diff --git a/DataLoader.py b/DataLoader.py new file mode 100644 index 0000000000000000000000000000000000000000..da2de2d86517c2e86049824f67b29e0fcc12690e --- /dev/null +++ b/DataLoader.py @@ -0,0 +1,278 @@ +import os +import numpy as np +import torch +import random +from PIL import Image, ImageDraw, ImageFont +import pickle +from config.GlobalVariables import * + +np.random.seed(0) + +class DataLoader(): + def __init__(self, num_writer=2, num_samples=5, divider=10.0, datadir='./data/writers'): + self.device = 'cuda' if torch.cuda.is_available() else 'cpu' + self.num_writer = num_writer + self.num_samples = num_samples + self.divider = divider + self.datadir = datadir + print ('self.datadir : ', self.datadir) + self.total_writers = len([name for name in os.listdir(datadir)]) + + def next_batch(self, TYPE='TRAIN', uid=-1, tids=[]): + all_sentence_level_stroke_in = [] + all_sentence_level_stroke_out = [] + all_sentence_level_stroke_length = [] + all_sentence_level_term = [] + all_sentence_level_char = [] + all_sentence_level_char_length = [] + all_word_level_stroke_in = [] + all_word_level_stroke_out = [] + all_word_level_stroke_length = [] + all_word_level_term = [] + all_word_level_char = [] + all_word_level_char_length = [] + all_segment_level_stroke_in = [] + all_segment_level_stroke_out = [] + all_segment_level_stroke_length = [] + all_segment_level_term = [] + all_segment_level_char = [] + all_segment_level_char_length = [] + + while len(all_sentence_level_stroke_in) < self.num_writer: + if uid < 0: + if TYPE == 'TRAIN': + if self.datadir == './data/NEW_writers' or self.datadir == './data/writers': + uid = np.random.choice([i for i in range(150)]) + else: + if self.device == 'cpu': + uid = np.random.choice([i for i in range(20)]) + else: + uid = np.random.choice([i for i in range(294)]) + else: + uid = np.random.choice([i for i in range(150,170)]) + + total_texts = len([name for name in os.listdir(self.datadir+'/'+str(uid))]) + if len(tids) == 0: + tids = random.sample([i for i in range(total_texts)], self.num_samples) + + user_sentence_level_stroke_in = [] + user_sentence_level_stroke_out = [] + user_sentence_level_stroke_length = [] + user_sentence_level_term = [] + user_sentence_level_char = [] + user_sentence_level_char_length = [] + user_word_level_stroke_in = [] + user_word_level_stroke_out = [] + user_word_level_stroke_length = [] + user_word_level_term = [] + user_word_level_char = [] + user_word_level_char_length = [] + user_segment_level_stroke_in = [] + user_segment_level_stroke_out = [] + user_segment_level_stroke_length = [] + user_segment_level_term = [] + user_segment_level_char = [] + user_segment_level_char_length = [] + + # print ("uid: ", uid, "\ttids:", tids) + for tid in tids: + if self.datadir == './data/NEW_writers': + [sentence_level_raw_stroke, sentence_level_stroke_in, sentence_level_stroke_out, sentence_level_term, sentence_level_char, word_level_raw_stroke, word_level_stroke_in, word_level_stroke_out, word_level_term, word_level_char, segment_level_raw_stroke, segment_level_stroke_in, segment_level_stroke_out, segment_level_term, segment_level_char] = \ + np.load(self.datadir+'/'+str(uid)+'/'+str(tid)+'.npy', allow_pickle=True, encoding='bytes') + + elif self.datadir == './data/DW_writers': + [sentence_level_raw_stroke, sentence_level_char, sentence_level_term, sentence_level_stroke_in, sentence_level_stroke_out, + word_level_raw_stroke, word_level_char, word_level_term, word_level_stroke_in, word_level_stroke_out, + segment_level_raw_stroke, segment_level_char, segment_level_term, segment_level_stroke_in, segment_level_stroke_out, _] = \ + np.load(self.datadir+'/'+str(uid)+'/'+str(tid)+'.npy', allow_pickle=True, encoding='bytes') + + elif self.datadir == './data/VALID_DW_writers': + [sentence_level_raw_stroke, sentence_level_char, sentence_level_term, sentence_level_stroke_in, sentence_level_stroke_out, + word_level_raw_stroke, word_level_char, word_level_term, word_level_stroke_in, word_level_stroke_out, + segment_level_raw_stroke, segment_level_char, segment_level_term, segment_level_stroke_in, segment_level_stroke_out, _] = \ + np.load(self.datadir+'/'+str(uid)+'/'+str(tid)+'.npy', allow_pickle=True, encoding='bytes') + + else: + [sentence_level_raw_stroke, sentence_level_stroke_in, sentence_level_stroke_out, sentence_level_term, sentence_level_char, word_level_raw_stroke, word_level_stroke_in, word_level_stroke_out, word_level_term, word_level_char, segment_level_raw_stroke, segment_level_stroke_in, segment_level_stroke_out, segment_level_term, segment_level_char, _] = \ + np.load(self.datadir+'/'+str(uid)+'/'+str(tid)+'.npy', allow_pickle=True, encoding='bytes') + + if self.datadir == './data/DW_writers': + sentence_level_char = sentence_level_char[1:] + sentence_level_term = sentence_level_term[1:] + + if self.datadir == './data/VALID_DW_writers': + sentence_level_char = sentence_level_char[1:] + sentence_level_term = sentence_level_term[1:] + + while True: + if len(sentence_level_term) == 0: + break + if sentence_level_term[-1] != 1.0: + sentence_level_raw_stroke = sentence_level_raw_stroke[:-1] + sentence_level_char = sentence_level_char[:-1] + sentence_level_term = sentence_level_term[:-1] + sentence_level_stroke_in = sentence_level_stroke_in[:-1] + sentence_level_stroke_out = sentence_level_stroke_out[:-1] + else: + break + + tmp = [] + for i, t in enumerate(sentence_level_term): + if t == 1: + tmp.append(sentence_level_char[i]) + + a = np.ones_like(sentence_level_stroke_in) + a[:,:2] /= self.divider + + if len(sentence_level_stroke_in) == len(sentence_level_term) and len(tmp) > 0 and len(sentence_level_stroke_in) > 0: + user_sentence_level_stroke_in.append(np.asarray(sentence_level_stroke_in) * a) + user_sentence_level_stroke_out.append(np.asarray(sentence_level_stroke_out) * a) + user_sentence_level_stroke_length.append(len(sentence_level_stroke_in)) + user_sentence_level_char.append(np.asarray(tmp)) + user_sentence_level_term.append(np.asarray(sentence_level_term)) + user_sentence_level_char_length.append(len(tmp)) + + for wid in range(len(word_level_stroke_in)): + each_word_level_stroke_in = word_level_stroke_in[wid] + each_word_level_stroke_out = word_level_stroke_out[wid] + + if self.datadir == './data/DW_writers': + each_word_level_term = word_level_term[wid][1:] + each_word_level_char = word_level_char[wid][1:] + elif self.datadir == './data/VALID_DW_writers': + each_word_level_term = word_level_term[wid][1:] + each_word_level_char = word_level_char[wid][1:] + else: + each_word_level_term = word_level_term[wid] + each_word_level_char = word_level_char[wid] + + + # assert (len(each_word_level_stroke_in) == len(each_word_level_char) == len(each_word_level_term)) + while True: + if len(each_word_level_term) == 0: + break + if each_word_level_term[-1] != 1.0: + # each_word_level_raw_stroke = each_word_level_raw_stroke[:-1] + each_word_level_char = each_word_level_char[:-1] + each_word_level_term = each_word_level_term[:-1] + each_word_level_stroke_in = each_word_level_stroke_in[:-1] + each_word_level_stroke_out = each_word_level_stroke_out[:-1] + else: + break + + tmp = [] + for i, t in enumerate(each_word_level_term): + if t == 1: + tmp.append(each_word_level_char[i]) + + b = np.ones_like(each_word_level_stroke_in) + b[:,:2] /= self.divider + + if len(each_word_level_stroke_in) == len(each_word_level_term) and len(tmp) > 0 and len(each_word_level_stroke_in) > 0: + user_word_level_stroke_in.append(np.asarray(each_word_level_stroke_in) * b) + user_word_level_stroke_out.append(np.asarray(each_word_level_stroke_out) * b) + user_word_level_stroke_length.append(len(each_word_level_stroke_in)) + user_word_level_char.append(np.asarray(tmp)) + user_word_level_term.append(np.asarray(each_word_level_term)) + user_word_level_char_length.append(len(tmp)) + + segment_level_stroke_in_list = [] + segment_level_stroke_out_list = [] + segment_level_stroke_length_list = [] + segment_level_char_list = [] + segment_level_term_list = [] + segment_level_char_length_list = [] + + for sid in range(len(segment_level_stroke_in[wid])): + each_segment_level_stroke_in = segment_level_stroke_in[wid][sid] + each_segment_level_stroke_out = segment_level_stroke_out[wid][sid] + + if self.datadir == './data/DW_writers': + each_segment_level_term = segment_level_term[wid][sid][1:] + each_segment_level_char = segment_level_char[wid][sid][1:] + elif self.datadir == './data/VALID_DW_writers': + each_segment_level_term = segment_level_term[wid][sid][1:] + each_segment_level_char = segment_level_char[wid][sid][1:] + else: + each_segment_level_term = segment_level_term[wid][sid] + each_segment_level_char = segment_level_char[wid][sid] + + while True: + if len(each_segment_level_term) == 0: + break + if each_segment_level_term[-1] != 1.0: + # each_segment_level_raw_stroke = each_segment_level_raw_stroke[:-1] + each_segment_level_char = each_segment_level_char[:-1] + each_segment_level_term = each_segment_level_term[:-1] + each_segment_level_stroke_in = each_segment_level_stroke_in[:-1] + each_segment_level_stroke_out = each_segment_level_stroke_out[:-1] + else: + break + + tmp = [] + for i, t in enumerate(each_segment_level_term): + if t == 1: + tmp.append(each_segment_level_char[i]) + + c = np.ones_like(each_segment_level_stroke_in) + c[:,:2] /= self.divider + + if len(each_segment_level_stroke_in) == len(each_segment_level_term) and len(tmp) > 0 and len(each_segment_level_stroke_in) > 0: + segment_level_stroke_in_list.append(np.asarray(each_segment_level_stroke_in) * c) + segment_level_stroke_out_list.append(np.asarray(each_segment_level_stroke_out) * c) + segment_level_stroke_length_list.append(len(each_segment_level_stroke_in)) + segment_level_char_list.append(np.asarray(tmp)) + segment_level_term_list.append(np.asarray(each_segment_level_term)) + segment_level_char_length_list.append(len(tmp)) + + if len(segment_level_stroke_length_list) > 0: + SEGMENT_MAX_STROKE_LENGTH = np.max(segment_level_stroke_length_list) + SEGMENT_MAX_CHARACTER_LENGTH = np.max(segment_level_char_length_list) + + new_segment_level_stroke_in_list = np.asarray([np.pad(a, ((0, SEGMENT_MAX_STROKE_LENGTH-len(a)), (0, 0)), 'constant') for a in segment_level_stroke_in_list]) + new_segment_level_stroke_out_list = np.asarray([np.pad(a, ((0, SEGMENT_MAX_STROKE_LENGTH-len(a)), (0, 0)), 'constant') for a in segment_level_stroke_out_list]) + new_segment_level_term_list = np.asarray([np.pad(a, ((0, SEGMENT_MAX_STROKE_LENGTH-len(a))), 'constant') for a in segment_level_term_list]) + new_segment_level_char_list = np.asarray([np.pad(a, ((0, SEGMENT_MAX_CHARACTER_LENGTH-len(a))), 'constant') for a in segment_level_char_list]) + + user_segment_level_stroke_in.append(new_segment_level_stroke_in_list) + user_segment_level_stroke_out.append(new_segment_level_stroke_out_list) + user_segment_level_stroke_length.append(segment_level_stroke_length_list) + user_segment_level_char.append(new_segment_level_char_list) + user_segment_level_term.append(new_segment_level_term_list) + user_segment_level_char_length.append(segment_level_char_length_list) + + WORD_MAX_STROKE_LENGTH = np.max(user_word_level_stroke_length) + WORD_MAX_CHARACTER_LENGTH = np.max(user_word_level_char_length) + + SENTENCE_MAX_STROKE_LENGTH = np.max(user_sentence_level_stroke_length) + SENTENCE_MAX_CHARACTER_LENGTH = np.max(user_sentence_level_char_length) + + new_sentence_level_stroke_in = np.asarray([np.pad(a, ((0, SENTENCE_MAX_STROKE_LENGTH-len(a)), (0,0)), 'constant') for a in user_sentence_level_stroke_in]) + new_sentence_level_stroke_out = np.asarray([np.pad(a, ((0, SENTENCE_MAX_STROKE_LENGTH-len(a)), (0,0)), 'constant') for a in user_sentence_level_stroke_out]) + new_sentence_level_term = np.asarray([np.pad(a, ((0, SENTENCE_MAX_STROKE_LENGTH-len(a))), 'constant') for a in user_sentence_level_term]) + new_sentence_level_char = np.asarray([np.pad(a, ((0, SENTENCE_MAX_CHARACTER_LENGTH-len(a))), 'constant') for a in user_sentence_level_char]) + new_word_level_stroke_in = np.asarray([np.pad(a, ((0, WORD_MAX_STROKE_LENGTH-len(a)), (0,0)), 'constant') for a in user_word_level_stroke_in]) + new_word_level_stroke_out = np.asarray([np.pad(a, ((0, WORD_MAX_STROKE_LENGTH-len(a)), (0,0)), 'constant') for a in user_word_level_stroke_out]) + new_word_level_term = np.asarray([np.pad(a, ((0, WORD_MAX_STROKE_LENGTH-len(a))), 'constant') for a in user_word_level_term]) + new_word_level_char = np.asarray([np.pad(a, ((0, WORD_MAX_CHARACTER_LENGTH-len(a))), 'constant') for a in user_word_level_char]) + + all_sentence_level_stroke_in.append(new_sentence_level_stroke_in) + all_sentence_level_stroke_out.append(new_sentence_level_stroke_out) + all_sentence_level_stroke_length.append(user_sentence_level_stroke_length) + all_sentence_level_term.append(new_sentence_level_term) + all_sentence_level_char.append(new_sentence_level_char) + all_sentence_level_char_length.append(user_sentence_level_char_length) + all_word_level_stroke_in.append(new_word_level_stroke_in) + all_word_level_stroke_out.append(new_word_level_stroke_out) + all_word_level_stroke_length.append(user_word_level_stroke_length) + all_word_level_term.append(new_word_level_term) + all_word_level_char.append(new_word_level_char) + all_word_level_char_length.append(user_word_level_char_length) + all_segment_level_stroke_in.append(user_segment_level_stroke_in) + all_segment_level_stroke_out.append(user_segment_level_stroke_out) + all_segment_level_stroke_length.append(user_segment_level_stroke_length) + all_segment_level_term.append(user_segment_level_term) + all_segment_level_char.append(user_segment_level_char) + all_segment_level_char_length.append(user_segment_level_char_length) + + return [all_sentence_level_stroke_in, all_sentence_level_stroke_out, all_sentence_level_stroke_length, all_sentence_level_term, all_sentence_level_char, all_sentence_level_char_length, all_word_level_stroke_in, all_word_level_stroke_out, all_word_level_stroke_length, all_word_level_term, all_word_level_char, all_word_level_char_length, all_segment_level_stroke_in, all_segment_level_stroke_out, all_segment_level_stroke_length, all_segment_level_term, all_segment_level_char, all_segment_level_char_length] diff --git a/SynthesisNetwork.py b/SynthesisNetwork.py new file mode 100644 index 0000000000000000000000000000000000000000..75502a8e5fa24c5dbef598f9e8196a6d17a777d6 --- /dev/null +++ b/SynthesisNetwork.py @@ -0,0 +1,1659 @@ +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 \ No newline at end of file diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..21060cfe39aff6a9d5a2ee280b76458503f9fa88 --- /dev/null +++ b/app.py @@ -0,0 +1,187 @@ +import torch +import argparse +import numpy as np +from helper import * +from config.GlobalVariables import * +from SynthesisNetwork import SynthesisNetwork +from DataLoader import DataLoader +import convenience +import gradio as gr + +#@title Demo +device = 'cpu' +num_samples = 10 + +net = SynthesisNetwork(weight_dim=256, num_layers=3).to(device) + +if not torch.cuda.is_available(): + try: # retrained model also contains loss in dict + net.load_state_dict(torch.load('./model/250000.pt', map_location=torch.device(device))["model_state_dict"]) + except: + net.load_state_dict(torch.load('./model/250000.pt', map_location=torch.device(device))) + + +dl = DataLoader(num_writer=1, num_samples=10, divider=5.0, datadir='./data/writers') + + +writer_options = [5, 14, 15, 16, 17, 22, 25, 80, 120, 137, 147, 151] +all_loaded_data = [] +avail_char = "0 1 2 3 4 5 6 7 8 9 a b c d e f g h i j k l m n o p q r s t u v w x y z A B C D E F G H I J K L M N O P Q R S T U V W X Y Z ! ? \" ' * + - = : ; , . < > \ / [ ] ( ) # $ % &" +avail_char_list = avail_char.split(" ") +for writer_id in [120, 80]: + loaded_data = dl.next_batch(TYPE='TRAIN', uid=writer_id, tids=list(range(num_samples))) + all_loaded_data.append(loaded_data) + +default_loaded_data = all_loaded_data[-1] +# for writer interpolation +def interpolate_writers(target_word, weight): + image = convenience.sample_blended_writers([1 - weight, weight], target_word, net, all_loaded_data, device).convert("RGB") + return image + +def choose_blend_writers(writer1, writer2): + id1, id2 = int(writer1.split(" ")[1]), int(writer1.split(" ")[1]) + all_loaded_data.clear() + for writer_id in [id1, id2]: + loaded_data = dl.next_batch(TYPE='TRAIN', uid=writer_id, tids=list(range(num_samples))) + all_loaded_data.append(loaded_data) + + return gr.Slider.update(label=f"{writer1} vs. {writer2}") +''' +def choose_writer(writ, c1, c2, c3, c4, val): + all_loaded_data.clear() + w = int(writ.split(" ")[1]) + loaded_data = dl.next_batch(TYPE='TRAIN', uid=w, tids=list(range(num_samples))) + all_loaded_data.append(loaded_data) + return char_grid(c1, c2, c3, c4, val) +''' +# for character grrid +def choose_grid_chars(c1, c2, c3, c4): + return gr.Button.update(value=f"Blend {c1}, {c2}, {c3}, and {c4}!") + +def char_grid(c1, c2, c3, c4): + image = convenience.sample_character_grid([c1, c2, c3, c4], 5, net, [default_loaded_data], device).convert("RGB") + return image + +# for character blend +def interpolate_chars(c1, c2, weight): + image = convenience.sample_blended_chars([1 - weight, weight], [c1, c2], net, [default_loaded_data], device).convert("RGB") + return image + +def choose_blend_chars(c1, c2): + return gr.Slider.update(label=f"'{c1}' vs. '{c2}'") + +# for MDN +def mdn_sample(word, maxs, maxr): + image = convenience.mdn_single_sample(word, maxs, maxr, net, [default_loaded_data], device).convert("RGB") + return image +""" +def char_vid(word): + #make word list + convenience.char_interpolation_video(word_list, 10, net, [default_loaded_data], device).convert('RGB') + vid_path = f"/content/drive/MyDrive/Colab Notebooks/Spring22/decoupled-style-descriptors-eb/results/abcdefg_video.mov" + return gr.Video.update(value=vid_path) +""" + +with gr.Blocks() as demo: + with gr.Tabs(): + with gr.TabItem("Blend Writers"): + target_word = gr.Textbox(label="Target Word", value="hello world", max_lines=1) + with gr.Row(): + left_ratio_options = ["Style " + str(id) for i, id in enumerate(writer_options) if i % 2 == 0] + right_ratio_options = ["Style " + str(id) for i, id in enumerate(writer_options) if i % 2 == 1] + with gr.Column(): + writer1 = gr.Radio(left_ratio_options, value="Style 120", label="Style for first writer") + with gr.Column(): + writer2 = gr.Radio(right_ratio_options, value="Style 80", label="Style for second writer") + with gr.Row(): + writer_slider = gr.Slider(0, 1, value=0.3, label="Style 120 vs. Style 80") + with gr.Row(): + writer_submit = gr.Button("Submit") + with gr.Row(): + writer_default_image = convenience.sample_blended_writers([0.7, 0.3], "hello world", net, all_loaded_data, device).convert("RGB") + writer_output = gr.Image(writer_default_image) + + writer_submit.click(fn=interpolate_writers, inputs=[target_word, writer_slider], outputs=[writer_output]) + writer_slider.change(fn=interpolate_writers, inputs=[target_word, writer_slider], outputs=[writer_output]) + target_word.submit(fn=interpolate_writers, inputs=[target_word, writer_slider], outputs=[writer_output]) + + writer1.change(fn=choose_blend_writers, inputs=[writer1, writer2], outputs=[writer_slider]) + writer2.change(fn=choose_blend_writers, inputs=[writer1, writer2], outputs=[writer_slider]) + + + with gr.TabItem("Blend Characters"): + with gr.Row(): + with gr.Column(): + char1 = gr.Dropdown(choices=avail_char_list, value="y", label="Character 1") + with gr.Column(): + char2 = gr.Dropdown(choices=avail_char_list, value="s", label="Character 2") + with gr.Row(): + char_slider = gr.Slider(0, 1, value=0.3, label="'y' vs. 's'") + with gr.Row(): + char_default_image = convenience.sample_blended_chars([0.7, 0.3], ["y", "s"], net, [default_loaded_data], device).convert("RGB") + char_output = gr.Image(char_default_image) + + char_slider.change(fn=interpolate_chars, inputs=[char1, char2, char_slider], outputs=[char_output]) + + char1.change(fn=choose_blend_chars, inputs=[char1, char2], outputs=[char_slider]) + char2.change(fn=choose_blend_chars, inputs=[char1, char2], outputs=[char_slider]) + + """ + with gr.TabItem("Character Grid"): #slow + with gr.Row(): + with gr.Column(): + char1 = gr.Dropdown(choices=avail_char_list, value="y", label="Character 1") + with gr.Column(): + char2 = gr.Dropdown(choices=avail_char_list, value="s", label="Character 2") + with gr.Column(): + char3 = gr.Dropdown(choices=avail_char_list, value="u", label="Character 3") + with gr.Column(): + char4 = gr.Dropdown(choices=avail_char_list, value="n", label="Character 4") + with gr.Row(): + submit_button = gr.Button(value="Blend y, s, u, and n!") + ''' + with gr.Row(): + radio_options2 = ["Writer " + str(n) for n in writer_options] + writer = gr.Radio(radio_options2, value="Writer 80", label="Style for Writer") + writer.change(fn=choose_writer, inputs=[writer, char1, char2, char3, char4, slider2], outputs=[output]) + ''' + #slider2 = gr.Slider(2, 20, value=10, label="Grid Size", step=1) + + default_image = convenience.sample_character_grid(['y', 's', 'u', 'n'], 10, net, [default_loaded_data], device).convert("RGB") + output = gr.Image(default_image) + + char1.change(fn=choose_grid_chars, inputs=[char1, char2, char3, char4], outputs=[submit_button]) + char2.change(fn=choose_grid_chars, inputs=[char1, char2, char3, char4], outputs=[submit_button]) + char3.change(fn=choose_grid_chars, inputs=[char1, char2, char3, char4], outputs=[submit_button]) + char4.change(fn=choose_grid_chars, inputs=[char1, char2, char3, char4], outputs=[submit_button]) + + + #slider2.change(fn=char_grid, inputs=[char1, char2, char3, char4, slider2], outputs=[output]) + submit_button.click(fn=char_grid, inputs=[char1, char2, char3, char4], outputs=[output]) + """ + + with gr.TabItem("Add Randomness"): + mdn_word = gr.Textbox(label="Target Word", value="hello world", max_lines=1) + ''' + with gr.Row(): + radio_options3 = ["Writer " + str(n) for n in writer_options] + writer = gr.Radio(radio_options3, value="Writer 80", label="Style for Writer") + writer.change(fn=new_writer_mdn, inputs=[writer, slider3, slider4], outputs=[output]) + ''' + with gr.Row(): + with gr.Column(): + max_rand = gr.Slider(0, 1, value=1, label="Maximum Randomness") + with gr.Column(): + scale_rand = gr.Slider(0, 3, value=0.5, label="Scale of Randomness") + with gr.Row(): + sample_button = gr.Button(value="Resample!") + with gr.Row(): + default_im = convenience.mdn_single_sample("hello world", 0.5, 1, net, [default_loaded_data], device).convert('RGB') + mdn_output = gr.Image(default_im) + + max_rand.change(fn=mdn_sample, inputs=[mdn_word, scale_rand, max_rand], outputs=[mdn_output]) + scale_rand.change(fn=mdn_sample, inputs=[mdn_word, scale_rand, max_rand], outputs=[mdn_output]) + sample_button.click(fn=mdn_sample, inputs=[mdn_word, scale_rand, max_rand], outputs=[mdn_output]) + mdn_word.submit(fn=mdn_sample, inputs=[mdn_word, scale_rand, max_rand], outputs=[mdn_output]) + +demo.launch() diff --git a/config/GlobalVariables.py b/config/GlobalVariables.py new file mode 100644 index 0000000000000000000000000000000000000000..a1791a55142638e6d0030b92c0c247ce973ff3b4 --- /dev/null +++ b/config/GlobalVariables.py @@ -0,0 +1,5 @@ +COLORS = [(255,255,255), (255,0,0), (0,255,0), (0,0,255), (255,255,0),(0,255,255),(255,0,255),(255,128,0),(0,255,128),(128,0,255),(255,0,128),(128,255,0),(0,128,255)] +CHARACTERS = ' !"#$%&\'()*+,-./0123456789:;<=>?ABCDEFGHIJKLMNOPQRSTUVWXYZ[]abcdefghijklmnopqrstuvwxyz' +# CHARACTERS = ' !"&\'(),-.:;?ABCDEFGHIJKLMNOPQRSTUVWXYZ[]abcdefghijklmnopqrstuvwxyz' + +''.join([CHARACTERS[i] for i in [4,2,30]]) diff --git a/config/__init__.py b/config/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/config/__pycache__/GlobalVariables.cpython-38.pyc b/config/__pycache__/GlobalVariables.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..436a1cbd6f1756ee9f0e3af64761f87118c4f393 Binary files /dev/null and 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file mode 100644 index 0000000000000000000000000000000000000000..ac56ea425ad54dec0505293b7006698d41c559fb --- /dev/null +++ b/convenience.py @@ -0,0 +1,555 @@ + +import os +import re +from random import random +import torch +import pickle +import argparse +import numpy as np +from helper import * +from PIL import Image +import torch.nn as nn +import torch.optim as optim +from config.GlobalVariables import * +from tensorboardX import SummaryWriter +from SynthesisNetwork import SynthesisNetwork +from DataLoader import DataLoader +# import ffmpeg # for problems with ffmpeg uninstall ffmpeg and then install ffmpeg-python + +L = 256 + +def get_mean_global_W(net, loaded_data, device): + """gets the mean global style vector for a given writer""" + [_, _, _, _, _, _, all_word_level_stroke_in, all_word_level_stroke_out, all_word_level_stroke_length, all_word_level_term, all_word_level_char, all_word_level_char_length, all_segment_level_stroke_in, all_segment_level_stroke_out, + all_segment_level_stroke_length, all_segment_level_term, all_segment_level_char, all_segment_level_char_length] = loaded_data + + batch_word_level_stroke_in = [torch.FloatTensor(a).to(device) for a in all_word_level_stroke_in] + batch_word_level_stroke_out = [torch.FloatTensor(a).to(device) for a in all_word_level_stroke_out] + batch_word_level_stroke_length = [torch.LongTensor(a).to(device).unsqueeze(-1) for a in all_word_level_stroke_length] + batch_word_level_term = [torch.FloatTensor(a).to(device) for a in all_word_level_term] + batch_word_level_char = [torch.LongTensor(a).to(device) for a in all_word_level_char] + batch_word_level_char_length = [torch.LongTensor(a).to(device).unsqueeze(-1) for a in all_word_level_char_length] + batch_segment_level_stroke_in = [[torch.FloatTensor(a).to(device) for a in b] for b in all_segment_level_stroke_in] + batch_segment_level_stroke_out = [[torch.FloatTensor(a).to(device) for a in b] for b in all_segment_level_stroke_out] + batch_segment_level_stroke_length = [[torch.LongTensor(a).to(device).unsqueeze(-1) for a in b] for b in all_segment_level_stroke_length] + batch_segment_level_term = [[torch.FloatTensor(a).to(device) for a in b] for b in all_segment_level_term] + batch_segment_level_char = [[torch.LongTensor(a).to(device) for a in b] for b in all_segment_level_char] + batch_segment_level_char_length = [[torch.LongTensor(a).to(device).unsqueeze(-1) for a in b] for b in all_segment_level_char_length] + + with torch.no_grad(): + word_inf_state_out = net.inf_state_fc1(batch_word_level_stroke_out[0]) + word_inf_state_out = net.inf_state_relu(word_inf_state_out) + word_inf_state_out, _ = net.inf_state_lstm(word_inf_state_out) + + user_word_level_char = batch_word_level_char[0] + user_word_level_term = batch_word_level_term[0] + + original_Wc = [] + word_batch_id = 0 + + curr_seq_len = batch_word_level_stroke_length[0][word_batch_id][0] + curr_char_len = batch_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(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 = net.char_vec_fc_1(char_vector) + char_vector_1 = net.char_vec_relu_1(char_vector_1) + + char_out_1 = char_vector_1.unsqueeze(0) + char_out_1, (c, h) = net.char_lstm_1(char_out_1) + char_out_1 = char_out_1.squeeze(0) + char_out_1 = net.char_vec_fc2_1(char_out_1) + char_matrix_1 = char_out_1.view([-1, 1, 256, 256]) + 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) + mean_global_W = torch.mean(W, 0) + return mean_global_W + + +def get_DSD(net, target_word, writer_mean_Ws, all_loaded_data, device): + """ + returns a style vector and character matrix for each character/segment in target_word + + n is the number of writers + M is the number of characters in the target word + L is the latent vector size (in this case 256) + + input: + - target_word, a string of length M to be converted to a DSD + - writer_mean_Ws, a list of n style vectors of size L + + output: + - all_writer_Ws, a tensor of size n x M x L representing the style vectors for each writer and character + - all_writer_Cs, a tensor of size n x M x L x L representing the corresponding character matrix + """ + + n = len(all_loaded_data) + M = len(target_word) + all_writer_Ws = torch.zeros(n, M, L) + all_writer_Cs = torch.zeros(n, M, L, L) + + for i in range(n): + np.random.seed(0) + + [_, _, _, _, _, _, all_word_level_stroke_in, all_word_level_stroke_out, all_word_level_stroke_length, all_word_level_term, all_word_level_char, all_word_level_char_length, all_segment_level_stroke_in, all_segment_level_stroke_out, + all_segment_level_stroke_length, all_segment_level_term, all_segment_level_char, all_segment_level_char_length] = all_loaded_data[i] + + available_segments = {} + for sid, sentence in enumerate(all_segment_level_char[0]): + for wid, word in enumerate(sentence): + segment = ''.join([CHARACTERS[i] for i in word]) + split_ids = np.asarray(np.nonzero(all_segment_level_term[0][sid][wid])) + + if segment in available_segments: + available_segments[segment].append([all_segment_level_stroke_out[0][sid][wid][:all_segment_level_stroke_length[0][sid][wid]], split_ids]) + else: + available_segments[segment] = [[all_segment_level_stroke_out[0][sid][wid][:all_segment_level_stroke_length[0][sid][wid]], split_ids]] + + index = 0 + all_W = [] + all_C = [] + + # while index <= len(target_word): + while index < len(target_word): + available = False + # Currently this just uses each character individually instead of the whole segment + # for end_index in range(len(target_word), index, -1): + # segment = target_word[index:end_index] + # print (segment) + segment = target_word[index] + if segment in available_segments: # method beta + # print(f'in dic - {segment}') + available = True + candidates = available_segments[segment] + segment_level_stroke_out, split_ids = candidates[np.random.randint(len(candidates))] + out = net.inf_state_fc1(torch.FloatTensor(segment_level_stroke_out).to(device).unsqueeze(0)) + out = net.inf_state_relu(out) + seg_W_c, (h_n, _) = net.inf_state_lstm(out) + + character = segment[0] # take the first character of the segment? + + # get character matrix using same method as method beta + char_vector = torch.eye(len(CHARACTERS))[CHARACTERS.index(character)].to(device).unsqueeze(0) + out = net.char_vec_fc_1(char_vector) + out = net.char_vec_relu_1(out) + out, _ = net.char_lstm_1(out.unsqueeze(0)) + out = out.squeeze(0) + out = net.char_vec_fc2_1(out) + char_matrix = out.view([-1, 256, 256]) + inv_char_matrix = char_matrix.inverse() + + id = split_ids[0][0] + W_c_vector = seg_W_c[0, id].squeeze() + + # invert to get writer-independed DSD + W_vector = torch.bmm(inv_char_matrix, W_c_vector.repeat(inv_char_matrix.size(0), 1).unsqueeze(2)) + all_W.append(W_vector) + all_C.append(char_matrix) + + index += 1 + + if index == len(target_word): + break + + if not available: # method alpha + character = target_word[index] + # print(f'no dic - {character}') + char_vector = torch.eye(len(CHARACTERS))[CHARACTERS.index(character)].to(device).unsqueeze(0) + out = net.char_vec_fc_1(char_vector) + out = net.char_vec_relu_1(out) + out, _ = net.char_lstm_1(out.unsqueeze(0)) + out = out.squeeze(0) + out = net.char_vec_fc2_1(out) + char_matrix = out.view([-1, 256, 256]) + + W_vector = writer_mean_Ws[i].repeat(char_matrix.size(0), 1).unsqueeze(2) + + # all_W.append([W_vector]) + all_W.append(W_vector) + all_C.append(char_matrix) + + index += 1 + + all_writer_Ws[i, :, :] = torch.stack(all_W).squeeze() + all_writer_Cs[i, :, :, :] = torch.stack(all_C).squeeze() + + return all_writer_Ws, all_writer_Cs + + +def get_writer_blend_W_c(writer_weights, all_Ws, all_Cs): + """ + generates character-dependent style-dependent DSDs for each character/segement in target_word, + averaging together the styles of the handwritings using provided weights + + n is the number of writers + M is the number of characters in the target word + L is the latent vector size (in this case 256) + + input: + - writer_weights, a list of length n weights for each writer that sum to one + - all_writer_Ws, an n x M x L tensor representing each weiter's style vector for every character + - all_writer_Cs, an n x M x L x L tensor representing the style's correspodning character matrix + + output: + - an M x 1 x L tensor of M scharacter-dependent style-dependent DSDs + """ + n, M, _ = all_Ws.shape + weights_tensor = torch.tensor(writer_weights).repeat_interleave(M * L).reshape(n, M, L) # repeat accross remaining dimensions + W_vectors = (weights_tensor * all_Ws).sum(axis=0).unsqueeze(-1) # take weighted sum accross writers axis + char_matrices = all_Cs[0, :, :, :] # character matrices are independent of writer + + W_cs = torch.bmm(char_matrices, W_vectors) + + return W_cs.reshape(M, 1, L) + + +def get_character_blend_W_c(character_weights, all_Ws, all_Cs): + """ + generates a single character-dependent style-dependent DSD, + averaging together the characters using provided weights + + M is the number of characters to blend + L is the latent vector size (in this case 256) + + input: + - character_weights, a list of length M weights for each character that sum to one + - all_Ws, a 1 x M x L tensor representing the wwiter's style vector for each character + - all_Cs, 1 x M x L x L tensor representing the style's correspodning character matrix + + output: + - a 1 x 1 x L tensor representing the character-dependent style-dependent DSDs + """ + M = len(character_weights) + W_vector = all_Ws[0, 0, :].unsqueeze(-1) + + weights_tensor = torch.tensor(character_weights).repeat_interleave(L * L).reshape(1, M, L, L) # repeat accross remaining dimensions + char_matrix = (weights_tensor * all_Cs).sum(axis=1).squeeze() # take weighted sum accross characters axis + + W_c = char_matrix @ W_vector + + return W_c.reshape(1, 1, L) + + +def get_commands(net, target_word, all_W_c): # seems like target_word is only used for length + """converts character-dependent style-dependent DSDs to a list of commands for drawing""" + all_commands = [] + current_id = 0 + while True: + word_Wc_rec_TYPE_D = [] + TYPE_D_REF = [] + cid = 0 + for segment_batch_id in range(len(all_W_c)): + if len(TYPE_D_REF) == 0: + for each_segment_Wc in all_W_c[segment_batch_id]: + if cid >= current_id: + word_Wc_rec_TYPE_D.append(each_segment_Wc) + cid += 1 + if len(word_Wc_rec_TYPE_D) > 0: + TYPE_D_REF.append(all_W_c[segment_batch_id][-1]) + else: + for each_segment_Wc in all_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) = net.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(all_W_c[segment_batch_id][-1]) + WC_ = torch.stack(word_Wc_rec_TYPE_D) + tmp_commands, res = net.sample_from_w_fix(WC_) + current_id += res + if len(all_commands) == 0: + all_commands.append(tmp_commands) + else: + all_commands.append(tmp_commands[1:]) + if res < 0 or current_id >= len(target_word): + break + + commands = [] + px, py = 0, 100 + for coms in all_commands: + for i, [dx, dy, t] in enumerate(coms): + x = px + dx * 5 + y = py + dy * 5 + commands.append([x, y, t]) + px, py = x, y + commands = np.asarray(commands) + commands[:, 0] -= np.min(commands[:, 0]) + + return commands + +def mdn_video(target_word, num_samples, scale_sd, clamp_mdn, net, all_loaded_data, device): + ''' + Method creating gif of mdn samples + num_samples: number of samples to be inputted + max_scale: the maximum value used to scale SD while sampling (increment is based on num samples) + ''' + words = target_word.split(' ') + us_target_word = re.sub(r"\s+", '_', target_word) + os.makedirs(f"./results/{us_target_word}_mdn_samples", exist_ok=True) + for i in range(num_samples): + im = Image.fromarray(np.zeros([160, 750])) + dr = ImageDraw.Draw(im) + width = 50 + + net.scale_sd = scale_sd + net.clamp_mdn = clamp_mdn + + mean_global_W = get_mean_global_W(net, all_loaded_data[0], device) + + for word in words: + writer_Ws, writer_Cs = get_DSD(net, word, [mean_global_W], [all_loaded_data[0]], device) + all_W_c = get_writer_blend_W_c([1], writer_Ws, writer_Cs) + all_commands = get_commands(net, word, all_W_c) + + for [x, y, t] in all_commands: + if t == 0: + dr.line((px+width, py, x+width, y), 255, 1) + px, py = x, y + width += np.max(all_commands[:, 0]) + 25 + + im.convert("RGB").save(f'results/{us_target_word}_mdn_samples/sample_{i}.png') + # Convert fromes to video using ffmpeg + photos = ffmpeg.input(f'results/{us_target_word}_mdn_samples/sample_*.png', pattern_type='glob', framerate=10) + videos = photos.output(f'results/{us_target_word}_video.mov', vcodec="libx264", pix_fmt="yuv420p") + videos.run(overwrite_output=True) + +def sample_blended_writers(writer_weights, target_sentence, net, all_loaded_data, device="cpu"): + """Generates an image of handwritten text based on target_sentence""" + words = target_sentence.split(' ') + + im = Image.fromarray(np.zeros([160, 750])) + dr = ImageDraw.Draw(im) + width = 50 + + writer_mean_Ws = [] + for loaded_data in all_loaded_data: + mean_global_W = get_mean_global_W(net, loaded_data, device) + writer_mean_Ws.append(mean_global_W) + + for word in words: + all_writer_Ws, all_writer_Cs = get_DSD(net, word, writer_mean_Ws, all_loaded_data, device) + all_W_c = get_writer_blend_W_c(writer_weights, all_writer_Ws, all_writer_Cs) + all_commands = get_commands(net, word, all_W_c) + + for [x, y, t] in all_commands: + if t == 0: + dr.line((px+width, py, x+width, y), 255, 1) + px, py = x, y + width += np.max(all_commands[:, 0]) + 25 + + return im + + +def sample_character_grid(letters, grid_size, net, all_loaded_data, device="cpu"): + """Generates an image of handwritten text based on target_sentence""" + width = 60 + im = Image.fromarray(np.zeros([(grid_size + 1) * width, (grid_size + 1) * width])) + dr = ImageDraw.Draw(im) + + M = len(letters) + mean_global_W = get_mean_global_W(net, all_loaded_data[0], device) + + # all_Ws = torch.zeros(1, M, L) + all_Cs = torch.zeros(1, M, L, L) + for i in range(M): # get corners of grid + W_vector, char_matrix = get_DSD(net, letters[i], [mean_global_W], [all_loaded_data[0]], device) + # all_Ws[:, i, :] = W_vector + all_Cs[:, i, :, :] = char_matrix + + all_Ws = mean_global_W.reshape(1, 1, L) + + for i in range(grid_size): + for j in range(grid_size): + wx = i / (grid_size - 1) + wy = j / (grid_size - 1) + + character_weights = [(1 - wx) * (1 - wy), # top left is 1 at (0, 0) + wx * (1 - wy), # top right is 1 at (1, 0) + (1 - wx) * wy, # bottom left is 1 at (0, 1) + wx * wy] # bottom right is 1 at (1, 1) + all_W_c = get_character_blend_W_c(character_weights, all_Ws, all_Cs) + all_commands = get_commands(net, letters[0], all_W_c) + + offset_x = i * width + offset_y = j * width + + for [x, y, t] in all_commands: + if t == 0: + dr.line(( + px + offset_x + width/2, + py + offset_y - width/2, # letters are shifted down for some reason + x + offset_x + width/2, + y + offset_y - width/2), 255, 1) + px, py = x, y + + return im + +def writer_interpolation_video(target_sentence, transition_time, net, all_loaded_data, device="cpu"): + """ + Generates a video of interpolating between each provided writer + """ + + n = len(all_loaded_data) + + os.makedirs(f"./results/{target_sentence}_blend_frames", exist_ok=True) + + words = target_sentence.split(' ') + + writer_mean_Ws = [] + for loaded_data in all_loaded_data: + mean_global_W = get_mean_global_W(net, loaded_data, device) + writer_mean_Ws.append(mean_global_W) + + word_Ws = [] + word_Cs = [] + + for word in words: + all_writer_Ws, all_writer_Cs = get_DSD(net, word, writer_mean_Ws, all_loaded_data, device) + word_Ws.append(all_writer_Ws) + word_Cs.append(all_writer_Cs) + + for i in range(n - 1): + for j in range(transition_time): + im = Image.fromarray(np.zeros([160, 750])) + dr = ImageDraw.Draw(im) + width = 50 + + completion = j/(transition_time) + + individual_weights = [1 - completion, completion] + writer_weights = [0] * i + individual_weights + [0] * (n - 2 - i) + + for k, word in enumerate(words): + all_writer_Ws, all_writer_Cs = word_Ws[k], word_Cs[k] + all_W_c = get_writer_blend_W_c(writer_weights, all_writer_Ws, all_writer_Cs) + all_commands = get_commands(net, word, all_W_c) + + for [x, y, t] in all_commands: + if t == 0: + dr.line((px+width, py, x+width, y), 255, 1) + px, py = x, y + width += np.max(all_commands[:, 0]) + 25 + + im.convert("RGB").save(f"./results/{target_sentence}_blend_frames/frame_{str(i * transition_time + j).zfill(3)}.png") + + # Convert fromes to video using ffmpeg + photos = ffmpeg.input(f"./results/{target_sentence}_blend_frames/frame_*.png", pattern_type='glob', framerate=10) + videos = photos.output(f"results/{target_sentence}_blend_video.mov", vcodec="libx264", pix_fmt="yuv420p") + videos.run(overwrite_output=True) + +def mdn_single_sample(target_word, scale_sd, clamp_mdn, net, all_loaded_data, device): + ''' + Method creating gif of mdn samples + num_samples: number of samples to be inputted + max_scale: the maximum value used to scale SD while sampling (increment is based on num samples) + ''' + words = target_word.split(' ') + im = Image.fromarray(np.zeros([160, 750])) + dr = ImageDraw.Draw(im) + width = 50 + + net.scale_sd = scale_sd + net.clamp_mdn = clamp_mdn + + mean_global_W = get_mean_global_W(net, all_loaded_data[0], device) + + for word in words: + writer_Ws, writer_Cs = get_DSD(net, word, [mean_global_W], [all_loaded_data[0]], device) + all_W_c = get_writer_blend_W_c([1], writer_Ws, writer_Cs) + all_commands = get_commands(net, word, all_W_c) + + for [x, y, t] in all_commands: + if t == 0: + dr.line((px+width, py, x+width, y), 255, 1) + px, py = x, y + width += np.max(all_commands[:, 0]) + 25 + + return im + +def sample_blended_chars(character_weights, letters, net, all_loaded_data, device="cpu"): + """Generates an image of handwritten text based on target_sentence""" + + width = 60 + im = Image.fromarray(np.zeros([100, 100])) + dr = ImageDraw.Draw(im) + + M = len(letters) + mean_global_W = get_mean_global_W(net, all_loaded_data[0], device) + + all_Cs = torch.zeros(1, M, L, L) + for i in range(M): # get corners of grid + W_vector, char_matrix = get_DSD(net, letters[i], [mean_global_W], [all_loaded_data[0]], device) + all_Cs[:, i, :, :] = char_matrix + + all_Ws = mean_global_W.reshape(1, 1, L) + + all_W_c = get_character_blend_W_c(character_weights, all_Ws, all_Cs) + all_commands = get_commands(net, letters[0], all_W_c) + + for [x, y, t] in all_commands: + if t == 0: + dr.line(( + px + width/2, + py - width/2, # letters are shifted down for some reason + x + width/2, + y - width/2), 255, 1) + px, py = x, y + + + return im + + +def char_interpolation_video(letters, transition_time, net, all_loaded_data, device="cpu"): + """Generates an image of handwritten text based on target_sentence""" + + os.makedirs(f"./results/{''.join(letters)}_frames", exist_ok=True) # make a folder for the frames + + width = 50 + + M = len(letters) + mean_global_W = get_mean_global_W(net, all_loaded_data[0], device) + + all_Cs = torch.zeros(1, M, L, L) + for i in range(M): # get corners of grid + W_vector, char_matrix = get_DSD(net, letters[i], [mean_global_W], [all_loaded_data[0]], device) + all_Cs[:, i, :, :] = char_matrix + + all_Ws = mean_global_W.reshape(1, 1, L) + + for i in range(M - 1): + for j in range(transition_time): + completion = j / (transition_time - 1) + individual_weights = [1 - completion, completion] + character_weights = [0] * i + individual_weights + [0] * (M - 2 - i) + all_W_c = get_character_blend_W_c(character_weights, all_Ws, all_Cs) + all_commands = get_commands(net, "change this later!", all_W_c) + + im = Image.fromarray(np.zeros([100, 100])) + dr = ImageDraw.Draw(im) + + for [x, y, t] in all_commands: + if t == 0: + dr.line(( + px + width/2, + py - width/2, # letters are shifted down for some reason + x + width/2, + y - width/2), 255, 1) + px, py = x, y + + + im.convert("RGB").save(f"results/{''.join(letters)}_frames/frames_{str(i * transition_time + j).zfill(3)}.png") + + # Convert fromes to video using ffmpeg + photos = ffmpeg.input(f"results/{''.join(letters)}_frames/frames_*.png", pattern_type='glob', framerate=24) + videos = photos.output(f"results/{''.join(letters)}_video.mov", vcodec="libx264", pix_fmt="yuv420p") + videos.run(overwrite_output=True) + + diff --git a/data/writers/120/0.npy 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np.concatenate([[[0, 0, 0]], tmp], 0) + else: + tmp = np.concatenate([raw_data[0:1], raw_data]) + tmp = tmp[1:] - tmp[:-1] + tmp[0,2] = 0 + tmp[1:,2] = raw_data[:-1, 2] + return tmp[:-1], tmp[1:] + + prohibits = [f'./BRUSH/5/118_resample{resample}', f'./BRUSH/7/14_resample{resample}', + f'./BRUSH/7/101_resample{resample}', f'./BRUSH/7/58_resample{resample}', + f'./BRUSH/14/20_resample{resample}', f'./BRUSH/22/45_resample{resample}', + f'./BRUSH/30/45_resample{resample}', f'./BRUSH/40/85_resample{resample}', + f'./BRUSH/50/45_resample{resample}', f'./BRUSH/59/29_resample{resample}', + f'./BRUSH/96/120_resample{resample}', f'./BRUSH/99/134_resample{resample}', + f'./BRUSH/140/35_resample{resample}', f'./BRUSH/144/55_resample{resample}', + f'./BRUSH/144/91_resample{resample}', f'./BRUSH/144/28_resample{resample}', + f'./BRUSH/144/69_resample{resample}'] + + preprocess_dir = 'preprocess' if pred_start == 1 else 'preprocess2' + + for writer_id in range(170): + print(f'Preprocessing the BRUSH dataset - Finished Writer ID: {writer_id + 1} / 170') + for sentence_id in [i for i in os.listdir(f'{data_dir}/{writer_id}') if i[-3:] == f'e{resample}']: + with open(f'{data_dir}/{writer_id}/{sentence_id}', 'rb') as f: + [sentence_text, raw_points, character_labels] = pickle.load(f) + if f'{data_dir}/{writer_id}/{sentence_id}' not in prohibits: + sentence_raw_points = raw_points + sentence_raw_points[:, 0] -= sentence_raw_points[0, 0] + sentence_stroke_in, sentence_stroke_out = reformat_raw_data(sentence_raw_points, pred_start=pred_start) + + split_char_ids = [i for i, c in enumerate(sentence_text) if c == ' '] + + sentence_char = [CHARACTERS.find(c) for c in sentence_text] + + sentence_term = [] + cid = 0 + for i in range(len(character_labels) - 1): + if character_labels[i + 1, cid] != 1: + if np.argmax(character_labels[i + 1]) >= cid: + cid += 1 + sentence_term.append(1) + else: + sentence_term.append(0) + else: + sentence_term.append(0) + sentence_term.append(1) + sentence_term = np.asarray(sentence_term) + + assert (len(sentence_term) == len(character_labels)) + + word_level_raw_stroke = [] + word_level_stroke_in = [] + word_level_stroke_out = [] + word_level_char = [] + word_level_term = [] + + segment_level_raw_stroke = [] + segment_level_stroke_in = [] + segment_level_stroke_out = [] + segment_level_char = [] + segment_level_term = [] + + character_level_raw_stroke = [] + character_level_stroke_in = [] + character_level_stroke_out = [] + character_level_char = [] + character_level_term = [] + + word_start_id = 0 + + for i, c in enumerate(sentence_text): + if c != ' ': + character_raw_points = raw_points[character_labels[:, i] > 0] + character_raw_points[:, 0] -= character_raw_points[0, 0] + character_level_raw_stroke.append(character_raw_points) + character_stroke_in, character_stroke_out = reformat_raw_data(character_raw_points, pred_start=pred_start) + character_level_stroke_in.append(character_stroke_in) + character_level_stroke_out.append(character_stroke_out) + term = np.zeros([len(character_raw_points)]) + term[-1] = 1 + character_level_term.append(term) + character_level_char.append([CHARACTERS.find(c)]) + + if i in split_char_ids: + word = sentence_text[word_start_id:i] + word_labels = np.zeros(len(character_labels)) + for j in range(word_start_id, i): + word_labels += character_labels[:, j] + word_raw_points = raw_points[word_labels > 0] + word_term = sentence_term[word_labels > 0] + word_term[0] = 0 + assert (np.sum(word_term) == len(word)) + word_raw_points[:, 0] -= word_raw_points[0, 0] + word_level_raw_stroke.append(word_raw_points) + word_stroke_in, word_stroke_out = reformat_raw_data(word_raw_points, pred_start=pred_start) + word_level_stroke_in.append(word_stroke_in) + word_level_stroke_out.append(word_stroke_out) + word_level_term.append(word_term) + word_level_char.append([CHARACTERS.find(c) for c in word]) + word_start_id = i + 1 + + assert (len(character_level_raw_stroke) == len(word)) + + segment_level_raw_stroke.append(character_level_raw_stroke) + segment_level_stroke_in.append(character_level_stroke_in) + segment_level_stroke_out.append(character_level_stroke_out) + segment_level_char.append(character_level_char) + segment_level_term.append(character_level_term) + character_level_raw_stroke = [] + character_level_stroke_in = [] + character_level_stroke_out = [] + character_level_char = [] + character_level_term = [] + + word = sentence_text[word_start_id:] + word_labels = np.zeros(len(character_labels)) + for j in range(word_start_id, len(sentence_text)): + word_labels += character_labels[:, j] + word_raw_points = raw_points[word_labels > 0] + word_raw_points[:, 0] -= word_raw_points[0, 0] + word_term = sentence_term[word_labels > 0] + word_term[0] = 0 + assert (np.sum(word_term) == len(word)) + word_level_raw_stroke.append(word_raw_points) + word_stroke_in, word_stroke_out = reformat_raw_data(word_raw_points, pred_start=pred_start) + word_level_stroke_in.append(word_stroke_in) + word_level_stroke_out.append(word_stroke_out) + word_level_term.append(word_term) + word_level_char.append([CHARACTERS.find(c) for c in word]) + assert (len(character_level_raw_stroke) == len(word)) + segment_level_raw_stroke.append(character_level_raw_stroke) + segment_level_stroke_in.append(character_level_stroke_in) + segment_level_stroke_out.append(character_level_stroke_out) + segment_level_char.append(character_level_char) + segment_level_term.append(character_level_term) + + if not os.path.exists(f'{data_dir}/{preprocess_dir}/{writer_id}'): + os.mkdir(f'{data_dir}/{preprocess_dir}/{writer_id}') + + with open(f'{data_dir}/{preprocess_dir}/{writer_id}/{sentence_id}', 'wb') as f: + pickle.dump([ + sentence_stroke_in, sentence_stroke_out, sentence_term, sentence_char, + word_level_stroke_in, word_level_stroke_out, word_level_term, word_level_char, + segment_level_stroke_in, segment_level_stroke_out, segment_level_term, segment_level_char], f) + + +def gaussian_2d(x1, x2, mu1, mu2, s1, s2, rho): + norm1 = x1 - mu1 + norm2 = x2 - mu2 + s1s2 = s1 * s2 + z = (norm1 / s1) ** 2 + (norm2 / s2) ** 2 - 2 * rho * norm1 * norm2 / s1s2 + numerator = torch.exp(-z / (2 * (1 - rho ** 2))) + denominator = 2 * math.pi * s1s2 * torch.sqrt(1 - rho ** 2) + gaussian = numerator / denominator + return gaussian + + +def get_minimax(stroke_results): + minimas = [] + maximas = [] + for stroke in stroke_results: + for i, [x, y] in enumerate(stroke): + if i == 0: + prev_x, prev_y = x, y + if i == len(stroke) - 1: + if prev_y <= y: + maximas.append([x, y]) + if prev_y >= y: + minimas.append([x, y]) + break + else: + next_x, next_y = stroke[i + 1] + if prev_y <= y and y >= next_y: + maximas.append([x, y]) + if prev_y >= y and y <= next_y: + minimas.append([x, y]) + + minimas = np.asarray(minimas) + maximas = np.asarray(maximas) + return minimas, maximas + + +def get_slope(minimas, maximas): + minima_slope, minima_intercept, _, _, _ = stats.linregress(minimas[:, 0], minimas[:, 1]) + maxima_slope, maxima_intercept, _, _, _ = stats.linregress(maximas[:, 0], maximas[:, 1]) + min_se = [] + max_se = [] + for [x, y] in minimas: + min_ny = minima_slope * x + minima_intercept + min_se.append(abs(min_ny - y)) + for [x, y] in maximas: + max_ny = maxima_slope * x + maxima_intercept + max_se.append(abs(max_ny - y)) + min_se, max_se = np.asarray(min_se), np.asarray(max_se) + new_minimas = minimas[min_se < np.mean(min_se)] + new_maximas = maximas[max_se < np.mean(max_se)] + if len(new_minimas) > 5: + minima_slope, minima_intercept, _, _, _ = stats.linregress(new_minimas[:, 0], new_minimas[:, 1]) + if len(new_maximas) > 5: + maxima_slope, maxima_intercept, _, _, _ = stats.linregress(new_maximas[:, 0], new_maximas[:, 1]) + + return minima_slope, minima_intercept, maxima_slope, maxima_intercept + + +def draw_commands(commands): + im = Image.fromarray(np.zeros([160, 750])) + dr = ImageDraw.Draw(im) + + px, py = 50, 100 + for i, [dx, dy, t] in enumerate(commands): + x = px + dx * 5 + y = py + dy * 5 + if t == 0: + dr.line((px, py, x, y), 255, 1) + px, py = x, y + + return im + + +def draw_points(raw_points, character_labels): + [w, h, _] = np.max(raw_points, 0) + im = Image.new("RGB", [int(w) + 100, int(h) + 100]) + dr = ImageDraw.Draw(im) + + colors = np.random.randint(0, 255, (len(character_labels[0]), 3)) + + for i, [x, y, t] in enumerate(raw_points): + if i > 0: + if pt == 0: + dr.line((px, py, x, y), tuple(colors[np.argmax(character_labels[i])]), 3) + px, py, pt = x, y, t + + return im diff --git a/interpolation.py b/interpolation.py new file mode 100644 index 0000000000000000000000000000000000000000..e52f8ad3eb80930a17fb4e6edb48b59df920cbec --- /dev/null +++ b/interpolation.py @@ -0,0 +1,113 @@ + +import torch +import argparse +import numpy as np +from helper import * +from config.GlobalVariables import * +from SynthesisNetwork import SynthesisNetwork +from DataLoader import DataLoader +import convenience + +L = 256 + + +def main(params): + np.random.seed(0) + torch.manual_seed(0) + + device = 'cpu' + + net = SynthesisNetwork(weight_dim=256, num_layers=3).to(device) + + if not torch.cuda.is_available(): + try: # retrained model also contains loss in dict + net.load_state_dict(torch.load('./model/250000.pt', map_location=torch.device('cpu'))["model_state_dict"]) + except: + net.load_state_dict(torch.load('./model/250000.pt', map_location=torch.device('cpu'))) + + + dl = DataLoader(num_writer=1, num_samples=10, divider=5.0, datadir='./data/writers') + + all_loaded_data = [] + + for writer_id in params.writer_ids: + loaded_data = dl.next_batch(TYPE='TRAIN', uid=writer_id, tids=list(range(params.num_samples))) + all_loaded_data.append(loaded_data) + + + if params.output == "image": + + if params.interpolate == "writer": + if len(params.blend_weights) != len(params.writer_ids): + raise ValueError("blend_weights must be same length as writer_ids") + im = convenience.sample_blended_writers(params.blend_weights, params.target_word, net, all_loaded_data, device) + im.convert("RGB").save(f'results/blend_{"+".join([str(i) for i in params.writer_ids])}.png') + elif params.interpolate == "character": + if len(params.blend_weights) != len(params.blend_chars): + raise ValueError("blend_weights must be same length as target_word") + im = convenience.sample_blended_chars(params.blend_weights, params.blend_chars, net, all_loaded_data, device) + im.convert("RGB").save(f'results/blend_{"+".join(params.blend_chars)}.png') + elif params.interpolate == "randomness": + if not 0 <= params.max_randomness <= 1: + raise ValueError("max_randomness must be between 0 and 1") + im = convenience.mdn_single_sample(params.target_word, params.scale_randomness, params.max_randomness, net, all_loaded_data, device) + im.convert("RGB").save(f"results/sample_{params.target_word.replace(' ', '_')}.png") + else: + raise ValueError("Invalid interpolation argument for outputting an image") + elif params.output == "grid": + + if params.interpolate == "character": + if len(params.grid_chars) != 4: + raise ValueError("grid_chars must be given exactly four characters") + im = convenience.sample_character_grid(params.grid_chars, params.grid_size, net, all_loaded_data, device) + im.convert("RGB").save(f'results/grid_{"".join(params.grid_chars)}.png') + else: + raise ValueError("Invalid interpolation argument for outputting a grid") + elif params.output == "video": + + if params.interpolate == "writer": + convenience.writer_interpolation_video(params.target_word, params.frames_per_step, net, all_loaded_data, device) + elif params.interpolate == "character": + convenience.char_interpolation_video(params.blend_chars, params.frames_per_step, net, all_loaded_data, device) + elif params.interpolate == "randomness": + if not 0 <= params.max_randomness <= 1: + raise ValueError("max_randomness must be between 0 and 1") + convenience.mdn_video(params.target_word, params.num_random_samples, params.scale_randomness, params.max_randomness, net, all_loaded_data, device) + else: + raise ValueError("Invalid interpolation argument for outputting a video") + else: + raise ValueError("Invalid output") + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Arguments for generating samples with the handwriting synthesis model.') + + # parser.add_argument('--writer_id', type=int, default=80) + parser.add_argument('--num_samples', type=int, default=10) + parser.add_argument('--generating_default', type=int, default=0) + + parser.add_argument('--output', type=str, default="image", choices=["image", "grid", "video"]) + parser.add_argument('--interpolate', type=str, default="randomness", choices=["writer", "character", "randomness"]) + + # PARAMS FOR BOTH WRITER AND CHARACTER INTERPOLATION: + # IF IMAGE - weights to use for a single sample of interpolation + parser.add_argument('--blend_weights', type=float, nargs="+", default=[0.5, 0.5]) + # IF VIDEO - the number of frames for each character/writer + parser.add_argument('--frames_per_step', type=int, default=10) + + # PARAMS IF WRITER INTERPOLATION: + parser.add_argument('--target_word', type=str, default="hello world") + parser.add_argument('--writer_ids', type=int, nargs="+", default=[80, 120]) + + # PARAMS IF CHARACTER INTERPOLATION: + # IF VIDEO OR BLEND + parser.add_argument('--blend_chars', type=str, nargs="+", default = ["a", "b", "c", "d", "e"]) + # IF GRID + parser.add_argument('--grid_chars', type=str, nargs="+", default= ["y", "s", "u", "n"]) + parser.add_argument('--grid_size', type=int, default=10) + + # PARAMS IF RANDOMNESS ITERPOLATION (--output will be ignored): + parser.add_argument('--max_randomness', type=float, default=1) + parser.add_argument('--scale_randomness', type=float, default=0.5) + parser.add_argument('--num_random_samples', type=int, default=10) + + main(parser.parse_args()) diff --git a/main.py b/main.py new file mode 100644 index 0000000000000000000000000000000000000000..661df567e54b9c4d6e3cf51f51bd324d0309ad75 --- /dev/null +++ b/main.py @@ -0,0 +1,486 @@ +# python -u main.py --divider 5.0 --weight_dim 256 --sample 5 --device 0 --num_layers 3 --num_writer 1 --lr 0.001 --VALIDATION 1 --datadir 2 --TYPE_B 0 --TYPE_C 0 + +import torch +import torch.nn as nn +import torch.optim as optim +import numpy as np +from tensorboardX import SummaryWriter +from PIL import Image, ImageDraw, ImageFont +from DataLoader import DataLoader +import pickle +from config.GlobalVariables import * +import os +import argparse +from SynthesisNetwork import SynthesisNetwork + +def main(params): + cwds = os.getcwd() + cwd = cwds.split('/')[-1] + + divider = params.divider + weight_dim = params.weight_dim + num_samples = params.sample + did = params.device + num_layers = params.num_layers + num_writer = params.num_writer + lr = params.lr + no_char = params.no_char + + datadir = './data/writers' + + if params.VALIDATION == 1: + VALIDATION = True + else: + VALIDATION = False + + if params.CHECKPOINT == 1: + LOAD_FROM_CHECKPOINT = True + else: + LOAD_FROM_CHECKPOINT = False + + if params.sentence_loss == 1: + sentence_loss = True + writer_sentence = SummaryWriter(logdir='./runs/sentence-' + cwd) + if VALIDATION: + valid_writer_sentence = SummaryWriter(logdir='./runs/valid-sentence-' + cwd) + else: + sentence_loss = False + if params.word_loss == 1: + word_loss = True + writer_word = SummaryWriter(logdir='./runs/word-' + cwd) + if VALIDATION: + valid_writer_word = SummaryWriter(logdir='./runs/valid-word-' + cwd) + else: + word_loss = False + if params.segment_loss == 1: + segment_loss = True + writer_segment = SummaryWriter(logdir='./runs/segment-' + cwd) + if VALIDATION: + valid_writer_segment = SummaryWriter(logdir='./runs/valid-segment-' + cwd) + else: + segment_loss = False + if params.TYPE_A == 1: + TYPE_A = True + else: + TYPE_A = False + if params.TYPE_B == 1: + TYPE_B = True + else: + TYPE_B = False + if params.TYPE_C == 1: + TYPE_C = True + else: + TYPE_C = False + if params.TYPE_D == 1: + TYPE_D = True + else: + TYPE_D = False + if params.ORIGINAL == 1: + ORIGINAL = True + else: + ORIGINAL = False + if params.REC == 1: + REC = True + else: + REC = False + + + timestep = 0 + grad_clip = 10.0 + device = "cuda" if torch.cuda.is_available() else "cpu" + if device == "cuda": + torch.cuda.set_device(did) + else: + num_writer = 1 + num_samples = 3 + + writer_all = SummaryWriter(logdir='./runs/all-'+cwd) + if VALIDATION: + valid_writer_all = SummaryWriter(logdir='./runs/valid-all-'+cwd) + + print (sentence_loss, word_loss, segment_loss) + net = SynthesisNetwork(weight_dim=weight_dim, num_layers=num_layers, sentence_loss=sentence_loss, word_loss=word_loss, segment_loss=segment_loss, TYPE_A=TYPE_A, TYPE_B=TYPE_B, TYPE_C=TYPE_C, TYPE_D=TYPE_D, ORIGINAL=ORIGINAL, REC=REC) + _ = net.to(device) + + for param in net.parameters(): + nn.init.normal_(param, mean=0.0, std=0.075) + + dl = DataLoader(num_writer=num_writer, num_samples=num_samples, divider=divider, datadir=datadir) + + optimizer = optim.Adam(net.parameters(), lr=lr) + step_size = int(10000 / (num_writer * num_samples)) + scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=step_size, gamma=0.99) + + if LOAD_FROM_CHECKPOINT: + checkpoints = os.listdir("./model") + max_timestep = max([int(filename[:-3]) for filename in checkpoints if ".pt" in filename]) + checkpoint = torch.load(f"./model/{max_timestep}.pt", map_location=torch.device('cpu')) + net.load_state_dict(checkpoint['model_state_dict']) + optimizer.load_state_dict(checkpoint['optimizer_state_dict']) + timestep = checkpoint['timestep'] + print(f"Loaded from checkpoint {timestep}") + + while True: + optimizer.zero_grad() + timestep += num_writer * num_samples + + [all_sentence_level_stroke_in, all_sentence_level_stroke_out, all_sentence_level_stroke_length, all_sentence_level_term, + all_sentence_level_char, all_sentence_level_char_length, all_word_level_stroke_in, all_word_level_stroke_out, + all_word_level_stroke_length, all_word_level_term, all_word_level_char, all_word_level_char_length, + all_segment_level_stroke_in, all_segment_level_stroke_out, all_segment_level_stroke_length, all_segment_level_term, + all_segment_level_char, all_segment_level_char_length] = dl.next_batch(TYPE='TRAIN') + + batch_sentence_level_stroke_in = [torch.FloatTensor(a).to(device) for a in all_sentence_level_stroke_in] + batch_sentence_level_stroke_out = [torch.FloatTensor(a).to(device) for a in all_sentence_level_stroke_out] + batch_sentence_level_stroke_length = [torch.LongTensor(a).to(device).unsqueeze(-1) for a in all_sentence_level_stroke_length] + batch_sentence_level_term = [torch.FloatTensor(a).to(device) for a in all_sentence_level_term] + batch_sentence_level_char = [torch.LongTensor(a).to(device) for a in all_sentence_level_char] + batch_sentence_level_char_length = [torch.LongTensor(a).to(device).unsqueeze(-1) for a in all_sentence_level_char_length] + batch_word_level_stroke_in = [torch.FloatTensor(a).to(device) for a in all_word_level_stroke_in] + batch_word_level_stroke_out = [torch.FloatTensor(a).to(device) for a in all_word_level_stroke_out] + batch_word_level_stroke_length = [torch.LongTensor(a).to(device).unsqueeze(-1) for a in all_word_level_stroke_length] + batch_word_level_term = [torch.FloatTensor(a).to(device) for a in all_word_level_term] + batch_word_level_char = [torch.LongTensor(a).to(device) for a in all_word_level_char] + batch_word_level_char_length = [torch.LongTensor(a).to(device).unsqueeze(-1) for a in all_word_level_char_length] + batch_segment_level_stroke_in = [[torch.FloatTensor(a).to(device) for a in b] for b in all_segment_level_stroke_in] + batch_segment_level_stroke_out = [[torch.FloatTensor(a).to(device) for a in b] for b in all_segment_level_stroke_out] + batch_segment_level_stroke_length = [[torch.LongTensor(a).to(device).unsqueeze(-1) for a in b] for b in all_segment_level_stroke_length] + batch_segment_level_term = [[torch.FloatTensor(a).to(device) for a in b] for b in all_segment_level_term] + batch_segment_level_char = [[torch.LongTensor(a).to(device) for a in b] for b in all_segment_level_char] + batch_segment_level_char_length = [[torch.LongTensor(a).to(device).unsqueeze(-1) for a in b] for b in all_segment_level_char_length] + + res = net([batch_sentence_level_stroke_in, batch_sentence_level_stroke_out, batch_sentence_level_stroke_length, + batch_sentence_level_term, batch_sentence_level_char, batch_sentence_level_char_length, + batch_word_level_stroke_in, batch_word_level_stroke_out, batch_word_level_stroke_length, + batch_word_level_term, batch_word_level_char, batch_word_level_char_length, batch_segment_level_stroke_in, + batch_segment_level_stroke_out, batch_segment_level_stroke_length, batch_segment_level_term, + batch_segment_level_char, batch_segment_level_char_length]) + + total_loss, sentence_losses, word_losses, segment_losses = res + + print ("Step :", timestep, "\tLoss :", total_loss.item(), "\tlr :", optimizer.param_groups[0]['lr']) + + writer_all.add_scalar('ALL/total_loss', total_loss, timestep) + + if sentence_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 + + writer_all.add_scalar('ALL/total_sentence_loss', total_sentence_loss, timestep) + writer_sentence.add_scalar('Loss/mean_W_consistency_loss', mean_sentence_W_consistency_loss, timestep) + if ORIGINAL: + writer_sentence.add_scalar('Loss/mean_ORIGINAL_loss', mean_ORIGINAL_sentence_termination_loss + mean_ORIGINAL_sentence_loc_reconstruct_loss + mean_ORIGINAL_sentence_touch_reconstruct_loss, timestep) + writer_sentence.add_scalar('Z_LOSS/mean_ORIGINAL_termination_loss', mean_ORIGINAL_sentence_termination_loss, timestep) + writer_sentence.add_scalar('Loss_Loc/mean_ORIGINAL_loc_reconstruct_loss', mean_ORIGINAL_sentence_loc_reconstruct_loss, timestep) + writer_sentence.add_scalar('Z_LOSS/mean_ORIGINAL_touch_reconstruct_loss', mean_ORIGINAL_sentence_touch_reconstruct_loss, timestep) + if TYPE_A: + writer_sentence.add_scalar('Loss/mean_TYPE_A_loss', mean_TYPE_A_sentence_termination_loss + mean_TYPE_A_sentence_loc_reconstruct_loss + mean_TYPE_A_sentence_touch_reconstruct_loss, timestep) + writer_sentence.add_scalar('Z_LOSS/mean_TYPE_A_termination_loss', mean_TYPE_A_sentence_termination_loss, timestep) + writer_sentence.add_scalar('Loss_Loc/mean_TYPE_A_loc_reconstruct_loss', mean_TYPE_A_sentence_loc_reconstruct_loss, timestep) + writer_sentence.add_scalar('Z_LOSS/mean_TYPE_A_touch_reconstruct_loss', mean_TYPE_A_sentence_touch_reconstruct_loss, timestep) + writer_sentence.add_scalar('Z_LOSS/mean_TYPE_A_WC_reconstruct_loss', mean_TYPE_A_sentence_WC_reconstruct_loss, timestep) + if TYPE_B: + writer_sentence.add_scalar('Loss/mean_TYPE_B_loss', mean_TYPE_B_sentence_termination_loss + mean_TYPE_B_sentence_loc_reconstruct_loss + mean_TYPE_B_sentence_touch_reconstruct_loss, timestep) + writer_sentence.add_scalar('Z_LOSS/mean_TYPE_B_termination_loss', mean_TYPE_B_sentence_termination_loss, timestep) + writer_sentence.add_scalar('Loss_Loc/mean_TYPE_B_loc_reconstruct_loss', mean_TYPE_B_sentence_loc_reconstruct_loss, timestep) + writer_sentence.add_scalar('Z_LOSS/mean_TYPE_B_touch_reconstruct_loss', mean_TYPE_B_sentence_touch_reconstruct_loss, timestep) + writer_sentence.add_scalar('Z_LOSS/mean_TYPE_B_WC_reconstruct_loss', mean_TYPE_B_sentence_WC_reconstruct_loss, timestep) + + if word_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 + writer_all.add_scalar('ALL/total_word_loss', total_word_loss, timestep) + writer_word.add_scalar('Loss/mean_W_consistency_loss', mean_word_W_consistency_loss, timestep) + + if ORIGINAL: + writer_word.add_scalar('Loss/mean_ORIGINAL_loss', mean_ORIGINAL_word_termination_loss + mean_ORIGINAL_word_loc_reconstruct_loss + mean_ORIGINAL_word_touch_reconstruct_loss, timestep) + writer_word.add_scalar('Z_LOSS/mean_ORIGINAL_termination_loss', mean_ORIGINAL_word_termination_loss, timestep) + writer_word.add_scalar('Loss_Loc/mean_ORIGINAL_loc_reconstruct_loss', mean_ORIGINAL_word_loc_reconstruct_loss, timestep) + writer_word.add_scalar('Z_LOSS/mean_ORIGINAL_touch_reconstruct_loss', mean_ORIGINAL_word_touch_reconstruct_loss, timestep) + if TYPE_A: + writer_word.add_scalar('Loss/mean_TYPE_A_loss', mean_TYPE_A_word_termination_loss + mean_TYPE_A_word_loc_reconstruct_loss + mean_TYPE_A_word_touch_reconstruct_loss, timestep) + writer_word.add_scalar('Z_LOSS/mean_TYPE_A_termination_loss', mean_TYPE_A_word_termination_loss, timestep) + writer_word.add_scalar('Loss_Loc/mean_TYPE_A_loc_reconstruct_loss', mean_TYPE_A_word_loc_reconstruct_loss, timestep) + writer_word.add_scalar('Z_LOSS/mean_TYPE_A_touch_reconstruct_loss', mean_TYPE_A_word_touch_reconstruct_loss, timestep) + writer_word.add_scalar('Z_LOSS/mean_TYPE_A_WC_reconstruct_loss', mean_TYPE_A_word_WC_reconstruct_loss, timestep) + if TYPE_B: + writer_word.add_scalar('Loss/mean_TYPE_B_loss', mean_TYPE_B_word_termination_loss + mean_TYPE_B_word_loc_reconstruct_loss + mean_TYPE_B_word_touch_reconstruct_loss, timestep) + writer_word.add_scalar('Z_LOSS/mean_TYPE_B_termination_loss', mean_TYPE_B_word_termination_loss, timestep) + writer_word.add_scalar('Loss_Loc/mean_TYPE_B_loc_reconstruct_loss', mean_TYPE_B_word_loc_reconstruct_loss, timestep) + writer_word.add_scalar('Z_LOSS/mean_TYPE_B_touch_reconstruct_loss', mean_TYPE_B_word_touch_reconstruct_loss, timestep) + writer_word.add_scalar('Z_LOSS/mean_TYPE_B_WC_reconstruct_loss', mean_TYPE_B_word_WC_reconstruct_loss, timestep) + if TYPE_C: + writer_word.add_scalar('Loss/mean_TYPE_C_loss', mean_TYPE_C_word_termination_loss + mean_TYPE_C_word_loc_reconstruct_loss + mean_TYPE_C_word_touch_reconstruct_loss, timestep) + writer_word.add_scalar('Z_LOSS/mean_TYPE_C_termination_loss', mean_TYPE_C_word_termination_loss, timestep) + writer_word.add_scalar('Loss_Loc/mean_TYPE_C_loc_reconstruct_loss', mean_TYPE_C_word_loc_reconstruct_loss, timestep) + writer_word.add_scalar('Z_LOSS/mean_TYPE_C_touch_reconstruct_loss', mean_TYPE_C_word_touch_reconstruct_loss, timestep) + writer_word.add_scalar('Z_LOSS/mean_TYPE_C_WC_reconstruct_loss', mean_TYPE_C_word_WC_reconstruct_loss, timestep) + if TYPE_D: + writer_word.add_scalar('Loss/mean_TYPE_D_loss', mean_TYPE_D_word_termination_loss + mean_TYPE_D_word_loc_reconstruct_loss + mean_TYPE_D_word_touch_reconstruct_loss, timestep) + writer_word.add_scalar('Z_LOSS/mean_TYPE_D_termination_loss', mean_TYPE_D_word_termination_loss, timestep) + writer_word.add_scalar('Loss_Loc/mean_TYPE_D_loc_reconstruct_loss', mean_TYPE_D_word_loc_reconstruct_loss, timestep) + writer_word.add_scalar('Z_LOSS/mean_TYPE_D_touch_reconstruct_loss', mean_TYPE_D_word_touch_reconstruct_loss, timestep) + writer_word.add_scalar('Z_LOSS/mean_TYPE_D_WC_reconstruct_loss', mean_TYPE_D_word_WC_reconstruct_loss, timestep) + + if segment_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 + writer_all.add_scalar('ALL/total_segment_loss', total_segment_loss, timestep) + writer_segment.add_scalar('Loss/mean_W_consistency_loss', mean_segment_W_consistency_loss, timestep) + if ORIGINAL: + writer_segment.add_scalar('Loss/mean_ORIGINAL_loss', mean_ORIGINAL_segment_termination_loss + mean_ORIGINAL_segment_loc_reconstruct_loss + mean_ORIGINAL_segment_touch_reconstruct_loss, timestep) + writer_segment.add_scalar('Z_LOSS/mean_ORIGINAL_termination_loss', mean_ORIGINAL_segment_termination_loss, timestep) + writer_segment.add_scalar('Loss_Loc/mean_ORIGINAL_loc_reconstruct_loss', mean_ORIGINAL_segment_loc_reconstruct_loss, timestep) + writer_segment.add_scalar('Z_LOSS/mean_ORIGINAL_touch_reconstruct_loss', mean_ORIGINAL_segment_touch_reconstruct_loss, timestep) + if TYPE_A: + writer_segment.add_scalar('Loss/mean_TYPE_A_loss', mean_TYPE_A_segment_termination_loss + mean_TYPE_A_segment_loc_reconstruct_loss + mean_TYPE_A_segment_touch_reconstruct_loss, timestep) + writer_segment.add_scalar('Z_LOSS/mean_TYPE_A_termination_loss', mean_TYPE_A_segment_termination_loss, timestep) + writer_segment.add_scalar('Loss_Loc/mean_TYPE_A_loc_reconstruct_loss', mean_TYPE_A_segment_loc_reconstruct_loss, timestep) + writer_segment.add_scalar('Z_LOSS/mean_TYPE_A_touch_reconstruct_loss', mean_TYPE_A_segment_touch_reconstruct_loss, timestep) + writer_segment.add_scalar('Z_LOSS/mean_TYPE_A_WC_reconstruct_loss', mean_TYPE_A_segment_WC_reconstruct_loss, timestep) + if TYPE_B: + writer_segment.add_scalar('Loss/mean_TYPE_B_loss', mean_TYPE_B_segment_termination_loss + mean_TYPE_B_segment_loc_reconstruct_loss + mean_TYPE_B_segment_touch_reconstruct_loss, timestep) + writer_segment.add_scalar('Z_LOSS/mean_TYPE_B_termination_loss', mean_TYPE_B_segment_termination_loss, timestep) + writer_segment.add_scalar('Loss_Loc/mean_TYPE_B_loc_reconstruct_loss', mean_TYPE_B_segment_loc_reconstruct_loss, timestep) + writer_segment.add_scalar('Z_LOSS/mean_TYPE_B_touch_reconstruct_loss', mean_TYPE_B_segment_touch_reconstruct_loss, timestep) + writer_segment.add_scalar('Z_LOSS/mean_TYPE_B_WC_reconstruct_loss', mean_TYPE_B_segment_WC_reconstruct_loss, timestep) + + total_loss.backward() + + torch.nn.utils.clip_grad_norm_(net.parameters(), grad_clip) + for p in net.parameters(): + if p.grad is not None: + # p.data.add_(-lr, p.grad.data) + p.data.add_(p.grad.data, alpha=-lr) + + optimizer.step() + + if timestep % (num_writer * num_samples * 1) == 0.0: + commands_list = net.sample([ batch_word_level_stroke_in, batch_word_level_stroke_out, batch_word_level_stroke_length, + batch_word_level_term, batch_word_level_char, batch_word_level_char_length, batch_segment_level_stroke_in, + batch_segment_level_stroke_out, batch_segment_level_stroke_length, batch_segment_level_term, + batch_segment_level_char, batch_segment_level_char_length]) + [t_commands, o_commands, a_commands, b_commands, c_commands, d_commands] = commands_list + + t_im = Image.fromarray(np.zeros([160, 750])) + t_dr = ImageDraw.Draw(t_im) + + px, py = 30, 100 + for i, [dx,dy,t] in enumerate(t_commands): + x = px + dx * 5 + y = py + dy * 5 + if t == 0: + t_dr.line((px,py,x,y),255,1) + px, py = x, y + + o_im = Image.fromarray(np.zeros([160, 750])) + o_dr = ImageDraw.Draw(o_im) + px, py = 30, 100 + for i, [dx,dy,t] in enumerate(o_commands): + x = px + dx * 5 + y = py + dy * 5 + if t == 0: + o_dr.line((px,py,x,y),255,1) + px, py = x, y + + a_im = Image.fromarray(np.zeros([160, 750])) + a_dr = ImageDraw.Draw(a_im) + px, py = 30, 100 + for i, [dx,dy,t] in enumerate(a_commands): + x = px + dx * 5 + y = py + dy * 5 + if t == 0: + a_dr.line((px,py,x,y),255,1) + px, py = x, y + + b_im = Image.fromarray(np.zeros([160, 750])) + b_dr = ImageDraw.Draw(b_im) + px, py = 30, 100 + for i, [dx,dy,t] in enumerate(b_commands): + x = px + dx * 5 + y = py + dy * 5 + if t == 0: + b_dr.line((px,py,x,y),255,1) + px, py = x, y + + c_im = Image.fromarray(np.zeros([160, 750])) + c_dr = ImageDraw.Draw(c_im) + px, py = 30, 100 + for i, [dx,dy,t] in enumerate(c_commands): + x = px + dx * 5 + y = py + dy * 5 + if t == 0: + c_dr.line((px,py,x,y),255,1) + px, py = x, y + + d_im = Image.fromarray(np.zeros([160, 750])) + d_dr = ImageDraw.Draw(d_im) + px, py = 30, 100 + for i, [dx,dy,t] in enumerate(d_commands): + x = px + dx * 5 + y = py + dy * 5 + if t == 0: + d_dr.line((px,py,x,y),255,1) + px, py = x, y + + dst = Image.new('RGB', (750, 960)) + dst.paste(t_im, (0, 0)) + dst.paste(o_im, (0, 160)) + dst.paste(a_im, (0, 320)) + dst.paste(b_im, (0, 480)) + dst.paste(c_im, (0, 640)) + dst.paste(d_im, (0, 800)) + writer_all.add_image('Res/Results', np.asarray(dst.convert("RGB")), timestep, dataformats='HWC') + + if VALIDATION: + [all_sentence_level_stroke_in, all_sentence_level_stroke_out, all_sentence_level_stroke_length, all_sentence_level_term, + all_sentence_level_char, all_sentence_level_char_length, all_word_level_stroke_in, all_word_level_stroke_out, + all_word_level_stroke_length, all_word_level_term, all_word_level_char, all_word_level_char_length, + all_segment_level_stroke_in, all_segment_level_stroke_out, all_segment_level_stroke_length, all_segment_level_term, + all_segment_level_char, all_segment_level_char_length] = dl.next_batch(TYPE='VALID') + + batch_sentence_level_stroke_in = [torch.FloatTensor(a).to(device) for a in all_sentence_level_stroke_in] + batch_sentence_level_stroke_out = [torch.FloatTensor(a).to(device) for a in all_sentence_level_stroke_out] + batch_sentence_level_stroke_length = [torch.LongTensor(a).to(device).unsqueeze(-1) for a in all_sentence_level_stroke_length] + batch_sentence_level_term = [torch.FloatTensor(a).to(device) for a in all_sentence_level_term] + batch_sentence_level_char = [torch.LongTensor(a).to(device) for a in all_sentence_level_char] + batch_sentence_level_char_length = [torch.LongTensor(a).to(device).unsqueeze(-1) for a in all_sentence_level_char_length] + batch_word_level_stroke_in = [torch.FloatTensor(a).to(device) for a in all_word_level_stroke_in] + batch_word_level_stroke_out = [torch.FloatTensor(a).to(device) for a in all_word_level_stroke_out] + batch_word_level_stroke_length = [torch.LongTensor(a).to(device).unsqueeze(-1) for a in all_word_level_stroke_length] + batch_word_level_term = [torch.FloatTensor(a).to(device) for a in all_word_level_term] + batch_word_level_char = [torch.LongTensor(a).to(device) for a in all_word_level_char] + batch_word_level_char_length = [torch.LongTensor(a).to(device).unsqueeze(-1) for a in all_word_level_char_length] + batch_segment_level_stroke_in = [[torch.FloatTensor(a).to(device) for a in b] for b in all_segment_level_stroke_in] + batch_segment_level_stroke_out = [[torch.FloatTensor(a).to(device) for a in b] for b in all_segment_level_stroke_out] + batch_segment_level_stroke_length = [[torch.LongTensor(a).to(device).unsqueeze(-1) for a in b] for b in all_segment_level_stroke_length] + batch_segment_level_term = [[torch.FloatTensor(a).to(device) for a in b] for b in all_segment_level_term] + batch_segment_level_char = [[torch.LongTensor(a).to(device) for a in b] for b in all_segment_level_char] + batch_segment_level_char_length = [[torch.LongTensor(a).to(device).unsqueeze(-1) for a in b] for b in all_segment_level_char_length] + + res = net([batch_sentence_level_stroke_in, batch_sentence_level_stroke_out, batch_sentence_level_stroke_length, + batch_sentence_level_term, batch_sentence_level_char, batch_sentence_level_char_length, + batch_word_level_stroke_in, batch_word_level_stroke_out, batch_word_level_stroke_length, + batch_word_level_term, batch_word_level_char, batch_word_level_char_length, batch_segment_level_stroke_in, + batch_segment_level_stroke_out, batch_segment_level_stroke_length, batch_segment_level_term, + batch_segment_level_char, batch_segment_level_char_length]) + + total_loss, sentence_losses, word_losses, segment_losses = res + + valid_writer_all.add_scalar('ALL/total_loss', total_loss, timestep) + + if sentence_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 + + valid_writer_all.add_scalar('ALL/total_sentence_loss', total_sentence_loss, timestep) + valid_writer_sentence.add_scalar('Loss/mean_W_consistency_loss', mean_sentence_W_consistency_loss, timestep) + if ORIGINAL: + valid_writer_sentence.add_scalar('Loss/mean_ORIGINAL_loss', mean_ORIGINAL_sentence_termination_loss + mean_ORIGINAL_sentence_loc_reconstruct_loss + mean_ORIGINAL_sentence_touch_reconstruct_loss, timestep) + valid_writer_sentence.add_scalar('Z_LOSS/mean_ORIGINAL_termination_loss', mean_ORIGINAL_sentence_termination_loss, timestep) + valid_writer_sentence.add_scalar('Loss_Loc/mean_ORIGINAL_loc_reconstruct_loss', mean_ORIGINAL_sentence_loc_reconstruct_loss, timestep) + valid_writer_sentence.add_scalar('Z_LOSS/mean_ORIGINAL_touch_reconstruct_loss', mean_ORIGINAL_sentence_touch_reconstruct_loss, timestep) + if TYPE_A: + valid_writer_sentence.add_scalar('Loss/mean_TYPE_A_loss', mean_TYPE_A_sentence_termination_loss + mean_TYPE_A_sentence_loc_reconstruct_loss + mean_TYPE_A_sentence_touch_reconstruct_loss, timestep) + valid_writer_sentence.add_scalar('Z_LOSS/mean_TYPE_A_termination_loss', mean_TYPE_A_sentence_termination_loss, timestep) + valid_writer_sentence.add_scalar('Loss_Loc/mean_TYPE_A_loc_reconstruct_loss', mean_TYPE_A_sentence_loc_reconstruct_loss, timestep) + valid_writer_sentence.add_scalar('Z_LOSS/mean_TYPE_A_touch_reconstruct_loss', mean_TYPE_A_sentence_touch_reconstruct_loss, timestep) + valid_writer_sentence.add_scalar('Z_LOSS/mean_TYPE_A_WC_reconstruct_loss', mean_TYPE_A_sentence_WC_reconstruct_loss, timestep) + if TYPE_B: + valid_writer_sentence.add_scalar('Loss/mean_TYPE_B_loss', mean_TYPE_B_sentence_termination_loss + mean_TYPE_B_sentence_loc_reconstruct_loss + mean_TYPE_B_sentence_touch_reconstruct_loss, timestep) + valid_writer_sentence.add_scalar('Z_LOSS/mean_TYPE_B_termination_loss', mean_TYPE_B_sentence_termination_loss, timestep) + valid_writer_sentence.add_scalar('Loss_Loc/mean_TYPE_B_loc_reconstruct_loss', mean_TYPE_B_sentence_loc_reconstruct_loss, timestep) + valid_writer_sentence.add_scalar('Z_LOSS/mean_TYPE_B_touch_reconstruct_loss', mean_TYPE_B_sentence_touch_reconstruct_loss, timestep) + valid_writer_sentence.add_scalar('Z_LOSS/mean_TYPE_B_WC_reconstruct_loss', mean_TYPE_B_sentence_WC_reconstruct_loss, timestep) + + if word_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 + valid_writer_all.add_scalar('ALL/total_word_loss', total_word_loss, timestep) + valid_writer_word.add_scalar('Loss/mean_W_consistency_loss', mean_word_W_consistency_loss, timestep) + + if ORIGINAL: + valid_writer_word.add_scalar('Loss/mean_ORIGINAL_loss', mean_ORIGINAL_word_termination_loss + mean_ORIGINAL_word_loc_reconstruct_loss + mean_ORIGINAL_word_touch_reconstruct_loss, timestep) + valid_writer_word.add_scalar('Z_LOSS/mean_ORIGINAL_termination_loss', mean_ORIGINAL_word_termination_loss, timestep) + valid_writer_word.add_scalar('Loss_Loc/mean_ORIGINAL_loc_reconstruct_loss', mean_ORIGINAL_word_loc_reconstruct_loss, timestep) + valid_writer_word.add_scalar('Z_LOSS/mean_ORIGINAL_touch_reconstruct_loss', mean_ORIGINAL_word_touch_reconstruct_loss, timestep) + if TYPE_A: + valid_writer_word.add_scalar('Loss/mean_TYPE_A_loss', mean_TYPE_A_word_termination_loss + mean_TYPE_A_word_loc_reconstruct_loss + mean_TYPE_A_word_touch_reconstruct_loss, timestep) + valid_writer_word.add_scalar('Z_LOSS/mean_TYPE_A_termination_loss', mean_TYPE_A_word_termination_loss, timestep) + valid_writer_word.add_scalar('Loss_Loc/mean_TYPE_A_loc_reconstruct_loss', mean_TYPE_A_word_loc_reconstruct_loss, timestep) + valid_writer_word.add_scalar('Z_LOSS/mean_TYPE_A_touch_reconstruct_loss', mean_TYPE_A_word_touch_reconstruct_loss, timestep) + valid_writer_word.add_scalar('Z_LOSS/mean_TYPE_A_WC_reconstruct_loss', mean_TYPE_A_word_WC_reconstruct_loss, timestep) + if TYPE_B: + valid_writer_word.add_scalar('Loss/mean_TYPE_B_loss', mean_TYPE_B_word_termination_loss + mean_TYPE_B_word_loc_reconstruct_loss + mean_TYPE_B_word_touch_reconstruct_loss, timestep) + valid_writer_word.add_scalar('Z_LOSS/mean_TYPE_B_termination_loss', mean_TYPE_B_word_termination_loss, timestep) + valid_writer_word.add_scalar('Loss_Loc/mean_TYPE_B_loc_reconstruct_loss', mean_TYPE_B_word_loc_reconstruct_loss, timestep) + valid_writer_word.add_scalar('Z_LOSS/mean_TYPE_B_touch_reconstruct_loss', mean_TYPE_B_word_touch_reconstruct_loss, timestep) + valid_writer_word.add_scalar('Z_LOSS/mean_TYPE_B_WC_reconstruct_loss', mean_TYPE_B_word_WC_reconstruct_loss, timestep) + if TYPE_C: + valid_writer_word.add_scalar('Loss/mean_TYPE_C_loss', mean_TYPE_C_word_termination_loss + mean_TYPE_C_word_loc_reconstruct_loss + mean_TYPE_C_word_touch_reconstruct_loss, timestep) + valid_writer_word.add_scalar('Z_LOSS/mean_TYPE_C_termination_loss', mean_TYPE_C_word_termination_loss, timestep) + valid_writer_word.add_scalar('Loss_Loc/mean_TYPE_C_loc_reconstruct_loss', mean_TYPE_C_word_loc_reconstruct_loss, timestep) + valid_writer_word.add_scalar('Z_LOSS/mean_TYPE_C_touch_reconstruct_loss', mean_TYPE_C_word_touch_reconstruct_loss, timestep) + valid_writer_word.add_scalar('Z_LOSS/mean_TYPE_C_WC_reconstruct_loss', mean_TYPE_C_word_WC_reconstruct_loss, timestep) + if TYPE_D: + valid_writer_word.add_scalar('Loss/mean_TYPE_D_loss', mean_TYPE_D_word_termination_loss + mean_TYPE_D_word_loc_reconstruct_loss + mean_TYPE_D_word_touch_reconstruct_loss, timestep) + valid_writer_word.add_scalar('Z_LOSS/mean_TYPE_D_termination_loss', mean_TYPE_D_word_termination_loss, timestep) + valid_writer_word.add_scalar('Loss_Loc/mean_TYPE_D_loc_reconstruct_loss', mean_TYPE_D_word_loc_reconstruct_loss, timestep) + valid_writer_word.add_scalar('Z_LOSS/mean_TYPE_D_touch_reconstruct_loss', mean_TYPE_D_word_touch_reconstruct_loss, timestep) + valid_writer_word.add_scalar('Z_LOSS/mean_TYPE_D_WC_reconstruct_loss', mean_TYPE_D_word_WC_reconstruct_loss, timestep) + + if segment_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 + valid_writer_all.add_scalar('ALL/total_segment_loss', total_segment_loss, timestep) + valid_writer_segment.add_scalar('Loss/mean_W_consistency_loss', mean_segment_W_consistency_loss, timestep) + if ORIGINAL: + valid_writer_segment.add_scalar('Loss/mean_ORIGINAL_loss', mean_ORIGINAL_segment_termination_loss + mean_ORIGINAL_segment_loc_reconstruct_loss + mean_ORIGINAL_segment_touch_reconstruct_loss, timestep) + valid_writer_segment.add_scalar('Z_LOSS/mean_ORIGINAL_termination_loss', mean_ORIGINAL_segment_termination_loss, timestep) + valid_writer_segment.add_scalar('Loss_Loc/mean_ORIGINAL_loc_reconstruct_loss', mean_ORIGINAL_segment_loc_reconstruct_loss, timestep) + valid_writer_segment.add_scalar('Z_LOSS/mean_ORIGINAL_touch_reconstruct_loss', mean_ORIGINAL_segment_touch_reconstruct_loss, timestep) + if TYPE_A: + valid_writer_segment.add_scalar('Loss/mean_TYPE_A_loss', mean_TYPE_A_segment_termination_loss + mean_TYPE_A_segment_loc_reconstruct_loss + mean_TYPE_A_segment_touch_reconstruct_loss, timestep) + valid_writer_segment.add_scalar('Z_LOSS/mean_TYPE_A_termination_loss', mean_TYPE_A_segment_termination_loss, timestep) + valid_writer_segment.add_scalar('Loss_Loc/mean_TYPE_A_loc_reconstruct_loss', mean_TYPE_A_segment_loc_reconstruct_loss, timestep) + valid_writer_segment.add_scalar('Z_LOSS/mean_TYPE_A_touch_reconstruct_loss', mean_TYPE_A_segment_touch_reconstruct_loss, timestep) + valid_writer_segment.add_scalar('Z_LOSS/mean_TYPE_A_WC_reconstruct_loss', mean_TYPE_A_segment_WC_reconstruct_loss, timestep) + if TYPE_B: + valid_writer_segment.add_scalar('Loss/mean_TYPE_B_loss', mean_TYPE_B_segment_termination_loss + mean_TYPE_B_segment_loc_reconstruct_loss + mean_TYPE_B_segment_touch_reconstruct_loss, timestep) + valid_writer_segment.add_scalar('Z_LOSS/mean_TYPE_B_termination_loss', mean_TYPE_B_segment_termination_loss, timestep) + valid_writer_segment.add_scalar('Loss_Loc/mean_TYPE_B_loc_reconstruct_loss', mean_TYPE_B_segment_loc_reconstruct_loss, timestep) + valid_writer_segment.add_scalar('Z_LOSS/mean_TYPE_B_touch_reconstruct_loss', mean_TYPE_B_segment_touch_reconstruct_loss, timestep) + valid_writer_segment.add_scalar('Z_LOSS/mean_TYPE_B_WC_reconstruct_loss', mean_TYPE_B_segment_WC_reconstruct_loss, timestep) + + if timestep % (num_writer * num_samples * 1000) == 0.0: + torch.save({'timestep': timestep, + 'model_state_dict': net.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'loss': total_loss.item(), + }, 'model/'+str(timestep)+'.pt') + + writer.close() + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Arguments for training the handwriting synthesis network.') + + parser.add_argument('--divider', type=float, default=5.0) + parser.add_argument('--weight_dim', type=int, default=256) + parser.add_argument('--sample', type=int, default=2) + parser.add_argument('--device', type=int, default=1) + parser.add_argument('--num_layers', type=int, default=3) + parser.add_argument('--num_writer', type=int, default=1) + parser.add_argument('--lr', type=float, default=0.001) + + parser.add_argument('--sentence_loss', type=int, default=1) + parser.add_argument('--word_loss', type=int, default=1) + parser.add_argument('--segment_loss', type=int, default=1) + + parser.add_argument('--TYPE_A', type=int, default=1) + parser.add_argument('--TYPE_B', type=int, default=1) + parser.add_argument('--TYPE_C', type=int, default=1) + parser.add_argument('--TYPE_D', type=int, default=1) + parser.add_argument('--ORIGINAL', type=int, default=1) + + parser.add_argument('--VALIDATION', type=int, default=1) + parser.add_argument('--no_char', type=int, default=0) + parser.add_argument('--REC', type=int, default=1) + parser.add_argument('--CHECKPOINT', type=int, default=0) + + main(parser.parse_args()) + diff --git a/model/250000.pt b/model/250000.pt new file mode 100644 index 0000000000000000000000000000000000000000..f0e3930570b27362053eed7f5ff939fe50be1cc4 --- /dev/null +++ b/model/250000.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e9f24a7b372123a489ff79e82d3c33a1d0a227409b3ae495f054ae518cbabb9b +size 363331941 diff --git a/sample.py b/sample.py new file mode 100644 index 0000000000000000000000000000000000000000..7933d34e0fbee9714169b6120a930fde70095f2c --- /dev/null +++ b/sample.py @@ -0,0 +1,328 @@ +import os +import torch +import pickle +import argparse +import numpy as np +from helper import * +from PIL import Image +import torch.nn as nn +import torch.optim as optim +from config.GlobalVariables import * +from tensorboardX import SummaryWriter +from SynthesisNetwork import SynthesisNetwork +from DataLoader import DataLoader + +def main(params): + np.random.seed(0) + torch.manual_seed(0) + + device = 'cpu' + dl = DataLoader(num_writer=1, num_samples=10, divider=5.0, datadir='./data/writers') + net = SynthesisNetwork(weight_dim=256, num_layers=3).to(device) + + if not torch.cuda.is_available(): + try: # retrained model also contains loss in dict + net.load_state_dict(torch.load('./model/250000.pt', map_location=torch.device('cpu'))["model_state_dict"]) + except: + net.load_state_dict(torch.load('./model/250000.pt', map_location=torch.device('cpu'))) + + [_, _, _, _, _, _, all_word_level_stroke_in, all_word_level_stroke_out, all_word_level_stroke_length, all_word_level_term, all_word_level_char, all_word_level_char_length, all_segment_level_stroke_in, all_segment_level_stroke_out, all_segment_level_stroke_length, all_segment_level_term,all_segment_level_char, all_segment_level_char_length] = dl.next_batch(TYPE='TRAIN', uid=params.writer_id, tids=[i for i in range(params.num_samples)]) + + batch_word_level_stroke_in = [torch.FloatTensor(a).to(device) for a in all_word_level_stroke_in] + batch_word_level_stroke_out = [torch.FloatTensor(a).to(device) for a in all_word_level_stroke_out] + batch_word_level_stroke_length = [torch.LongTensor(a).to(device).unsqueeze(-1) for a in all_word_level_stroke_length] + batch_word_level_term = [torch.FloatTensor(a).to(device) for a in all_word_level_term] + batch_word_level_char = [torch.LongTensor(a).to(device) for a in all_word_level_char] + batch_word_level_char_length = [torch.LongTensor(a).to(device).unsqueeze(-1) for a in all_word_level_char_length] + batch_segment_level_stroke_in = [[torch.FloatTensor(a).to(device) for a in b] for b in all_segment_level_stroke_in] + batch_segment_level_stroke_out = [[torch.FloatTensor(a).to(device) for a in b] for b in all_segment_level_stroke_out] + batch_segment_level_stroke_length = [[torch.LongTensor(a).to(device).unsqueeze(-1) for a in b] for b in all_segment_level_stroke_length] + batch_segment_level_term = [[torch.FloatTensor(a).to(device) for a in b] for b in all_segment_level_term] + batch_segment_level_char = [[torch.LongTensor(a).to(device) for a in b] for b in all_segment_level_char] + batch_segment_level_char_length = [[torch.LongTensor(a).to(device).unsqueeze(-1) for a in b] for b in all_segment_level_char_length] + + if params.generating_default == 1: + with torch.no_grad(): + commands_list = net.sample([ batch_word_level_stroke_in, batch_word_level_stroke_out, batch_word_level_stroke_length, batch_word_level_term, batch_word_level_char, batch_word_level_char_length, batch_segment_level_stroke_in, batch_segment_level_stroke_out, batch_segment_level_stroke_length, batch_segment_level_term, batch_segment_level_char, batch_segment_level_char_length]) + [t_commands, o_commands, a_commands, b_commands, c_commands, d_commands] = commands_list + dst = Image.new('RGB', (750, 640)) + dst.paste(draw_commands(t_commands), (0, 0)) + dst.paste(draw_commands(o_commands), (0, 160)) + dst.paste(draw_commands(a_commands), (0, 320)) + dst.paste(draw_commands(d_commands), (0, 480)) + dst.save(f'results/default.png') + + with torch.no_grad(): + word_inf_state_out = net.inf_state_fc1(batch_word_level_stroke_out[0]) + word_inf_state_out = net.inf_state_relu(word_inf_state_out) + word_inf_state_out, _ = net.inf_state_lstm(word_inf_state_out) + + user_word_level_char = batch_word_level_char[0] + user_word_level_term = batch_word_level_term[0] + + original_Wc = [] + word_batch_id = 0 + + curr_seq_len = batch_word_level_stroke_length[0][word_batch_id][0] + curr_char_len = batch_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(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 = net.char_vec_fc_1(char_vector) + char_vector_1 = net.char_vec_relu_1(char_vector_1) + + char_out_1 = char_vector_1.unsqueeze(0) + char_out_1, (c,h) = net.char_lstm_1(char_out_1) + char_out_1 = char_out_1.squeeze(0) + char_out_1 = net.char_vec_fc2_1(char_out_1) + char_matrix_1 = char_out_1.view([-1,1,256,256]) + 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) + mean_global_W = torch.mean(W, 0) + + + def sample_word(target_word): + available_segments = {} + for sid, sentence in enumerate(all_segment_level_char[0]): + for wid, word in enumerate(sentence): + segment = ''.join([CHARACTERS[i] for i in word]) + split_ids = np.asarray(np.nonzero(all_segment_level_term[0][sid][wid])) + + if segment in available_segments: + available_segments[segment].append([all_segment_level_stroke_out[0][sid][wid][:all_segment_level_stroke_length[0][sid][wid]], split_ids]) + else: + available_segments[segment] = [[all_segment_level_stroke_out[0][sid][wid][:all_segment_level_stroke_length[0][sid][wid]], split_ids]] + + index = 0 + all_W_c = [] + + while index <= len(target_word): + available = False + for end_index in range(len(target_word), index, -1): + segment = target_word[index:end_index] + # print (segment) + + if segment in available_segments: + # print (f'in dic - {segment}') + available = True + candidates = available_segments[segment] + segment_level_stroke_out, split_ids = candidates[np.random.randint(len(candidates))] + out = net.inf_state_fc1(torch.FloatTensor(segment_level_stroke_out).to(device).unsqueeze(0)) + out = net.inf_state_relu(out) + seg_W_c, _ = net.inf_state_lstm(out) + tmp = [] + for id in split_ids[0]: + tmp.append(seg_W_c[0, id].squeeze()) + all_W_c.append(tmp) + index = end_index + + if index == len(target_word): + break + + if not available: + character = target_word[index] + # print (f'no dic - {character}') + char_vector = torch.eye(len(CHARACTERS))[CHARACTERS.index(character)].to(device).unsqueeze(0) + out = net.char_vec_fc_1(char_vector) + out = net.char_vec_relu_1(out) + out, _ = net.char_lstm_1(out.unsqueeze(0)) + out = out.squeeze(0) + out = net.char_vec_fc2_1(out) + char_matrix = out.view([-1, 256, 256]) + TYPE_A_WC = torch.bmm(char_matrix, mean_global_W.repeat(char_matrix.size(0), 1).unsqueeze(2)).squeeze() + all_W_c.append([TYPE_A_WC]) + index += 1 + + + all_commands = [] + current_id = 0 + while True: + word_Wc_rec_TYPE_D = [] + TYPE_D_REF = [] + cid = 0 + for segment_batch_id in range(len(all_W_c)): + if len(TYPE_D_REF) == 0: + for each_segment_Wc in all_W_c[segment_batch_id]: + if cid >= current_id: + word_Wc_rec_TYPE_D.append(each_segment_Wc) + cid += 1 + if len(word_Wc_rec_TYPE_D) > 0: + TYPE_D_REF.append(all_W_c[segment_batch_id][-1]) + else: + for each_segment_Wc in all_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) = net.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(all_W_c[segment_batch_id][-1]) + WC_ = torch.stack(word_Wc_rec_TYPE_D) + tmp_commands, res = net.sample_from_w_fix(WC_, target_word) + current_id = current_id + res + if len(all_commands) == 0: + all_commands.append(tmp_commands) + else: + all_commands.append(tmp_commands[1:]) + if res < 0 or current_id >= len(target_word): + break + + # tmp_commands = net.sample_from_w_fix(torch.stack(tmp_WC), _, target_word) + + commands = [] + px, py = 0, 100 + for coms in all_commands: + for i, [dx, dy, t] in enumerate(coms): + x = px + dx * 5 + y = py + dy * 5 + commands.append([x,y,t]) + px, py = x, y + commands = np.asarray(commands) + commands[:, 0] -= np.min(commands[:, 0]) + + return commands + + def sample(target_sentence): + words = target_sentence.split(' ') + + im = Image.fromarray(np.zeros([160, 750])) + dr = ImageDraw.Draw(im) + width = 50 + + for word in words: + all_commands = sample_word(word) + + for [x,y,t] in all_commands: + if t == 0: + dr.line((px+width, py, x+width, y), 255, 1) + px, py = x, y + width += np.max(all_commands[:, 0]) + 25 + + # im.convert("RGB").save(f'results/{target_word}.png') + im.convert("RGB").save(f'results/hello.png') + + def sample_word2(target_word): + available_segments = {} + for sid, sentence in enumerate(all_segment_level_char[0]): + for wid, word in enumerate(sentence): + segment = ''.join([CHARACTERS[i] for i in word]) + split_ids = np.asarray(np.nonzero(all_segment_level_term[0][sid][wid])) + + if segment in available_segments: + available_segments[segment].append([all_segment_level_stroke_in[0][sid][wid][:all_segment_level_stroke_length[0][sid][wid]], all_segment_level_stroke_out[0][sid][wid][:all_segment_level_stroke_length[0][sid][wid]], split_ids]) + else: + available_segments[segment] = [[all_segment_level_stroke_in[0][sid][wid][:all_segment_level_stroke_length[0][sid][wid]], all_segment_level_stroke_out[0][sid][wid][:all_segment_level_stroke_length[0][sid][wid]], split_ids]] + + index = 0 + all_W_c = [] + + all_commands = [] + know_chars = [] + while index <= len(target_word): + available = False + for end_index in range(len(target_word), index, -1): + segment = target_word[index:end_index] + if segment in available_segments: + # print (f'in dic - {segment}') + available = True + candidates = available_segments[segment] + segment_level_stroke_in, segment_level_stroke_out, split_ids = candidates[np.random.randint(len(candidates))] + out = net.inf_state_fc1(torch.FloatTensor(segment_level_stroke_in).to(device).unsqueeze(0)) + out = net.inf_state_relu(out) + seg_W_c, _ = net.inf_state_lstm(out) + tmp = [] + for id in split_ids[0]: + # print (id) + tmp.append(seg_W_c[0, id].squeeze()) + all_W_c.append(tmp) + index = end_index + for i in range(index, end_index): + know_chars.append(i) + + commands = [] + px, py = 0, 100 + for i, [dx, dy, t] in enumerate(segment_level_stroke_out): + x = px + dx * 5 + y = py + dy * 5 + commands.append([x,y,t]) + px, py = x, y + commands = np.asarray(commands) + commands[:, 0] -= np.min(commands[:, 0]) + all_commands.append(commands) + + if index == len(target_word): + break + + if not available: + character = target_word[index] + # print (f'no dic - {character}') + char_vector = torch.eye(len(CHARACTERS))[CHARACTERS.index(character)].to(device).unsqueeze(0) + out = net.char_vec_fc_1(char_vector) + out = net.char_vec_relu_1(out) + out, _ = net.char_lstm_1(out.unsqueeze(0)) + out = out.squeeze(0) + out = net.char_vec_fc2_1(out) + char_matrix = out.view([-1, 256, 256]) + TYPE_A_WC = torch.bmm(char_matrix, mean_global_W.repeat(char_matrix.size(0), 1).unsqueeze(2)).squeeze().unsqueeze(0) + index += 1 + + temp_commands = net.sample_from_w(TYPE_A_WC, character) + + commands = [] + px, py = 0, 100 + for i, [dx, dy, t] in enumerate(temp_commands): + x = px + dx * 5 + y = py + dy * 5 + commands.append([x,y,t]) + px, py = x, y + commands = np.asarray(commands) + commands[:, 0] -= np.min(commands[:, 0]) + all_commands.append(commands) + return all_commands + + def sample2(target_sentence): + words = target_sentence.split(' ') + + im = Image.fromarray(np.zeros([160, 750])) + dr = ImageDraw.Draw(im) + width = 50 + + for word in words: + all_commands = sample_word2(word) + + for c in all_commands: + for [x,y,t] in c: + if t == 0: + dr.line((px+width, py, x+width, y), 255, 1) + px, py = x, y + width += np.max(c[:, 0]) + 5 + + width += 25 + + # im.convert("RGB").save(f'results/{target_word}.png') + im.convert("RGB").save(f'results/hello2.png') + + while True: + target_word = input("Type a sentence to generate : ") + if len(target_word) > 0: + if params.direct_use == 0: + sample(target_word) + else: + sample2(target_word) + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Arguments for generating samples with the handwriting synthesis model.') + + parser.add_argument('--writer_id', type=int, default=80) + parser.add_argument('--num_samples', type=int, default=10) + parser.add_argument('--generating_default', type=int, default=0) + parser.add_argument('--direct_use', type=int, default=0) + main(parser.parse_args()) +