wc_t_rec_TYPE_A], -1)
sentence_gen_lstm2_in_TYPE_A = sentence_gen_lstm2_in_TYPE_A.unsqueeze(0)
sentence_gen_out_TYPE_A, (c,h) = self.gen_state_lstm2(sentence_gen_lstm2_in_TYPE_A)
sentence_gen_out_TYPE_A = sentence_gen_out_TYPE_A.squeeze(0)
mdn_out_TYPE_A = self.gen_state_fc2(sentence_gen_out_TYPE_A)
eos_TYPE_A = mdn_out_TYPE_A[:,0:1]
[mu1_TYPE_A, mu2_TYPE_A, sig1_TYPE_A, sig2_TYPE_A, rho_TYPE_A, pi_TYPE_A] = torch.split(mdn_out_TYPE_A[:,1:], self.num_mixtures, 1)
sig1_TYPE_A = sig1_TYPE_A.exp() + 1e-3
sig2_TYPE_A = sig2_TYPE_A.exp() + 1e-3
rho_TYPE_A = self.mdn_tanh(rho_TYPE_A)
pi_TYPE_A = self.mdn_softmax(pi_TYPE_A)
term_out_TYPE_A = self.term_fc1(sentence_gen_out_TYPE_A)
term_out_TYPE_A = self.term_relu1(term_out_TYPE_A)
term_out_TYPE_A = self.term_fc2(term_out_TYPE_A)
term_out_TYPE_A = self.term_relu2(term_out_TYPE_A)
term_out_TYPE_A = self.term_fc3(term_out_TYPE_A)
term_pred_TYPE_A = self.term_sigmoid(term_out_TYPE_A)
gaussian_TYPE_A = gaussian_2d(sentence_level_target_x, sentence_level_target_y, mu1_TYPE_A, mu2_TYPE_A, sig1_TYPE_A, sig2_TYPE_A, rho_TYPE_A)
loss_gaussian_TYPE_A = - torch.log(torch.sum(pi_TYPE_A*gaussian_TYPE_A, dim=1) + 1e-5)
TYPE_A_sentence_term_loss = self.term_bce_loss(term_out_TYPE_A.squeeze(1), sentence_level_target_term)
TYPE_A_sentence_loc_loss = torch.mean(loss_gaussian_TYPE_A)
TYPE_A_sentence_touch_loss = self.mdn_bce_loss(eos_TYPE_A.squeeze(1), sentence_level_target_eos)
TYPE_A_sentence_termination_loss.append(TYPE_A_sentence_term_loss)
TYPE_A_sentence_loc_reconstruct_loss.append(TYPE_A_sentence_loc_loss)
TYPE_A_sentence_touch_reconstruct_loss.append(TYPE_A_sentence_touch_loss)
# TYPE B
if self.TYPE_B:
unique_char_matrix_1 = sentence_unique_char_matrices_1[sentence_batch_id]
unique_char_matrices_1 = torch.stack(unique_char_matrix_1)
unique_char_matrices_1 = unique_char_matrices_1.squeeze(1)
# sentence_W_c_TYPE_B_RAW = torch.bmm(unique_char_matrices, sentence_W_mean.repeat(unique_char_matrices.size(0), 1).unsqueeze(2)).squeeze(-1)
sentence_W_c_TYPE_B_RAW = torch.bmm(unique_char_matrices_1,
sentence_W_mean.repeat(unique_char_matrices_1.size(0), 1).unsqueeze(2)).squeeze(-1)
sentence_W_c_TYPE_B_RAW = sentence_W_c_TYPE_B_RAW.unsqueeze(0)
sentence_Wc_rec_TYPE_B, (c,h) = self.magic_lstm(sentence_W_c_TYPE_B_RAW)
sentence_Wc_rec_TYPE_B = sentence_Wc_rec_TYPE_B.squeeze(0)
sentence_Wcs_reconstruct_TYPE_B.append(sentence_Wc_rec_TYPE_B)
sentence_W_lstm_in_TYPE_B = []
curr_id = 0
for i in range(user_sentence_level_stroke_length[sentence_batch_id][0]):
sentence_W_lstm_in_TYPE_B.append(sentence_Wc_rec_TYPE_B[curr_id])
if i in sentence_SPLITS[sentence_batch_id]:
curr_id += 1
sentence_Wc_t_rec_TYPE_B = torch.stack(sentence_W_lstm_in_TYPE_B)
sentence_gen_lstm2_in_TYPE_B = torch.cat([sentence_level_gen_encoded, sentence_Wc_t_rec_TYPE_B], -1)
sentence_gen_lstm2_in_TYPE_B = sentence_gen_lstm2_in_TYPE_B.unsqueeze(0)
sentence_gen_out_TYPE_B, (c,h) = self.gen_state_lstm2(sentence_gen_lstm2_in_TYPE_B)
sentence_gen_out_TYPE_B = sentence_gen_out_TYPE_B.squeeze(0)
mdn_out_TYPE_B = self.gen_state_fc2(sentence_gen_out_TYPE_B)
eos_TYPE_B = mdn_out_TYPE_B[:,0:1]
[mu1_TYPE_B, mu2_TYPE_B, sig1_TYPE_B, sig2_TYPE_B, rho_TYPE_B, pi_TYPE_B] = torch.split(mdn_out_TYPE_B[:,1:], self.num_mixtures, 1)
sig1_TYPE_B = sig1_TYPE_B.exp() + 1e-3
sig2_TYPE_B = sig2_TYPE_B.exp() + 1e-3
rho_TYPE_B = self.mdn_tanh(rho_TYPE_B)
pi_TYPE_B = self.mdn_softmax(pi_TYPE_B)
term_out_TYPE_B = self.term_fc1(sentence_gen_out_TYPE_B)
term_out_TYPE_B = self.term_relu1(term_out_TYPE_B)
term_out_TYPE_B = self.term_fc2(term_out_TYPE_B)
term_out_TYPE_B = self.term_relu2(term_out_TYPE_B)
term_out_TYPE_B = self.term_fc3(term_out_TYPE_B)
term_pred_TYPE_B = self.term_sigmoid(term_out_TYPE_B)
gaussian_TYPE_B = gaussian_2d(sentence_level_target_x, sentence_level_target_y, mu1_TYPE_B, mu2_TYPE_B, sig1_TYPE_B, sig2_TYPE_B, rho_TYPE_B)
loss_gaussian_TYPE_B = - torch.log(torch.sum(pi_TYPE_B*gaussian_TYPE_B, dim=1) + 1e-5)
TYPE_B_sentence_term_loss = self.term_bce_loss(term_out_TYPE_B.squeeze(1), sentence_level_target_term)
TYPE_B_sentence_loc_loss = torch.mean(loss_gaussian_TYPE_B)
TYPE_B_sentence_touch_loss = self.mdn_bce_loss(eos_TYPE_B.squeeze(1), sentence_level_target_eos)
TYPE_B_sentence_termination_loss.append(TYPE_B_sentence_term_loss)
TYPE_B_sentence_loc_reconstruct_loss.append(TYPE_B_sentence_loc_loss)
TYPE_B_sentence_touch_reconstruct_loss.append(TYPE_B_sentence_touch_loss)
if self.ORIGINAL:
ALL_ORIGINAL_sentence_termination_loss.append(torch.mean(torch.stack(ORIGINAL_sentence_termination_loss)))
ALL_ORIGINAL_sentence_loc_reconstruct_loss.append(torch.mean(torch.stack(ORIGINAL_sentence_loc_reconstruct_loss)))
ALL_ORIGINAL_sentence_touch_reconstruct_loss.append(torch.mean(torch.stack(ORIGINAL_sentence_touch_reconstruct_loss)))
if self.TYPE_A:
ALL_TYPE_A_sentence_termination_loss.append(torch.mean(torch.stack(TYPE_A_sentence_termination_loss)))
ALL_TYPE_A_sentence_loc_reconstruct_loss.append(torch.mean(torch.stack(TYPE_A_sentence_loc_reconstruct_loss)))
ALL_TYPE_A_sentence_touch_reconstruct_loss.append(torch.mean(torch.stack(TYPE_A_sentence_touch_reconstruct_loss)))
if self.REC:
TYPE_A_sentence_WC_reconstruct_loss = []
for sentence_batch_id in range(len(sentence_Wc_rec_TYPE_)):
sentence_Wc_ORIGINAL = sentence_Wc_rec_TYPE_[sentence_batch_id]
sentence_Wc_TYPE_A = sentence_Wcs_reconstruct_TYPE_A[sentence_batch_id]
sentence_WC_reconstruct_loss_TYPE_A = torch.mean(torch.mean(torch.mul(sentence_Wc_ORIGINAL - sentence_Wc_TYPE_A, sentence_Wc_ORIGINAL - sentence_Wc_TYPE_A), -1))
TYPE_A_sentence_WC_reconstruct_loss.append(sentence_WC_reconstruct_loss_TYPE_A)
ALL_TYPE_A_sentence_WC_reconstruct_loss.append(torch.mean(torch.stack(TYPE_A_sentence_WC_reconstruct_loss)))
if self.TYPE_B:
ALL_TYPE_B_sentence_termination_loss.append(torch.mean(torch.stack(TYPE_B_sentence_termination_loss)))
ALL_TYPE_B_sentence_loc_reconstruct_loss.append(torch.mean(torch.stack(TYPE_B_sentence_loc_reconstruct_loss)))
ALL_TYPE_B_sentence_touch_reconstruct_loss.append(torch.mean(torch.stack(TYPE_B_sentence_touch_reconstruct_loss)))
if self.REC:
TYPE_B_sentence_WC_reconstruct_loss = []
for sentence_batch_id in range(len(sentence_Wc_rec_TYPE_)):
sentence_Wc_ORIGINAL = sentence_Wc_rec_TYPE_[sentence_batch_id]
sentence_Wc_TYPE_B = sentence_Wcs_reconstruct_TYPE_B[sentence_batch_id]
sentence_WC_reconstruct_loss_TYPE_B = torch.mean(torch.mean(torch.mul(sentence_Wc_ORIGINAL - sentence_Wc_TYPE_B, sentence_Wc_ORIGINAL - sentence_Wc_TYPE_B), -1))
TYPE_B_sentence_WC_reconstruct_loss.append(sentence_WC_reconstruct_loss_TYPE_B)
ALL_TYPE_B_sentence_WC_reconstruct_loss.append(torch.mean(torch.stack(TYPE_B_sentence_WC_reconstruct_loss)))
if self.word_loss:
user_word_level_stroke_in = word_level_stroke_in[uid]
user_word_level_stroke_out = word_level_stroke_out[uid]
user_word_level_stroke_length = word_level_stroke_length[uid]
user_word_level_term = word_level_term[uid]
user_word_level_char = word_level_char[uid]
user_word_level_char_length = word_level_char_length[uid]
word_batch_size = len(user_word_level_stroke_in)
word_inf_state_out = self.inf_state_fc1(user_word_level_stroke_out)
word_inf_state_out = self.inf_state_relu(word_inf_state_out)
word_inf_state_out, (c,h) = self.inf_state_lstm(word_inf_state_out)
word_gen_state_out = self.gen_state_fc1(user_word_level_stroke_in)
word_gen_state_out = self.gen_state_relu(word_gen_state_out)
word_gen_state_out, (c,h) = self.gen_state_lstm1(word_gen_state_out)
word_Ws = []
word_Wc_rec_ORIGINAL = []
word_SPLITS = []
word_Cs_1 = []
word_unique_char_matrices_1 = []
W_C_ORIGINALS = []
for word_batch_id in range(word_batch_size):
curr_seq_len = user_word_level_stroke_length[word_batch_id][0]
curr_char_len = user_word_level_char_length[word_batch_id][0]
char_vector = torch.eye(len(CHARACTERS))[user_word_level_char[word_batch_id][:curr_char_len]].to(self.device)
current_term = user_word_level_term[word_batch_id][:curr_seq_len].unsqueeze(-1)
split_ids = torch.nonzero(current_term)[:,0]
char_vector_1 = self.char_vec_fc_1(char_vector)
char_vector_1 = self.char_vec_relu_1(char_vector_1)
unique_char_matrices_1 = []
for cid in range(len(char_vector)):
# Tower 1
unique_char_vector_1 = char_vector_1[cid:cid+1]
unique_char_input_1 = unique_char_vector_1.unsqueeze(0)
unique_char_out_1, (c,h) = self.char_lstm_1(unique_char_input_1)
unique_char_out_1 = unique_char_out_1.squeeze(0)
unique_char_out_1 = self.char_vec_fc2_1(unique_char_out_1)
unique_char_matrix_1 = unique_char_out_1.view([-1,1,self.weight_dim,self.weight_dim])
unique_char_matrix_1 = unique_char_matrix_1.squeeze(1)
unique_char_matrices_1.append(unique_char_matrix_1)
# Tower 1
char_out_1 = char_vector_1.unsqueeze(0)
char_out_1, (c,h) = self.char_lstm_1(char_out_1)
char_out_1 = char_out_1.squeeze(0)
char_out_1 = self.char_vec_fc2_1(char_out_1)
char_matrix_1 = char_out_1.view([-1,1,self.weight_dim,self.weight_dim])
char_matrix_1 = char_matrix_1.squeeze(1)
char_matrix_inv_1 = torch.inverse(char_matrix_1)
W_c_t = word_inf_state_out[word_batch_id][:curr_seq_len]
W_c = torch.stack([W_c_t[i] for i in split_ids])
W_C_ORIGINAL = {}
for i in range(curr_char_len):
sub_s = "".join(CHARACTERS[i] for i in user_word_level_char[word_batch_id][:i+1])
W_C_ORIGINAL[sub_s] = [W_c[i]]
W_C_ORIGINALS.append(W_C_ORIGINAL)
# W = torch.bmm(char_matrix_inv, W_c.unsqueeze(2)).squeeze(-1)
W = torch.bmm(char_matrix_inv_1,
W_c.unsqueeze(2)).squeeze(-1)
word_Ws.append(W)
word_Wc_rec_ORIGINAL.append(W_c)
word_SPLITS.append(split_ids)
# word_Cs.append(char_matrix)
# word_unique_char_matrices.append(unique_char_matrices)
word_Cs_1.append(char_matrix_1)
word_unique_char_matrices_1.append(unique_char_matrices_1)
word_Ws_stacked = torch.cat(word_Ws, 0)
word_Ws_reshaped = word_Ws_stacked.view([-1,self.weight_dim])
word_W_mean = word_Ws_reshaped.mean(0)
word_Ws_reshaped_mean_repeat = word_W_mean.repeat(word_Ws_reshaped.size(0),1)
word_Ws_consistency_loss = torch.mean(torch.mean(torch.mul(word_Ws_reshaped_mean_repeat - word_Ws_reshaped, word_Ws_reshaped_mean_repeat - word_Ws_reshaped), -1))
ALL_word_W_consistency_loss.append(word_Ws_consistency_loss)
# word
ORIGINAL_word_termination_loss = []
ORIGINAL_word_loc_reconstruct_loss = []
ORIGINAL_word_touch_reconstruct_loss = []
TYPE_A_word_termination_loss = []
TYPE_A_word_loc_reconstruct_loss = []
TYPE_A_word_touch_reconstruct_loss = []
TYPE_B_word_termination_loss = []
TYPE_B_word_loc_reconstruct_loss = []
TYPE_B_word_touch_reconstruct_loss = []
TYPE_C_word_termination_loss = []
TYPE_C_word_loc_reconstruct_loss = []
TYPE_C_word_touch_reconstruct_loss = []
TYPE_D_word_termination_loss = []
TYPE_D_word_loc_reconstruct_loss = []
TYPE_D_word_touch_reconstruct_loss = []
word_Wcs_reconstruct_TYPE_A = []
word_Wcs_reconstruct_TYPE_B = []
word_Wcs_reconstruct_TYPE_C = []
word_Wcs_reconstruct_TYPE_D = []
# segment
ALL_segment_W_consistency_loss = []
ALL_ORIGINAL_segment_termination_loss = []
ALL_ORIGINAL_segment_loc_reconstruct_loss = []
ALL_ORIGINAL_segment_touch_reconstruct_loss = []
ALL_TYPE_A_segment_termination_loss = []
ALL_TYPE_A_segment_loc_reconstruct_loss = []
ALL_TYPE_A_segment_touch_reconstruct_loss = []
ALL_TYPE_A_segment_WC_reconstruct_loss = []
ALL_TYPE_B_segment_termination_loss = []
ALL_TYPE_B_segment_loc_reconstruct_loss = []
ALL_TYPE_B_segment_touch_reconstruct_loss = []
ALL_TYPE_B_segment_WC_reconstruct_loss = []
ALL_segment_Wcs_reconstruct_TYPE_A = []
ALL_segment_Wcs_reconstruct_TYPE_B = []
W_C_SEGMENTS = []
W_C_UNIQUES = []
for word_batch_id in range(word_batch_size):
word_level_gen_encoded = word_gen_state_out[word_batch_id][:user_word_level_stroke_length[word_batch_id][0]]
word_level_target_eos = user_word_level_stroke_out[word_batch_id][:user_word_level_stroke_length[word_batch_id][0]][:,2]
word_level_target_x = user_word_level_stroke_out[word_batch_id][:user_word_level_stroke_length[word_batch_id][0]][:,0:1]
word_level_target_y = user_word_level_stroke_out[word_batch_id][:user_word_level_stroke_length[word_batch_id][0]][:,1:2]
word_level_target_term = user_word_level_term[word_batch_id][:user_word_level_stroke_length[word_batch_id][0]]
# ORIGINAL
if self.ORIGINAL:
word_W_lstm_in_ORIGINAL = []
curr_id = 0
for i in range(user_word_level_stroke_length[word_batch_id][0]):
word_W_lstm_in_ORIGINAL.append(word_Wc_rec_ORIGINAL[word_batch_id][curr_id])
if i in word_SPLITS[word_batch_id]:
curr_id += 1
word_W_lstm_in_ORIGINAL = torch.stack(word_W_lstm_in_ORIGINAL)
word_Wc_t_ORIGINAL = word_W_lstm_in_ORIGINAL
word_gen_lstm2_in_ORIGINAL = torch.cat([word_level_gen_encoded, word_Wc_t_ORIGINAL], -1)
word_gen_lstm2_in_ORIGINAL = word_gen_lstm2_in_ORIGINAL.unsqueeze(0)
word_gen_out_ORIGINAL,(c,h) = self.gen_state_lstm2(word_gen_lstm2_in_ORIGINAL)
word_gen_out_ORIGINAL = word_gen_out_ORIGINAL.squeeze(0)
mdn_out_ORIGINAL = self.gen_state_fc2(word_gen_out_ORIGINAL)
eos_ORIGINAL = mdn_out_ORIGINAL[:,0:1]
[mu1_ORIGINAL, mu2_ORIGINAL, sig1_ORIGINAL, sig2_ORIGINAL, rho_ORIGINAL, pi_ORIGINAL] = torch.split(mdn_out_ORIGINAL[:,1:], self.num_mixtures, 1)
sig1_ORIGINAL = sig1_ORIGINAL.exp() + 1e-3
sig2_ORIGINAL = sig2_ORIGINAL.exp() + 1e-3
rho_ORIGINAL = self.mdn_tanh(rho_ORIGINAL)
pi_ORIGINAL = self.mdn_softmax(pi_ORIGINAL)
term_out_ORIGINAL = self.term_fc1(word_gen_out_ORIGINAL)
term_out_ORIGINAL = self.term_relu1(term_out_ORIGINAL)
term_out_ORIGINAL = self.term_fc2(term_out_ORIGINAL)
term_out_ORIGINAL = self.term_relu2(term_out_ORIGINAL)
term_out_ORIGINAL = self.term_fc3(term_out_ORIGINAL)
term_pred_ORIGINAL = self.term_sigmoid(term_out_ORIGINAL)
gaussian_ORIGINAL = gaussian_2d(word_level_target_x, word_level_target_y, mu1_ORIGINAL, mu2_ORIGINAL, sig1_ORIGINAL, sig2_ORIGINAL, rho_ORIGINAL)
loss_gaussian_ORIGINAL = - torch.log(torch.sum(pi_ORIGINAL*gaussian_ORIGINAL, dim=1) + 1e-5)
ORIGINAL_word_term_loss = self.term_bce_loss(term_out_ORIGINAL.squeeze(1), word_level_target_term)
ORIGINAL_word_loc_loss = torch.mean(loss_gaussian_ORIGINAL)
ORIGINAL_word_touch_loss = self.mdn_bce_loss(eos_ORIGINAL.squeeze(1), word_level_target_eos)
ORIGINAL_word_termination_loss.append(ORIGINAL_word_term_loss)
ORIGINAL_word_loc_reconstruct_loss.append(ORIGINAL_word_loc_loss)
ORIGINAL_word_touch_reconstruct_loss.append(ORIGINAL_word_touch_loss)
# TYPE A
if self.TYPE_A:
word_C1 = word_Cs_1[word_batch_id]
word_Wc_rec_TYPE_A = torch.bmm(word_C1,
word_W_mean.repeat(word_C1.size(0),1).unsqueeze(2)).squeeze(-1)
word_Wcs_reconstruct_TYPE_A.append(word_Wc_rec_TYPE_A)
word_W_lstm_in_TYPE_A = []
curr_id = 0
for i in range(user_word_level_stroke_length[word_batch_id][0]):
word_W_lstm_in_TYPE_A.append(word_Wc_rec_TYPE_A[curr_id])
if i in word_SPLITS[word_batch_id]:
curr_id += 1
word_Wc_t_rec_TYPE_A = torch.stack(word_W_lstm_in_TYPE_A)
word_gen_lstm2_in_TYPE_A = torch.cat([word_level_gen_encoded, word_Wc_t_rec_TYPE_A], -1)
word_gen_lstm2_in_TYPE_A = word_gen_lstm2_in_TYPE_A.unsqueeze(0)
word_gen_out_TYPE_A, (c,h) = self.gen_state_lstm2(word_gen_lstm2_in_TYPE_A)
word_gen_out_TYPE_A = word_gen_out_TYPE_A.squeeze(0)
mdn_out_TYPE_A = self.gen_state_fc2(word_gen_out_TYPE_A)
eos_TYPE_A = mdn_out_TYPE_A[:,0:1]
[mu1_TYPE_A, mu2_TYPE_A, sig1_TYPE_A, sig2_TYPE_A, rho_TYPE_A, pi_TYPE_A] = torch.split(mdn_out_TYPE_A[:,1:], self.num_mixtures, 1)
sig1_TYPE_A = sig1_TYPE_A.exp() + 1e-3
sig2_TYPE_A = sig2_TYPE_A.exp() + 1e-3
rho_TYPE_A = self.mdn_tanh(rho_TYPE_A)
pi_TYPE_A = self.mdn_softmax(pi_TYPE_A)
term_out_TYPE_A = self.term_fc1(word_gen_out_TYPE_A)
term_out_TYPE_A = self.term_relu1(term_out_TYPE_A)
term_out_TYPE_A = self.term_fc2(term_out_TYPE_A)
term_out_TYPE_A = self.term_relu2(term_out_TYPE_A)
term_out_TYPE_A = self.term_fc3(term_out_TYPE_A)
term_pred_TYPE_A = self.term_sigmoid(term_out_TYPE_A)
gaussian_TYPE_A = gaussian_2d(word_level_target_x, word_level_target_y, mu1_TYPE_A, mu2_TYPE_A, sig1_TYPE_A, sig2_TYPE_A, rho_TYPE_A)
loss_gaussian_TYPE_A = - torch.log(torch.sum(pi_TYPE_A*gaussian_TYPE_A, dim=1) + 1e-5)
TYPE_A_word_term_loss = self.term_bce_loss(term_out_TYPE_A.squeeze(1), word_level_target_term)
TYPE_A_word_loc_loss = torch.mean(loss_gaussian_TYPE_A)
TYPE_A_word_touch_loss = self.mdn_bce_loss(eos_TYPE_A.squeeze(1), word_level_target_eos)
TYPE_A_word_termination_loss.append(TYPE_A_word_term_loss)
TYPE_A_word_loc_reconstruct_loss.append(TYPE_A_word_loc_loss)
TYPE_A_word_touch_reconstruct_loss.append(TYPE_A_word_touch_loss)
# TYPE B
if self.TYPE_B:
unique_char_matrix_1 = word_unique_char_matrices_1[word_batch_id]
unique_char_matrices_1 = torch.stack(unique_char_matrix_1)
unique_char_matrices_1 = unique_char_matrices_1.squeeze(1)
# word_W_c_TYPE_B_RAW = torch.bmm(unique_char_matrices, word_W_mean.repeat(unique_char_matrices.size(0), 1).unsqueeze(2)).squeeze(-1)
word_W_c_TYPE_B_RAW = torch.bmm(unique_char_matrices_1,
word_W_mean.repeat(unique_char_matrices_1.size(0), 1).unsqueeze(2)).squeeze(-1)
word_W_c_TYPE_B_RAW = word_W_c_TYPE_B_RAW.unsqueeze(0)
word_Wc_rec_TYPE_B, (c,h) = self.magic_lstm(word_W_c_TYPE_B_RAW)
word_Wc_rec_TYPE_B = word_Wc_rec_TYPE_B.squeeze(0)
word_Wcs_reconstruct_TYPE_B.append(word_Wc_rec_TYPE_B)
word_W_lstm_in_TYPE_B = []
curr_id = 0
for i in range(user_word_level_stroke_length[word_batch_id][0]):
word_W_lstm_in_TYPE_B.append(word_Wc_rec_TYPE_B[curr_id])
if i in word_SPLITS[word_batch_id]:
curr_id += 1
word_Wc_t_rec_TYPE_B = torch.stack(word_W_lstm_in_TYPE_B)
word_gen_lstm2_in_TYPE_B = torch.cat([word_level_gen_encoded, word_Wc_t_rec_TYPE_B], -1)
word_gen_lstm2_in_TYPE_B = word_gen_lstm2_in_TYPE_B.unsqueeze(0)
word_gen_out_TYPE_B, (c,h) = self.gen_state_lstm2(word_gen_lstm2_in_TYPE_B)
word_gen_out_TYPE_B = word_gen_out_TYPE_B.squeeze(0)
mdn_out_TYPE_B = self.gen_state_fc2(word_gen_out_TYPE_B)
eos_TYPE_B = mdn_out_TYPE_B[:,0:1]
[mu1_TYPE_B, mu2_TYPE_B, sig1_TYPE_B, sig2_TYPE_B, rho_TYPE_B, pi_TYPE_B] = torch.split(mdn_out_TYPE_B[:,1:], self.num_mixtures, 1)
sig1_TYPE_B = sig1_TYPE_B.exp() + 1e-3
sig2_TYPE_B = sig2_TYPE_B.exp() + 1e-3
rho_TYPE_B = self.mdn_tanh(rho_TYPE_B)
pi_TYPE_B = self.mdn_softmax(pi_TYPE_B)
term_out_TYPE_B = self.term_fc1(word_gen_out_TYPE_B)
term_out_TYPE_B = self.term_relu1(term_out_TYPE_B)
term_out_TYPE_B = self.term_fc2(term_out_TYPE_B)
term_out_TYPE_B = self.term_relu2(term_out_TYPE_B)
term_out_TYPE_B = self.term_fc3(term_out_TYPE_B)
term_pred_TYPE_B = self.term_sigmoid(term_out_TYPE_B)
gaussian_TYPE_B = gaussian_2d(word_level_target_x, word_level_target_y, mu1_TYPE_B, mu2_TYPE_B, sig1_TYPE_B, sig2_TYPE_B, rho_TYPE_B)
loss_gaussian_TYPE_B = - torch.log(torch.sum(pi_TYPE_B*gaussian_TYPE_B, dim=1) + 1e-5)
TYPE_B_word_term_loss = self.term_bce_loss(term_out_TYPE_B.squeeze(1), word_level_target_term)
TYPE_B_word_loc_loss = torch.mean(loss_gaussian_TYPE_B)
TYPE_B_word_touch_loss = self.mdn_bce_loss(eos_TYPE_B.squeeze(1), word_level_target_eos)
TYPE_B_word_termination_loss.append(TYPE_B_word_term_loss)
TYPE_B_word_loc_reconstruct_loss.append(TYPE_B_word_loc_loss)
TYPE_B_word_touch_reconstruct_loss.append(TYPE_B_word_touch_loss)
# TYPE C
# if self.TYPE_C:
user_segment_level_stroke_in = segment_level_stroke_in[uid][word_batch_id]
user_segment_level_stroke_out = segment_level_stroke_out[uid][word_batch_id]
user_segment_level_stroke_length = segment_level_stroke_length[uid][word_batch_id]
user_segment_level_term = segment_level_term[uid][word_batch_id]
user_segment_level_char = segment_level_char[uid][word_batch_id]
user_segment_level_char_length = segment_level_char_length[uid][word_batch_id]
segment_batch_size = len(user_segment_level_stroke_in)
segment_inf_state_out = self.inf_state_fc1(user_segment_level_stroke_out)
segment_inf_state_out = self.inf_state_relu(segment_inf_state_out)
segment_inf_state_out, (c,h) = self.inf_state_lstm(segment_inf_state_out)
segment_gen_state_out = self.gen_state_fc1(user_segment_level_stroke_in)
segment_gen_state_out = self.gen_state_relu(segment_gen_state_out)
segment_gen_state_out, (c,h) = self.gen_state_lstm1(segment_gen_state_out)
segment_Ws = []
segment_Wc_rec_ORIGINAL = []
segment_SPLITS = []
segment_Cs_1 = []
segment_unique_char_matrices_1 = []
W_C_SEGMENT = {}
for segment_batch_id in range(segment_batch_size):
curr_seq_len = user_segment_level_stroke_length[segment_batch_id][0]
curr_char_len = user_segment_level_char_length[segment_batch_id][0]
char_vector = torch.eye(len(CHARACTERS))[user_segment_level_char[segment_batch_id][:curr_char_len]].to(self.device)
current_term = user_segment_level_term[segment_batch_id][:curr_seq_len].unsqueeze(-1)
split_ids = torch.nonzero(current_term)[:,0]
char_vector_1 = self.char_vec_fc_1(char_vector)
char_vector_1 = self.char_vec_relu_1(char_vector_1)
unique_char_matrices_1 = []
for cid in range(len(char_vector)):
# Tower 1
unique_char_vector_1 = char_vector_1[cid:cid+1]
unique_char_input_1 = unique_char_vector_1.unsqueeze(0)
unique_char_out_1, (c,h) = self.char_lstm_1(unique_char_input_1)
unique_char_out_1 = unique_char_out_1.squeeze(0)
unique_char_out_1 = self.char_vec_fc2_1(unique_char_out_1)
unique_char_matrix_1 = unique_char_out_1.view([-1,1,self.weight_dim,self.weight_dim])
unique_char_matrix_1 = unique_char_matrix_1.squeeze(1)
unique_char_matrices_1.append(unique_char_matrix_1)
# Tower 1
char_out_1 = char_vector_1.unsqueeze(0)
char_out_1, (c,h) = self.char_lstm_1(char_out_1)
char_out_1 = char_out_1.squeeze(0)
char_out_1 = self.char_vec_fc2_1(char_out_1)
char_matrix_1 = char_out_1.view([-1,1,self.weight_dim,self.weight_dim])
char_matrix_1 = char_matrix_1.squeeze(1)
char_matrix_inv_1 = torch.inverse(char_matrix_1)
W_c_t = segment_inf_state_out[segment_batch_id][:curr_seq_len]
W_c = torch.stack([W_c_t[i] for i in split_ids])
for i in range(curr_char_len):
sub_s = "".join(CHARACTERS[i] for i in user_segment_level_char[segment_batch_id][:i+1])
if sub_s in W_C_SEGMENT:
W_C_SEGMENT[sub_s].append(W_c[i])
else:
W_C_SEGMENT[sub_s] = [W_c[i]]
W = torch.bmm(char_matrix_inv_1,
W_c.unsqueeze(2)).squeeze(-1)
segment_Ws.append(W)
segment_Wc_rec_ORIGINAL.append(W_c)
segment_SPLITS.append(split_ids)
segment_Cs_1.append(char_matrix_1)
segment_unique_char_matrices_1.append(unique_char_matrices_1)
W_C_SEGMENTS.append(W_C_SEGMENT)
if self.segment_loss:
segment_Ws_stacked = torch.cat(segment_Ws, 0)
segment_Ws_reshaped = segment_Ws_stacked.view([-1,self.weight_dim])
segment_W_mean = segment_Ws_reshaped.mean(0)
segment_Ws_reshaped_mean_repeat = segment_W_mean.repeat(segment_Ws_reshaped.size(0),1)
segment_Ws_consistency_loss = torch.mean(torch.mean(torch.mul(segment_Ws_reshaped_mean_repeat - segment_Ws_reshaped, segment_Ws_reshaped_mean_repeat - segment_Ws_reshaped), -1))
ALL_segment_W_consistency_loss.append(segment_Ws_consistency_loss)
ORIGINAL_segment_termination_loss = []
ORIGINAL_segment_loc_reconstruct_loss = []
ORIGINAL_segment_touch_reconstruct_loss = []
TYPE_A_segment_termination_loss = []
TYPE_A_segment_loc_reconstruct_loss = []
TYPE_A_segment_touch_reconstruct_loss = []
TYPE_B_segment_termination_loss = []
TYPE_B_segment_loc_reconstruct_loss = []
TYPE_B_segment_touch_reconstruct_loss = []
segment_Wcs_reconstruct_TYPE_A = []
segment_Wcs_reconstruct_TYPE_B = []
for segment_batch_id in range(segment_batch_size):
segment_level_gen_encoded = segment_gen_state_out[segment_batch_id][:user_segment_level_stroke_length[segment_batch_id][0]]
segment_level_target_eos = user_segment_level_stroke_out[segment_batch_id][:user_segment_level_stroke_length[segment_batch_id][0]][:,2]
segment_level_target_x = user_segment_level_stroke_out[segment_batch_id][:user_segment_level_stroke_length[segment_batch_id][0]][:,0:1]
segment_level_target_y = user_segment_level_stroke_out[segment_batch_id][:user_segment_level_stroke_length[segment_batch_id][0]][:,1:2]
segment_level_target_term = user_segment_level_term[segment_batch_id][:user_segment_level_stroke_length[segment_batch_id][0]]
if self.ORIGINAL:
segment_W_lstm_in_ORIGINAL = []
curr_id = 0
for i in range(user_segment_level_stroke_length[segment_batch_id][0]):
segment_W_lstm_in_ORIGINAL.append(segment_Wc_rec_ORIGINAL[segment_batch_id][curr_id])
if i in segment_SPLITS[segment_batch_id]:
curr_id += 1
segment_W_lstm_in_ORIGINAL = torch.stack(segment_W_lstm_in_ORIGINAL)
segment_Wc_t_ORIGINAL = segment_W_lstm_in_ORIGINAL
segment_gen_lstm2_in_ORIGINAL = torch.cat([segment_level_gen_encoded, segment_Wc_t_ORIGINAL], -1)
segment_gen_lstm2_in_ORIGINAL = segment_gen_lstm2_in_ORIGINAL.unsqueeze(0)
segment_gen_out_ORIGINAL,(c,h) = self.gen_state_lstm2(segment_gen_lstm2_in_ORIGINAL)
segment_gen_out_ORIGINAL = segment_gen_out_ORIGINAL.squeeze(0)
mdn_out_ORIGINAL = self.gen_state_fc2(segment_gen_out_ORIGINAL)
eos_ORIGINAL = mdn_out_ORIGINAL[:,0:1]
[mu1_ORIGINAL, mu2_ORIGINAL, sig1_ORIGINAL, sig2_ORIGINAL, rho_ORIGINAL, pi_ORIGINAL] = torch.split(mdn_out_ORIGINAL[:,1:], self.num_mixtures, 1)
sig1_ORIGINAL = sig1_ORIGINAL.exp() + 1e-3
sig2_ORIGINAL = sig2_ORIGINAL.exp() + 1e-3
rho_ORIGINAL = self.mdn_tanh(rho_ORIGINAL)
pi_ORIGINAL = self.mdn_softmax(pi_ORIGINAL)
term_out_ORIGINAL = self.term_fc1(segment_gen_out_ORIGINAL)
term_out_ORIGINAL = self.term_relu1(term_out_ORIGINAL)
term_out_ORIGINAL = self.term_fc2(term_out_ORIGINAL)
term_out_ORIGINAL = self.term_relu2(term_out_ORIGINAL)
term_out_ORIGINAL = self.term_fc3(term_out_ORIGINAL)
term_pred_ORIGINAL = self.term_sigmoid(term_out_ORIGINAL)
gaussian_ORIGINAL = gaussian_2d(segment_level_target_x, segment_level_target_y, mu1_ORIGINAL, mu2_ORIGINAL, sig1_ORIGINAL, sig2_ORIGINAL, rho_ORIGINAL)
loss_gaussian_ORIGINAL = - torch.log(torch.sum(pi_ORIGINAL*gaussian_ORIGINAL, dim=1) + 1e-5)
ORIGINAL_segment_term_loss = self.term_bce_loss(term_out_ORIGINAL.squeeze(1), segment_level_target_term)
ORIGINAL_segment_loc_loss = torch.mean(loss_gaussian_ORIGINAL)
ORIGINAL_segment_touch_loss = self.mdn_bce_loss(eos_ORIGINAL.squeeze(1), segment_level_target_eos)
ORIGINAL_segment_termination_loss.append(ORIGINAL_segment_term_loss)
ORIGINAL_segment_loc_reconstruct_loss.append(ORIGINAL_segment_loc_loss)
ORIGINAL_segment_touch_reconstruct_loss.append(ORIGINAL_segment_touch_loss)
# TYPE A
if self.TYPE_A:
segment_C1 = segment_Cs_1[segment_batch_id]
segment_Wc_rec_TYPE_A = torch.bmm(segment_C1,
segment_W_mean.repeat(segment_C1.size(0),1).unsqueeze(2)).squeeze(-1)
segment_Wcs_reconstruct_TYPE_A.append(segment_Wc_rec_TYPE_A)
segment_W_lstm_in_TYPE_A = []
curr_id = 0
for i in range(user_segment_level_stroke_length[segment_batch_id][0]):
segment_W_lstm_in_TYPE_A.append(segment_Wc_rec_TYPE_A[curr_id])
if i in segment_SPLITS[segment_batch_id]:
curr_id += 1
segment_Wc_t_rec_TYPE_A = torch.stack(segment_W_lstm_in_TYPE_A)
segment_gen_lstm2_in_TYPE_A = torch.cat([segment_level_gen_encoded, segment_Wc_t_rec_TYPE_A], -1)
segment_gen_lstm2_in_TYPE_A = segment_gen_lstm2_in_TYPE_A.unsqueeze(0)
segment_gen_out_TYPE_A, (c,h) = self.gen_state_lstm2(segment_gen_lstm2_in_TYPE_A)
segment_gen_out_TYPE_A = segment_gen_out_TYPE_A.squeeze(0)
mdn_out_TYPE_A = self.gen_state_fc2(segment_gen_out_TYPE_A)
eos_TYPE_A = mdn_out_TYPE_A[:,0:1]
[mu1_TYPE_A, mu2_TYPE_A, sig1_TYPE_A, sig2_TYPE_A, rho_TYPE_A, pi_TYPE_A] = torch.split(mdn_out_TYPE_A[:,1:], self.num_mixtures, 1)
sig1_TYPE_A = sig1_TYPE_A.exp() + 1e-3
sig2_TYPE_A = sig2_TYPE_A.exp() + 1e-3
rho_TYPE_A = self.mdn_tanh(rho_TYPE_A)
pi_TYPE_A = self.mdn_softmax(pi_TYPE_A)
term_out_TYPE_A = self.term_fc1(segment_gen_out_TYPE_A)
term_out_TYPE_A = self.term_relu1(term_out_TYPE_A)
term_out_TYPE_A = self.term_fc2(term_out_TYPE_A)
term_out_TYPE_A = self.term_relu2(term_out_TYPE_A)
term_out_TYPE_A = self.term_fc3(term_out_TYPE_A)
term_pred_TYPE_A = self.term_sigmoid(term_out_TYPE_A)
gaussian_TYPE_A = gaussian_2d(segment_level_target_x, segment_level_target_y, mu1_TYPE_A, mu2_TYPE_A, sig1_TYPE_A, sig2_TYPE_A, rho_TYPE_A)
loss_gaussian_TYPE_A = - torch.log(torch.sum(pi_TYPE_A*gaussian_TYPE_A, dim=1) + 1e-5)
TYPE_A_segment_term_loss = self.term_bce_loss(term_out_TYPE_A.squeeze(1), segment_level_target_term)
TYPE_A_segment_loc_loss = torch.mean(loss_gaussian_TYPE_A)
TYPE_A_segment_touch_loss = self.mdn_bce_loss(eos_TYPE_A.squeeze(1), segment_level_target_eos)
TYPE_A_segment_termination_loss.append(TYPE_A_segment_term_loss)
TYPE_A_segment_loc_reconstruct_loss.append(TYPE_A_segment_loc_loss)
TYPE_A_segment_touch_reconstruct_loss.append(TYPE_A_segment_touch_loss)
# TYPE B
if self.TYPE_B:
unique_char_matrix_1 = segment_unique_char_matrices_1[segment_batch_id]
unique_char_matrices_1 = torch.stack(unique_char_matrix_1)
unique_char_matrices_1 = unique_char_matrices_1.squeeze(1)
# segment_W_c_TYPE_B_RAW = torch.bmm(unique_char_matrices, segment_W_mean.repeat(unique_char_matrices.size(0), 1).unsqueeze(2)).squeeze(-1)
segment_W_c_TYPE_B_RAW = torch.bmm(unique_char_matrices_1,
segment_W_mean.repeat(unique_char_matrices_1.size(0), 1).unsqueeze(2)).squeeze(-1)
segment_W_c_TYPE_B_RAW = segment_W_c_TYPE_B_RAW.unsqueeze(0)
segment_Wc_rec_TYPE_B, (c,h) = self.magic_lstm(segment_W_c_TYPE_B_RAW)
segment_Wc_rec_TYPE_B = segment_Wc_rec_TYPE_B.squeeze(0)
segment_Wcs_reconstruct_TYPE_B.append(segment_Wc_rec_TYPE_B)
segment_W_lstm_in_TYPE_B = []
curr_id = 0
for i in range(user_segment_level_stroke_length[segment_batch_id][0]):
segment_W_lstm_in_TYPE_B.append(segment_Wc_rec_TYPE_B[curr_id])
if i in segment_SPLITS[segment_batch_id]:
curr_id += 1
segment_Wc_t_rec_TYPE_B = torch.stack(segment_W_lstm_in_TYPE_B)
segment_gen_lstm2_in_TYPE_B = torch.cat([segment_level_gen_encoded, segment_Wc_t_rec_TYPE_B], -1)
segment_gen_lstm2_in_TYPE_B = segment_gen_lstm2_in_TYPE_B.unsqueeze(0)
segment_gen_out_TYPE_B, (c,h) = self.gen_state_lstm2(segment_gen_lstm2_in_TYPE_B)
segment_gen_out_TYPE_B = segment_gen_out_TYPE_B.squeeze(0)
mdn_out_TYPE_B = self.gen_state_fc2(segment_gen_out_TYPE_B)
eos_TYPE_B = mdn_out_TYPE_B[:,0:1]
[mu1_TYPE_B, mu2_TYPE_B, sig1_TYPE_B, sig2_TYPE_B, rho_TYPE_B, pi_TYPE_B] = torch.split(mdn_out_TYPE_B[:,1:], self.num_mixtures, 1)
sig1_TYPE_B = sig1_TYPE_B.exp() + 1e-3
sig2_TYPE_B = sig2_TYPE_B.exp() + 1e-3
rho_TYPE_B = self.mdn_tanh(rho_TYPE_B)
pi_TYPE_B = self.mdn_softmax(pi_TYPE_B)
term_out_TYPE_B = self.term_fc1(segment_gen_out_TYPE_B)
term_out_TYPE_B = self.term_relu1(term_out_TYPE_B)
term_out_TYPE_B = self.term_fc2(term_out_TYPE_B)
term_out_TYPE_B = self.term_relu2(term_out_TYPE_B)
term_out_TYPE_B = self.term_fc3(term_out_TYPE_B)
term_pred_TYPE_B = self.term_sigmoid(term_out_TYPE_B)
gaussian_TYPE_B = gaussian_2d(segment_level_target_x, segment_level_target_y, mu1_TYPE_B, mu2_TYPE_B, sig1_TYPE_B, sig2_TYPE_B, rho_TYPE_B)
loss_gaussian_TYPE_B = - torch.log(torch.sum(pi_TYPE_B*gaussian_TYPE_B, dim=1) + 1e-5)
TYPE_B_segment_term_loss = self.term_bce_loss(term_out_TYPE_B.squeeze(1), segment_level_target_term)
TYPE_B_segment_loc_loss = torch.mean(loss_gaussian_TYPE_B)
TYPE_B_segment_touch_loss = self.mdn_bce_loss(eos_TYPE_B.squeeze(1), segment_level_target_eos)
TYPE_B_segment_termination_loss.append(TYPE_B_segment_term_loss)
TYPE_B_segment_loc_reconstruct_loss.append(TYPE_B_segment_loc_loss)
TYPE_B_segment_touch_reconstruct_loss.append(TYPE_B_segment_touch_loss)
if self.ORIGINAL:
ALL_ORIGINAL_segment_termination_loss.append(torch.mean(torch.stack(ORIGINAL_segment_termination_loss)))
ALL_ORIGINAL_segment_loc_reconstruct_loss.append(torch.mean(torch.stack(ORIGINAL_segment_loc_reconstruct_loss)))
ALL_ORIGINAL_segment_touch_reconstruct_loss.append(torch.mean(torch.stack(ORIGINAL_segment_touch_reconstruct_loss)))
if self.TYPE_A:
ALL_TYPE_A_segment_termination_loss.append(torch.mean(torch.stack(TYPE_A_segment_termination_loss)))
ALL_TYPE_A_segment_loc_reconstruct_loss.append(torch.mean(torch.stack(TYPE_A_segment_loc_reconstruct_loss)))
ALL_TYPE_A_segment_touch_reconstruct_loss.append(torch.mean(torch.stack(TYPE_A_segment_touch_reconstruct_loss)))
if self.REC:
TYPE_A_segment_WC_reconstruct_loss = []
for segment_batch_id in range(len(segment_Wc_rec_ORIGINAL)):
segment_Wc_ORIGINAL = segment_Wc_rec_ORIGINAL[segment_batch_id]
segment_Wc_TYPE_A = segment_Wcs_reconstruct_TYPE_A[segment_batch_id]
segment_WC_reconstruct_loss_TYPE_A = torch.mean(torch.mean(torch.mul(segment_Wc_ORIGINAL - segment_Wc_TYPE_A, segment_Wc_ORIGINAL - segment_Wc_TYPE_A), -1))
TYPE_A_segment_WC_reconstruct_loss.append(segment_WC_reconstruct_loss_TYPE_A)
ALL_TYPE_A_segment_WC_reconstruct_loss.append(torch.mean(torch.stack(TYPE_A_segment_WC_reconstruct_loss)))
if self.TYPE_B:
ALL_TYPE_B_segment_termination_loss.append(torch.mean(torch.stack(TYPE_B_segment_termination_loss)))
ALL_TYPE_B_segment_loc_reconstruct_loss.append(torch.mean(torch.stack(TYPE_B_segment_loc_reconstruct_loss)))
ALL_TYPE_B_segment_touch_reconstruct_loss.append(torch.mean(torch.stack(TYPE_B_segment_touch_reconstruct_loss)))
if self.REC:
TYPE_B_segment_WC_reconstruct_loss = []
for segment_batch_id in range(len(segment_Wc_rec_ORIGINAL)):
segment_Wc_ORIGINAL = segment_Wc_rec_ORIGINAL[segment_batch_id]
segment_Wc_TYPE_B = segment_Wcs_reconstruct_TYPE_B[segment_batch_id]
segment_WC_reconstruct_loss_TYPE_B = torch.mean(torch.mean(torch.mul(segment_Wc_ORIGINAL - segment_Wc_TYPE_B, segment_Wc_ORIGINAL - segment_Wc_TYPE_B), -1))
TYPE_B_segment_WC_reconstruct_loss.append(segment_WC_reconstruct_loss_TYPE_B)
ALL_TYPE_B_segment_WC_reconstruct_loss.append(torch.mean(torch.stack(TYPE_B_segment_WC_reconstruct_loss)))
if self.TYPE_C:
# target
original_W_c = word_Wc_rec_ORIGINAL[word_batch_id]
word_Wc_rec_TYPE_C = []
for segment_batch_id in range(len(segment_Wc_rec_ORIGINAL)):
if segment_batch_id == 0:
for each_segment_Wc in segment_Wc_rec_ORIGINAL[segment_batch_id]:
word_Wc_rec_TYPE_C.append(each_segment_Wc)
prev_id = len(word_Wc_rec_TYPE_C) - 1
else:
prev_original_W_c = original_W_c[prev_id]
for each_segment_Wc in segment_Wc_rec_ORIGINAL[segment_batch_id]:
magic_inp = torch.stack([prev_original_W_c, each_segment_Wc])
magic_inp = magic_inp.unsqueeze(0)
type_c_out, (c,h) = self.magic_lstm(magic_inp)
type_c_out = type_c_out.squeeze(0)
word_Wc_rec_TYPE_C.append(type_c_out[-1])
prev_id = len(word_Wc_rec_TYPE_C) - 1
word_Wc_rec_TYPE_C = torch.stack(word_Wc_rec_TYPE_C)
word_Wcs_reconstruct_TYPE_C.append(word_Wc_rec_TYPE_C)
if len(word_Wc_rec_TYPE_C) == len(word_SPLITS[word_batch_id]):
word_W_lstm_in_TYPE_C = []
curr_id = 0
for i in range(user_word_level_stroke_length[word_batch_id][0]):
word_W_lstm_in_TYPE_C.append(word_Wc_rec_TYPE_C[curr_id])
if i in word_SPLITS[word_batch_id]:
curr_id += 1
word_Wc_t_rec_TYPE_C = torch.stack(word_W_lstm_in_TYPE_C)
word_gen_lstm2_in_TYPE_C = torch.cat([word_level_gen_encoded, word_Wc_t_rec_TYPE_C], -1)
word_gen_lstm2_in_TYPE_C = word_gen_lstm2_in_TYPE_C.unsqueeze(0)
word_gen_out_TYPE_C, (c,h) = self.gen_state_lstm2(word_gen_lstm2_in_TYPE_C)
word_gen_out_TYPE_C = word_gen_out_TYPE_C.squeeze(0)
mdn_out_TYPE_C = self.gen_state_fc2(word_gen_out_TYPE_C)
eos_TYPE_C = mdn_out_TYPE_C[:,0:1]
[mu1_TYPE_C, mu2_TYPE_C, sig1_TYPE_C, sig2_TYPE_C, rho_TYPE_C, pi_TYPE_C] = torch.split(mdn_out_TYPE_C[:,1:], self.num_mixtures, 1)
sig1_TYPE_C = sig1_TYPE_C.exp() + 1e-3
sig2_TYPE_C = sig2_TYPE_C.exp() + 1e-3
rho_TYPE_C = self.mdn_tanh(rho_TYPE_C)
pi_TYPE_C = self.mdn_softmax(pi_TYPE_C)
term_out_TYPE_C = self.term_fc1(word_gen_out_TYPE_C)
term_out_TYPE_C = self.term_relu1(term_out_TYPE_C)
term_out_TYPE_C = self.term_fc2(term_out_TYPE_C)
term_out_TYPE_C = self.term_relu2(term_out_TYPE_C)
term_out_TYPE_C = self.term_fc3(term_out_TYPE_C)
term_pred_TYPE_C = self.term_sigmoid(term_out_TYPE_C)
gaussian_TYPE_C = gaussian_2d(word_level_target_x, word_level_target_y, mu1_TYPE_C, mu2_TYPE_C, sig1_TYPE_C, sig2_TYPE_C, rho_TYPE_C)
loss_gaussian_TYPE_C = - torch.log(torch.sum(pi_TYPE_C*gaussian_TYPE_C, dim=1) + 1e-5)
TYPE_C_word_term_loss = self.term_bce_loss(term_out_TYPE_C.squeeze(1), word_level_target_term)
TYPE_C_word_loc_loss = torch.mean(loss_gaussian_TYPE_C)
TYPE_C_word_touch_loss = self.mdn_bce_loss(eos_TYPE_C.squeeze(1), word_level_target_eos)
TYPE_C_word_termination_loss.append(TYPE_C_word_term_loss)
TYPE_C_word_loc_reconstruct_loss.append(TYPE_C_word_loc_loss)
TYPE_C_word_touch_reconstruct_loss.append(TYPE_C_word_touch_loss)
else:
print ("not C")
if self.TYPE_D:
word_Wc_rec_TYPE_D = []
TYPE_D_REF = []
for segment_batch_id in range(len(segment_Wc_rec_ORIGINAL)):
if segment_batch_id == 0:
for each_segment_Wc in segment_Wc_rec_ORIGINAL[segment_batch_id]:
word_Wc_rec_TYPE_D.append(each_segment_Wc)
TYPE_D_REF.append(segment_Wc_rec_ORIGINAL[segment_batch_id][-1])
else:
for each_segment_Wc in segment_Wc_rec_ORIGINAL[segment_batch_id]:
magic_inp = torch.cat([torch.stack(TYPE_D_REF, 0), each_segment_Wc.unsqueeze(0)], 0)
magic_inp = magic_inp.unsqueeze(0)
TYPE_D_out, (c,h) = self.magic_lstm(magic_inp)
TYPE_D_out = TYPE_D_out.squeeze(0)
word_Wc_rec_TYPE_D.append(TYPE_D_out[-1])
TYPE_D_REF.append(segment_Wc_rec_ORIGINAL[segment_batch_id][-1])
word_Wc_rec_TYPE_D = torch.stack(word_Wc_rec_TYPE_D)
word_Wcs_reconstruct_TYPE_D.append(word_Wc_rec_TYPE_D)
if len(word_Wc_rec_TYPE_D) == len(word_SPLITS[word_batch_id]):
word_W_lstm_in_TYPE_D = []
curr_id = 0
for i in range(user_word_level_stroke_length[word_batch_id][0]):
word_W_lstm_in_TYPE_D.append(word_Wc_rec_TYPE_D[curr_id])
if i in word_SPLITS[word_batch_id]:
curr_id += 1
word_Wc_t_rec_TYPE_D = torch.stack(word_W_lstm_in_TYPE_D)
word_gen_lstm2_in_TYPE_D = torch.cat([word_level_gen_encoded, word_Wc_t_rec_TYPE_D], -1)
word_gen_lstm2_in_TYPE_D = word_gen_lstm2_in_TYPE_D.unsqueeze(0)
word_gen_out_TYPE_D, (c,h) = self.gen_state_lstm2(word_gen_lstm2_in_TYPE_D)
word_gen_out_TYPE_D = word_gen_out_TYPE_D.squeeze(0)
mdn_out_TYPE_D = self.gen_state_fc2(word_gen_out_TYPE_D)
eos_TYPE_D = mdn_out_TYPE_D[:,0:1]
[mu1_TYPE_D, mu2_TYPE_D, sig1_TYPE_D, sig2_TYPE_D, rho_TYPE_D, pi_TYPE_D] = torch.split(mdn_out_TYPE_D[:,1:], self.num_mixtures, 1)
sig1_TYPE_D = sig1_TYPE_D.exp() + 1e-3
sig2_TYPE_D = sig2_TYPE_D.exp() + 1e-3
rho_TYPE_D = self.mdn_tanh(rho_TYPE_D)
pi_TYPE_D = self.mdn_softmax(pi_TYPE_D)
term_out_TYPE_D = self.term_fc1(word_gen_out_TYPE_D)
term_out_TYPE_D = self.term_relu1(term_out_TYPE_D)
term_out_TYPE_D = self.term_fc2(term_out_TYPE_D)
term_out_TYPE_D = self.term_relu2(term_out_TYPE_D)
term_out_TYPE_D = self.term_fc3(term_out_TYPE_D)
term_pred_TYPE_D = self.term_sigmoid(term_out_TYPE_D)
gaussian_TYPE_D = gaussian_2d(word_level_target_x, word_level_target_y, mu1_TYPE_D, mu2_TYPE_D, sig1_TYPE_D, sig2_TYPE_D, rho_TYPE_D)
loss_gaussian_TYPE_D = - torch.log(torch.sum(pi_TYPE_D*gaussian_TYPE_D, dim=1) + 1e-5)
TYPE_D_word_term_loss = self.term_bce_loss(term_out_TYPE_D.squeeze(1), word_level_target_term)
TYPE_D_word_loc_loss = torch.mean(loss_gaussian_TYPE_D)
TYPE_D_word_touch_loss = self.mdn_bce_loss(eos_TYPE_D.squeeze(1), word_level_target_eos)
TYPE_D_word_termination_loss.append(TYPE_D_word_term_loss)
TYPE_D_word_loc_reconstruct_loss.append(TYPE_D_word_loc_loss)
TYPE_D_word_touch_reconstruct_loss.append(TYPE_D_word_touch_loss)
else:
print ("not D")
# word
if self.ORIGINAL:
ALL_ORIGINAL_word_termination_loss.append(torch.mean(torch.stack(ORIGINAL_word_termination_loss)))
ALL_ORIGINAL_word_loc_reconstruct_loss.append(torch.mean(torch.stack(ORIGINAL_word_loc_reconstruct_loss)))
ALL_ORIGINAL_word_touch_reconstruct_loss.append(torch.mean(torch.stack(ORIGINAL_word_touch_reconstruct_loss)))
if self.TYPE_A:
ALL_TYPE_A_word_termination_loss.append(torch.mean(torch.stack(TYPE_A_word_termination_loss)))
ALL_TYPE_A_word_loc_reconstruct_loss.append(torch.mean(torch.stack(TYPE_A_word_loc_reconstruct_loss)))
ALL_TYPE_A_word_touch_reconstruct_loss.append(torch.mean(torch.stack(TYPE_A_word_touch_reconstruct_loss)))
if self.REC:
TYPE_A_word_WC_reconstruct_loss = []
for word_batch_id in range(len(word_Wc_rec_ORIGINAL)):
word_Wc_ORIGINAL = word_Wc_rec_ORIGINAL[word_batch_id]
word_Wc_TYPE_A = word_Wcs_reconstruct_TYPE_A[word_batch_id]
if len(word_Wc_ORIGINAL) == len(word_Wc_TYPE_A):
word_WC_reconstruct_loss_TYPE_A = torch.mean(torch.mean(torch.mul(word_Wc_ORIGINAL - word_Wc_TYPE_A, word_Wc_ORIGINAL - word_Wc_TYPE_A), -1))
TYPE_A_word_WC_reconstruct_loss.append(word_WC_reconstruct_loss_TYPE_A)
if len(TYPE_A_word_WC_reconstruct_loss) > 0:
ALL_TYPE_A_word_WC_reconstruct_loss.append(torch.mean(torch.stack(TYPE_A_word_WC_reconstruct_loss)))
if self.TYPE_B:
ALL_TYPE_B_word_termination_loss.append(torch.mean(torch.stack(TYPE_B_word_termination_loss)))
ALL_TYPE_B_word_loc_reconstruct_loss.append(torch.mean(torch.stack(TYPE_B_word_loc_reconstruct_loss)))
ALL_TYPE_B_word_touch_reconstruct_loss.append(torch.mean(torch.stack(TYPE_B_word_touch_reconstruct_loss)))
if self.REC:
TYPE_B_word_WC_reconstruct_loss = []
for word_batch_id in range(len(word_Wc_rec_ORIGINAL)):
word_Wc_ORIGINAL = word_Wc_rec_ORIGINAL[word_batch_id]
word_Wc_TYPE_B = word_Wcs_reconstruct_TYPE_B[word_batch_id]
if len(word_Wc_ORIGINAL) == len(word_Wc_TYPE_B):
word_WC_reconstruct_loss_TYPE_B = torch.mean(torch.mean(torch.mul(word_Wc_ORIGINAL - word_Wc_TYPE_B, word_Wc_ORIGINAL - word_Wc_TYPE_B), -1))
TYPE_B_word_WC_reconstruct_loss.append(word_WC_reconstruct_loss_TYPE_B)
if len(TYPE_B_word_WC_reconstruct_loss) > 0:
ALL_TYPE_B_word_WC_reconstruct_loss.append(torch.mean(torch.stack(TYPE_B_word_WC_reconstruct_loss)))
if self.TYPE_C:
ALL_TYPE_C_word_termination_loss.append(torch.mean(torch.stack(TYPE_C_word_termination_loss)))
ALL_TYPE_C_word_loc_reconstruct_loss.append(torch.mean(torch.stack(TYPE_C_word_loc_reconstruct_loss)))
ALL_TYPE_C_word_touch_reconstruct_loss.append(torch.mean(torch.stack(TYPE_C_word_touch_reconstruct_loss)))
if self.REC:
TYPE_C_word_WC_reconstruct_loss = []
for word_batch_id in range(len(word_Wc_rec_ORIGINAL)):
word_Wc_ORIGINAL = word_Wc_rec_ORIGINAL[word_batch_id]
word_Wc_TYPE_C = word_Wcs_reconstruct_TYPE_C[word_batch_id]
if len(word_Wc_ORIGINAL) == len(word_Wc_TYPE_C):
word_WC_reconstruct_loss_TYPE_C = torch.mean(torch.mean(torch.mul(word_Wc_ORIGINAL - word_Wc_TYPE_C, word_Wc_ORIGINAL - word_Wc_TYPE_C), -1))
TYPE_C_word_WC_reconstruct_loss.append(word_WC_reconstruct_loss_TYPE_C)
if len(TYPE_C_word_WC_reconstruct_loss) > 0:
ALL_TYPE_C_word_WC_reconstruct_loss.append(torch.mean(torch.stack(TYPE_C_word_WC_reconstruct_loss)))
if self.TYPE_D:
ALL_TYPE_D_word_termination_loss.append(torch.mean(torch.stack(TYPE_D_word_termination_loss)))
ALL_TYPE_D_word_loc_reconstruct_loss.append(torch.mean(torch.stack(TYPE_D_word_loc_reconstruct_loss)))
ALL_TYPE_D_word_touch_reconstruct_loss.append(torch.mean(torch.stack(TYPE_D_word_touch_reconstruct_loss)))
if self.REC:
TYPE_D_word_WC_reconstruct_loss = []
for word_batch_id in range(len(word_Wc_rec_ORIGINAL)):
word_Wc_ORIGINAL = word_Wc_rec_ORIGINAL[word_batch_id]
word_Wc_TYPE_D = word_Wcs_reconstruct_TYPE_D[word_batch_id]
if len(word_Wc_ORIGINAL) == len(word_Wc_TYPE_D):
word_WC_reconstruct_loss_TYPE_D = torch.mean(torch.mean(torch.mul(word_Wc_ORIGINAL - word_Wc_TYPE_D, word_Wc_ORIGINAL - word_Wc_TYPE_D), -1))
TYPE_D_word_WC_reconstruct_loss.append(word_WC_reconstruct_loss_TYPE_D)
if len(TYPE_D_word_WC_reconstruct_loss) > 0:
ALL_TYPE_D_word_WC_reconstruct_loss.append(torch.mean(torch.stack(TYPE_D_word_WC_reconstruct_loss)))
# segment
if self.segment_loss:
SUPER_ALL_segment_W_consistency_loss.append(torch.mean(torch.stack(ALL_segment_W_consistency_loss)))
if self.ORIGINAL:
SUPER_ALL_ORIGINAL_segment_termination_loss.append(torch.mean(torch.stack(ALL_ORIGINAL_segment_termination_loss)))
SUPER_ALL_ORIGINAL_segment_loc_reconstruct_loss.append(torch.mean(torch.stack(ALL_ORIGINAL_segment_loc_reconstruct_loss)))
SUPER_ALL_ORIGINAL_segment_touch_reconstruct_loss.append(torch.mean(torch.stack(ALL_ORIGINAL_segment_touch_reconstruct_loss)))
if self.TYPE_A:
SUPER_ALL_TYPE_A_segment_termination_loss.append(torch.mean(torch.stack(ALL_TYPE_A_segment_termination_loss)))
SUPER_ALL_TYPE_A_segment_loc_reconstruct_loss.append(torch.mean(torch.stack(ALL_TYPE_A_segment_loc_reconstruct_loss)))
SUPER_ALL_TYPE_A_segment_touch_reconstruct_loss.append(torch.mean(torch.stack(ALL_TYPE_A_segment_touch_reconstruct_loss)))
if self.REC:
SUPER_ALL_TYPE_A_segment_WC_reconstruct_loss.append(torch.mean(torch.stack(ALL_TYPE_A_segment_WC_reconstruct_loss)))
if self.TYPE_B:
SUPER_ALL_TYPE_B_segment_termination_loss.append(torch.mean(torch.stack(ALL_TYPE_B_segment_termination_loss)))
SUPER_ALL_TYPE_B_segment_loc_reconstruct_loss.append(torch.mean(torch.stack(ALL_TYPE_B_segment_loc_reconstruct_loss)))
SUPER_ALL_TYPE_B_segment_touch_reconstruct_loss.append(torch.mean(torch.stack(ALL_TYPE_B_segment_touch_reconstruct_loss)))
if self.REC:
SUPER_ALL_TYPE_B_segment_WC_reconstruct_loss.append(torch.mean(torch.stack(ALL_TYPE_B_segment_WC_reconstruct_loss)))
total_sentence_loss = 0
sentence_losses = []
if self.sentence_loss:
mean_ORIGINAL_sentence_termination_loss = 0
mean_ORIGINAL_sentence_loc_reconstruct_loss = 0
mean_ORIGINAL_sentence_touch_reconstruct_loss = 0
mean_TYPE_A_sentence_termination_loss = 0
mean_TYPE_A_sentence_loc_reconstruct_loss = 0
mean_TYPE_A_sentence_touch_reconstruct_loss = 0
mean_TYPE_B_sentence_termination_loss = 0
mean_TYPE_B_sentence_loc_reconstruct_loss = 0
mean_TYPE_B_sentence_touch_reconstruct_loss = 0
mean_TYPE_A_sentence_WC_reconstruct_loss = 0
mean_TYPE_B_sentence_WC_reconstruct_loss = 0
mean_sentence_W_consistency_loss = torch.mean(torch.stack(ALL_sentence_W_consistency_loss))
if self.ORIGINAL:
mean_ORIGINAL_sentence_termination_loss = torch.mean(torch.stack(ALL_ORIGINAL_sentence_termination_loss))
mean_ORIGINAL_sentence_loc_reconstruct_loss = torch.mean(torch.stack(ALL_ORIGINAL_sentence_loc_reconstruct_loss))
mean_ORIGINAL_sentence_touch_reconstruct_loss = torch.mean(torch.stack(ALL_ORIGINAL_sentence_touch_reconstruct_loss))
if self.TYPE_A:
mean_TYPE_A_sentence_termination_loss = torch.mean(torch.stack(ALL_TYPE_A_sentence_termination_loss))
mean_TYPE_A_sentence_loc_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_A_sentence_loc_reconstruct_loss))
mean_TYPE_A_sentence_touch_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_A_sentence_touch_reconstruct_loss))
if self.REC:
mean_TYPE_A_sentence_WC_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_A_sentence_WC_reconstruct_loss))
if self.TYPE_B:
mean_TYPE_B_sentence_termination_loss = torch.mean(torch.stack(ALL_TYPE_B_sentence_termination_loss))
mean_TYPE_B_sentence_loc_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_B_sentence_loc_reconstruct_loss))
mean_TYPE_B_sentence_touch_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_B_sentence_touch_reconstruct_loss))
if self.REC:
mean_TYPE_B_sentence_WC_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_B_sentence_WC_reconstruct_loss))
total_sentence_loss = mean_sentence_W_consistency_loss + mean_ORIGINAL_sentence_termination_loss + mean_ORIGINAL_sentence_loc_reconstruct_loss + mean_ORIGINAL_sentence_touch_reconstruct_loss + mean_TYPE_A_sentence_termination_loss + mean_TYPE_A_sentence_loc_reconstruct_loss + mean_TYPE_A_sentence_touch_reconstruct_loss + mean_TYPE_B_sentence_termination_loss + mean_TYPE_B_sentence_loc_reconstruct_loss + mean_TYPE_B_sentence_touch_reconstruct_loss + mean_TYPE_A_sentence_WC_reconstruct_loss + mean_TYPE_B_sentence_WC_reconstruct_loss
sentence_losses = [total_sentence_loss, mean_sentence_W_consistency_loss, mean_ORIGINAL_sentence_termination_loss, mean_ORIGINAL_sentence_loc_reconstruct_loss, mean_ORIGINAL_sentence_touch_reconstruct_loss, mean_TYPE_A_sentence_termination_loss, mean_TYPE_A_sentence_loc_reconstruct_loss, mean_TYPE_A_sentence_touch_reconstruct_loss, mean_TYPE_B_sentence_termination_loss, mean_TYPE_B_sentence_loc_reconstruct_loss, mean_TYPE_B_sentence_touch_reconstruct_loss, mean_TYPE_A_sentence_WC_reconstruct_loss, mean_TYPE_B_sentence_WC_reconstruct_loss]
total_word_loss = 0
word_losses = []
if self.word_loss:
mean_ORIGINAL_word_termination_loss = 0
mean_ORIGINAL_word_loc_reconstruct_loss = 0
mean_ORIGINAL_word_touch_reconstruct_loss = 0
mean_TYPE_A_word_termination_loss = 0
mean_TYPE_A_word_loc_reconstruct_loss = 0
mean_TYPE_A_word_touch_reconstruct_loss = 0
mean_TYPE_B_word_termination_loss = 0
mean_TYPE_B_word_loc_reconstruct_loss = 0
mean_TYPE_B_word_touch_reconstruct_loss = 0
mean_TYPE_C_word_termination_loss = 0
mean_TYPE_C_word_loc_reconstruct_loss = 0
mean_TYPE_C_word_touch_reconstruct_loss = 0
mean_TYPE_D_word_termination_loss = 0
mean_TYPE_D_word_loc_reconstruct_loss = 0
mean_TYPE_D_word_touch_reconstruct_loss = 0
mean_TYPE_A_word_WC_reconstruct_loss = 0
mean_TYPE_B_word_WC_reconstruct_loss = 0
mean_TYPE_C_word_WC_reconstruct_loss = 0
mean_TYPE_D_word_WC_reconstruct_loss = 0
mean_word_W_consistency_loss = torch.mean(torch.stack(ALL_word_W_consistency_loss))
if self.ORIGINAL:
mean_ORIGINAL_word_termination_loss = torch.mean(torch.stack(ALL_ORIGINAL_word_termination_loss))
mean_ORIGINAL_word_loc_reconstruct_loss = torch.mean(torch.stack(ALL_ORIGINAL_word_loc_reconstruct_loss))
mean_ORIGINAL_word_touch_reconstruct_loss = torch.mean(torch.stack(ALL_ORIGINAL_word_touch_reconstruct_loss))
if self.TYPE_A:
mean_TYPE_A_word_termination_loss = torch.mean(torch.stack(ALL_TYPE_A_word_termination_loss))
mean_TYPE_A_word_loc_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_A_word_loc_reconstruct_loss))
mean_TYPE_A_word_touch_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_A_word_touch_reconstruct_loss))
if self.REC:
mean_TYPE_A_word_WC_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_A_word_WC_reconstruct_loss))
if self.TYPE_B:
mean_TYPE_B_word_termination_loss = torch.mean(torch.stack(ALL_TYPE_B_word_termination_loss))
mean_TYPE_B_word_loc_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_B_word_loc_reconstruct_loss))
mean_TYPE_B_word_touch_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_B_word_touch_reconstruct_loss))
if self.REC:
mean_TYPE_B_word_WC_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_B_word_WC_reconstruct_loss))
if self.TYPE_C:
mean_TYPE_C_word_termination_loss = torch.mean(torch.stack(ALL_TYPE_C_word_termination_loss))
mean_TYPE_C_word_loc_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_C_word_loc_reconstruct_loss))
mean_TYPE_C_word_touch_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_C_word_touch_reconstruct_loss))
if self.REC:
mean_TYPE_C_word_WC_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_C_word_WC_reconstruct_loss))
if self.TYPE_D:
mean_TYPE_D_word_termination_loss = torch.mean(torch.stack(ALL_TYPE_D_word_termination_loss))
mean_TYPE_D_word_loc_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_D_word_loc_reconstruct_loss))
mean_TYPE_D_word_touch_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_D_word_touch_reconstruct_loss))
if self.REC:
mean_TYPE_D_word_WC_reconstruct_loss = torch.mean(torch.stack(ALL_TYPE_D_word_WC_reconstruct_loss))
total_word_loss = mean_word_W_consistency_loss + mean_ORIGINAL_word_termination_loss + mean_ORIGINAL_word_loc_reconstruct_loss + mean_ORIGINAL_word_touch_reconstruct_loss + mean_TYPE_A_word_termination_loss + mean_TYPE_A_word_loc_reconstruct_loss + mean_TYPE_A_word_touch_reconstruct_loss + mean_TYPE_B_word_termination_loss + mean_TYPE_B_word_loc_reconstruct_loss + mean_TYPE_B_word_touch_reconstruct_loss + mean_TYPE_C_word_termination_loss + mean_TYPE_C_word_loc_reconstruct_loss + mean_TYPE_C_word_touch_reconstruct_loss + mean_TYPE_D_word_termination_loss + mean_TYPE_D_word_loc_reconstruct_loss + mean_TYPE_D_word_touch_reconstruct_loss + mean_TYPE_A_word_WC_reconstruct_loss + mean_TYPE_B_word_WC_reconstruct_loss + mean_TYPE_C_word_WC_reconstruct_loss + mean_TYPE_D_word_WC_reconstruct_loss
word_losses = [total_word_loss, mean_word_W_consistency_loss, mean_ORIGINAL_word_termination_loss, mean_ORIGINAL_word_loc_reconstruct_loss, mean_ORIGINAL_word_touch_reconstruct_loss, mean_TYPE_A_word_termination_loss, mean_TYPE_A_word_loc_reconstruct_loss, mean_TYPE_A_word_touch_reconstruct_loss, mean_TYPE_B_word_termination_loss, mean_TYPE_B_word_loc_reconstruct_loss, mean_TYPE_B_word_touch_reconstruct_loss, mean_TYPE_C_word_termination_loss, mean_TYPE_C_word_loc_reconstruct_loss, mean_TYPE_C_word_touch_reconstruct_loss, mean_TYPE_D_word_termination_loss, mean_TYPE_D_word_loc_reconstruct_loss, mean_TYPE_D_word_touch_reconstruct_loss, mean_TYPE_A_word_WC_reconstruct_loss, mean_TYPE_B_word_WC_reconstruct_loss, mean_TYPE_C_word_WC_reconstruct_loss, mean_TYPE_D_word_WC_reconstruct_loss]
total_segment_loss = 0
segment_losses = []
if self.segment_loss:
mean_segment_W_consistency_loss = torch.mean(torch.stack(SUPER_ALL_segment_W_consistency_loss))
mean_ORIGINAL_segment_termination_loss = 0
mean_ORIGINAL_segment_loc_reconstruct_loss = 0
mean_ORIGINAL_segment_touch_reconstruct_loss = 0
mean_TYPE_A_segment_termination_loss = 0
mean_TYPE_A_segment_loc_reconstruct_loss = 0
mean_TYPE_A_segment_touch_reconstruct_loss = 0
mean_TYPE_B_segment_termination_loss = 0
mean_TYPE_B_segment_loc_reconstruct_loss = 0
mean_TYPE_B_segment_touch_reconstruct_loss = 0
mean_TYPE_A_segment_WC_reconstruct_loss = 0
mean_TYPE_B_segment_WC_reconstruct_loss = 0
if self.ORIGINAL:
mean_ORIGINAL_segment_termination_loss = torch.mean(torch.stack(SUPER_ALL_ORIGINAL_segment_termination_loss))
mean_ORIGINAL_segment_loc_reconstruct_loss = torch.mean(torch.stack(SUPER_ALL_ORIGINAL_segment_loc_reconstruct_loss))
mean_ORIGINAL_segment_touch_reconstruct_loss = torch.mean(torch.stack(SUPER_ALL_ORIGINAL_segment_touch_reconstruct_loss))
if self.TYPE_A:
mean_TYPE_A_segment_termination_loss = torch.mean(torch.stack(SUPER_ALL_TYPE_A_segment_termination_loss))
mean_TYPE_A_segment_loc_reconstruct_loss = torch.mean(torch.stack(SUPER_ALL_TYPE_A_segment_loc_reconstruct_loss))
mean_TYPE_A_segment_touch_reconstruct_loss = torch.mean(torch.stack(SUPER_ALL_TYPE_A_segment_touch_reconstruct_loss))
if self.REC:
mean_TYPE_A_segment_WC_reconstruct_loss = torch.mean(torch.stack(SUPER_ALL_TYPE_A_segment_WC_reconstruct_loss))
if self.TYPE_B:
mean_TYPE_B_segment_termination_loss = torch.mean(torch.stack(SUPER_ALL_TYPE_B_segment_termination_loss))
mean_TYPE_B_segment_loc_reconstruct_loss = torch.mean(torch.stack(SUPER_ALL_TYPE_B_segment_loc_reconstruct_loss))
mean_TYPE_B_segment_touch_reconstruct_loss = torch.mean(torch.stack(SUPER_ALL_TYPE_B_segment_touch_reconstruct_loss))
if self.REC:
mean_TYPE_B_segment_WC_reconstruct_loss = torch.mean(torch.stack(SUPER_ALL_TYPE_B_segment_WC_reconstruct_loss))
total_segment_loss = mean_segment_W_consistency_loss + mean_ORIGINAL_segment_termination_loss + mean_ORIGINAL_segment_loc_reconstruct_loss + mean_ORIGINAL_segment_touch_reconstruct_loss + mean_TYPE_A_segment_termination_loss + mean_TYPE_A_segment_loc_reconstruct_loss + mean_TYPE_A_segment_touch_reconstruct_loss + mean_TYPE_B_segment_termination_loss + mean_TYPE_B_segment_loc_reconstruct_loss + mean_TYPE_B_segment_touch_reconstruct_loss + mean_TYPE_A_segment_WC_reconstruct_loss + mean_TYPE_B_segment_WC_reconstruct_loss
segment_losses = [total_segment_loss, mean_segment_W_consistency_loss, mean_ORIGINAL_segment_termination_loss, mean_ORIGINAL_segment_loc_reconstruct_loss, mean_ORIGINAL_segment_touch_reconstruct_loss, mean_TYPE_A_segment_termination_loss, mean_TYPE_A_segment_loc_reconstruct_loss, mean_TYPE_A_segment_touch_reconstruct_loss, mean_TYPE_B_segment_termination_loss, mean_TYPE_B_segment_loc_reconstruct_loss, mean_TYPE_B_segment_touch_reconstruct_loss, mean_TYPE_A_segment_WC_reconstruct_loss, mean_TYPE_B_segment_WC_reconstruct_loss]
total_loss = total_sentence_loss + total_word_loss + total_segment_loss
return total_loss, sentence_losses, word_losses, segment_losses
def sample(self, inputs):
[ word_level_stroke_in, word_level_stroke_out, word_level_stroke_length,
word_level_term, word_level_char, word_level_char_length, segment_level_stroke_in,
segment_level_stroke_out, segment_level_stroke_length, segment_level_term,
segment_level_char, segment_level_char_length ] = inputs
word_inf_state_out = self.inf_state_fc1(word_level_stroke_out[0])
word_inf_state_out = self.inf_state_relu(word_inf_state_out)
word_inf_state_out, (c,h) = self.inf_state_lstm(word_inf_state_out)
user_word_level_char = word_level_char[0]
user_word_level_term = word_level_term[0]
raw_Ws = []
original_Wc = []
word_batch_id = 0
# ORIGINAL
curr_seq_len = word_level_stroke_length[0][word_batch_id][0]
curr_char_len = word_level_char_length[0][word_batch_id][0]
char_vector = torch.eye(len(CHARACTERS))[user_word_level_char[word_batch_id][:curr_char_len]].to(self.device)
current_term = user_word_level_term[word_batch_id][:curr_seq_len].unsqueeze(-1)
split_ids = torch.nonzero(current_term)[:,0]
# char_vector = self.char_vec_fc(char_vector)
# char_vector = self.char_vec_relu(char_vector)
char_vector_1 = self.char_vec_fc_1(char_vector)
char_vector_1 = self.char_vec_relu_1(char_vector_1)
# unique_char_matrices = []
# for cid in range(len(char_vector)):
# unique_char_vector = char_vector[cid:cid+1]
# unique_char_out = unique_char_vector.unsqueeze(0)
# unique_char_out, (c,h) = self.char_lstm(unique_char_out)
# unique_char_out = unique_char_out.squeeze(0)
# unique_char_out = self.char_vec_fc2(unique_char_out)
# unique_char_matrix = unique_char_out.view([-1,1,self.weight_dim,self.weight_dim])
# unique_char_matrix = unique_char_matrix.squeeze(1)
# unique_char_matrices.append(unique_char_matrix)
unique_char_matrices_1 = []
for cid in range(len(char_vector)):
# Tower 1
unique_char_vector_1 = char_vector_1[cid:cid+1]
unique_char_input_1 = unique_char_vector_1.unsqueeze(0)
unique_char_out_1, (c,h) = self.char_lstm_1(unique_char_input_1)
unique_char_out_1 = unique_char_out_1.squeeze(0)
unique_char_out_1 = self.char_vec_fc2_1(unique_char_out_1)
unique_char_matrix_1 = unique_char_out_1.view([-1,1,self.weight_dim,self.weight_dim])
unique_char_matrix_1 = unique_char_matrix_1.squeeze(1)
unique_char_matrices_1.append(unique_char_matrix_1)
# Tower 1
char_out_1 = char_vector_1.unsqueeze(0)
char_out_1, (c,h) = self.char_lstm_1(char_out_1)
char_out_1 = char_out_1.squeeze(0)
char_out_1 = self.char_vec_fc2_1(char_out_1)
char_matrix_1 = char_out_1.view([-1,1,self.weight_dim,self.weight_dim])
char_matrix_1 = char_matrix_1.squeeze(1)
char_matrix_inv_1 = torch.inverse(char_matrix_1)
W_c_t = word_inf_state_out[word_batch_id][:curr_seq_len]
W_c = torch.stack([W_c_t[i] for i in split_ids])
original_Wc.append(W_c)
W = torch.bmm(char_matrix_inv_1,
W_c.unsqueeze(2)).squeeze(-1)
user_segment_level_stroke_length = segment_level_stroke_length[0][word_batch_id]
user_segment_level_char_length = segment_level_char_length[0][word_batch_id]
user_segment_level_term = segment_level_term[0][word_batch_id]
user_segment_level_char = segment_level_char[0][word_batch_id]
user_segment_level_stroke_in = segment_level_stroke_in[0][word_batch_id]
user_segment_level_stroke_out = segment_level_stroke_out[0][word_batch_id]
segment_inf_state_out = self.inf_state_fc1(user_segment_level_stroke_out)
segment_inf_state_out = self.inf_state_relu(segment_inf_state_out)
segment_inf_state_out, (c,h) = self.inf_state_lstm(segment_inf_state_out)
segment_W_c = []
for segment_batch_id in range(len(user_segment_level_char)):
curr_seq_len = user_segment_level_stroke_length[segment_batch_id][0]
curr_char_len = user_segment_level_char_length[segment_batch_id][0]
current_term = user_segment_level_term[segment_batch_id][:curr_seq_len].unsqueeze(-1)
split_ids = torch.nonzero(current_term)[:,0]
seg_W_c_t = segment_inf_state_out[segment_batch_id][:curr_seq_len]
seg_W_c = torch.stack([seg_W_c_t[i] for i in split_ids])
segment_W_c.append(seg_W_c)
target_characters_ids = word_level_char[0][0][:word_level_char_length[0][0]]
target_characters = ''.join([CHARACTERS[i] for i in target_characters_ids])
mean_global_W = torch.mean(W, 0)
TYPE_A_WC = torch.bmm(char_matrix_1,
mean_global_W.repeat(char_matrix_1.size(0), 1).unsqueeze(2)).squeeze(-1)
unique_char_matrix_1 = torch.stack(unique_char_matrices_1)
unique_char_matrix_1 = unique_char_matrix_1.squeeze(1)
TYPE_B_WC_RAW = torch.bmm(unique_char_matrix_1,
mean_global_W.repeat(unique_char_matrix_1.size(0), 1).unsqueeze(2)).squeeze(-1)
TYPE_B_WC_RAW = TYPE_B_WC_RAW.unsqueeze(0)
TYPE_B_WC, (c,h) = self.magic_lstm(TYPE_B_WC_RAW)
TYPE_B_WC = TYPE_B_WC.squeeze(0)
# CC
TYPE_C_WC = []
for segment_batch_id in range(len(segment_W_c)):
if segment_batch_id == 0:
for each_segment_Wc in segment_W_c[segment_batch_id]:
TYPE_C_WC.append(each_segment_Wc)
prev_id = len(TYPE_C_WC) - 1
else:
prev_original_W_c = W_c[prev_id]
for each_segment_Wc in segment_W_c[segment_batch_id]:
magic_inp = torch.stack([prev_original_W_c, each_segment_Wc])
magic_inp = magic_inp.unsqueeze(0)
type_c_out, (c,h) = self.magic_lstm(magic_inp)
type_c_out = type_c_out.squeeze(0)
TYPE_C_WC.append(type_c_out[-1])
prev_id = len(TYPE_C_WC) - 1
TYPE_C_WC = torch.stack(TYPE_C_WC)
# DD
TYPE_D_WC = []
TYPE_D_REF = []
for segment_batch_id in range(len(segment_W_c)):
if segment_batch_id == 0:
for each_segment_Wc in segment_W_c[segment_batch_id]:
TYPE_D_WC.append(each_segment_Wc)
TYPE_D_REF.append(segment_W_c[segment_batch_id][-1])
else:
for each_segment_Wc in segment_W_c[segment_batch_id]:
magic_inp = torch.cat([torch.stack(TYPE_D_REF, 0), each_segment_Wc.unsqueeze(0)], 0)
magic_inp = magic_inp.unsqueeze(0)
TYPE_D_out, (c,h) = self.magic_lstm(magic_inp)
TYPE_D_out = TYPE_D_out.squeeze(0)
TYPE_D_WC.append(TYPE_D_out[-1])
TYPE_D_REF.append(segment_W_c[segment_batch_id][-1])
TYPE_D_WC = torch.stack(TYPE_D_WC)
o_tc = ''.join([CHARACTERS[c] for c in word_level_char[0][0][:word_level_char_length[0][0]]])
o_commands = self.sample_from_w(original_Wc[0], o_tc)
if len(TYPE_A_WC) == len(original_Wc[0]):
a_commands = self.sample_from_w(TYPE_A_WC, target_characters)
else:
a_commands = [[0,0,0]]
if len(TYPE_B_WC) == len(original_Wc[0]):
b_commands = self.sample_from_w(TYPE_B_WC, target_characters)
else:
b_commands = [[0,0,0]]
if len(TYPE_C_WC) == len(original_Wc[0]):
c_commands = self.sample_from_w(TYPE_C_WC, target_characters)
else:
c_commands = [[0,0,0]]
if len(TYPE_D_WC) == len(original_Wc[0]):
d_commands = self.sample_from_w(TYPE_D_WC, target_characters)
else:
d_commands = [[0,0,0]]
return [word_level_stroke_out[0][0], o_commands, a_commands, b_commands, c_commands, d_commands]
def sample_from_w(self, W_c_rec, target_sentence):
gen_input = torch.zeros([1, 1, 3]).to(self.device)
current_char_id_count = 0
gc1 = torch.zeros([self.num_layers, 1, self.weight_dim]).to(self.device)
gh1 = torch.zeros([self.num_layers, 1, self.weight_dim]).to(self.device)
gc2 = torch.zeros([self.num_layers, 1, self.weight_dim * 2]).to(self.device)
gh2 = torch.zeros([self.num_layers, 1, self.weight_dim * 2]).to(self.device)
terms = []
commands = []
character_nums = 0
cx, cy = 100, 150
for zz in range(800):
W_c_t_now = W_c_rec[current_char_id_count:current_char_id_count + 1]
gen_state = self.gen_state_fc1(gen_input)
gen_state = self.gen_state_relu(gen_state)
gen_state, (gc1, gh1) = self.gen_state_lstm1(gen_state, (gc1, gh1))
gen_encoded = gen_state.squeeze(0)
gen_lstm2_input = torch.cat([gen_encoded, W_c_t_now], -1)
gen_lstm2_input = gen_lstm2_input.view([1, 1, self.weight_dim * 2])
gen_out, (gc2, gh2) = self.gen_state_lstm2(gen_lstm2_input, (gc2, gh2))
gen_out = gen_out.squeeze(0)
mdn_out = self.gen_state_fc2(gen_out)
term_out = self.term_fc1(gen_out)
term_out = self.term_relu1(term_out)
term_out = self.term_fc2(term_out)
term_out = self.term_relu2(term_out)
term_out = self.term_fc3(term_out)
term = self.term_sigmoid(term_out)
eos = self.mdn_sigmoid(mdn_out[:, 0])
[mu1, mu2, sig1, sig2, rho, pi] = torch.split(mdn_out[:, 1:], self.num_mixtures, 1)
sig1 = sig1.exp() + 1e-3
sig2 = sig2.exp() + 1e-3
rho = self.mdn_tanh(rho)
pi = self.mdn_softmax(pi)
mus = torch.stack([mu1, mu2], -1).squeeze()
pi = pi.cpu().detach().numpy()
mus = mus.cpu().detach().numpy()
rho = rho.cpu().detach().numpy()[0]
eos = eos.cpu().detach().numpy()[0]
term = term.cpu().detach().numpy()[0][0]
terms.append(term)
[dx, dy] = np.sum(pi.reshape(20, 1) * mus, 0)
# print (eos)
touch = 1 if eos > 0.5 else 0
commands.append([dx, dy, touch])
gen_input = torch.FloatTensor([dx, dy, touch]).view([1, 1, 3]).to(self.device)
character_nums += 1
# print (zz, term)
if term > 0.3:
if target_sentence[current_char_id_count] == ' ':
current_char_id_count += 1
character_nums = 0
if current_char_id_count == len(W_c_rec):
break
elif character_nums > 5:
current_char_id_count += 1
character_nums = 0
if current_char_id_count == len(W_c_rec):
break
cx += dx * 2.0 * 5.0
cy += dy * 2.0 * 5.0
if cx > 1000 or cx < 0:
break
if cy > 350 or cy < 0:
break
return commands
def sample_from_w_fix(self, W_c_rec):
gen_input = torch.zeros([1, 1, 3]).to(self.device)
current_char_id_count = 0
gc1 = torch.zeros([self.num_layers, 1, self.weight_dim]).to(self.device)
gh1 = torch.zeros([self.num_layers, 1, self.weight_dim]).to(self.device)
gc2 = torch.zeros([self.num_layers, 1, self.weight_dim * 2]).to(self.device)
gh2 = torch.zeros([self.num_layers, 1, self.weight_dim * 2]).to(self.device)
terms = []
commands = []
character_nums = 0
cx, cy = 100, 150
new_char = False
renewal = False
for zz in range(800):
# print (torch.sum(gc1))
W_c_t_now = W_c_rec[current_char_id_count:current_char_id_count + 1]
gen_state = self.gen_state_fc1(gen_input)
gen_state = self.gen_state_relu(gen_state)
gen_state, (gc1, gh1) = self.gen_state_lstm1(gen_state, (gc1, gh1))
gen_encoded = gen_state.squeeze(0)
gen_lstm2_input = torch.cat([gen_encoded, W_c_t_now], -1)
gen_lstm2_input = gen_lstm2_input.view([1, 1, self.weight_dim * 2])
gen_out, (gc2, gh2) = self.gen_state_lstm2(gen_lstm2_input, (gc2, gh2))
gen_out = gen_out.squeeze(0)
mdn_out = self.gen_state_fc2(gen_out)
term_out = self.term_fc1(gen_out)
term_out = self.term_relu1(term_out)
term_out = self.term_fc2(term_out)
term_out = self.term_relu2(term_out)
term_out = self.term_fc3(term_out)
term = self.term_sigmoid(term_out)
eos = self.mdn_sigmoid(mdn_out[:, 0])
[mu1, mu2, sig1, sig2, rho, pi] = torch.split(mdn_out[:, 1:], self.num_mixtures, 1)
sig1 = sig1.exp() + 1e-3
sig2 = sig2.exp() + 1e-3
rho = self.mdn_tanh(rho)
pi = self.mdn_softmax(pi)
mus = torch.stack([mu1, mu2], -1).squeeze()
sigs = torch.stack([sig1, sig2], -1).squeeze() * self.scale_sd
distribution = torch.distributions.normal.Normal(loc=mus, scale=sigs)
sample = distribution.sample()
min_clamp = distribution.icdf(0.5 - torch.ones_like(mus) * self.clamp_mdn/2)
max_clamp = distribution.icdf(0.5 + torch.ones_like(mus) * self.clamp_mdn/2)
sample = sample.clamp(min=min_clamp, max=max_clamp)
pi = pi.cpu().detach().numpy()
mus = mus.cpu().detach().numpy()
rho = rho.cpu().detach().numpy()[0]
eos = eos.cpu().detach().numpy()[0]
term = term.cpu().detach().numpy()[0][0]
sample = sample.cpu().detach().numpy()
terms.append(term)
[dx, dy] = np.sum(pi.reshape(20, 1) * sample, 0)
touch = 1 if eos > 0.5 else 0
if new_char and touch == 1:
new_char = False
commands.append([dx, dy, touch])
return commands, current_char_id_count
else:
commands.append([dx, dy, touch])
gen_input = torch.FloatTensor([dx, dy, touch]).view([1, 1, 3]).to(self.device)
character_nums += 1
# print (zz, term)
if term > 0.5:
if character_nums > 5:
current_char_id_count += 1
character_nums = 0
new_char = True
if current_char_id_count == len(W_c_rec):
break
cx += dx * 2.0 * 5.0
cy += dy * 2.0 * 5.0
if cx > 1000 or cx < 0:
break
if cy > 350 or cy < 0:
break
return commands, -1 |