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| # data loader for training main model | |
| import os | |
| import pickle | |
| import torch | |
| import torch.utils.data as data | |
| import torchvision.transforms as T | |
| import sys | |
| import numpy as np | |
| torch.multiprocessing.set_sharing_strategy('file_system') | |
| class SVGDataset(data.Dataset): | |
| def __init__(self, root_path, img_size=128, lang='eng', char_num=52, max_seq_len=51, dim_seq=10, transform=None, mode='train'): | |
| super().__init__() | |
| self.mode = mode | |
| self.img_size = img_size | |
| self.char_num = char_num | |
| self.max_seq_len = max_seq_len | |
| self.dim_seq = dim_seq | |
| self.trans = transform | |
| self.font_paths = [] | |
| self.dir_path = os.path.join(root_path, lang, self.mode) | |
| for root, dirs, files in os.walk(self.dir_path): | |
| depth = root.count('/') - self.dir_path.count('/') | |
| if depth == 0: | |
| for dir_name in dirs: | |
| self.font_paths.append(os.path.join(self.dir_path, dir_name)) | |
| self.font_paths.sort() | |
| print(f"Finished loading {mode} paths, number: {str(len(self.font_paths))}") | |
| def __getitem__(self, index): | |
| item = {} | |
| font_path = self.font_paths[index] | |
| item = {} | |
| item['class'] = torch.LongTensor(np.load(os.path.join(font_path, 'class.npy'))) | |
| item['seq_len'] = torch.LongTensor(np.load(os.path.join(font_path, 'seq_len.npy'))) | |
| item['sequence'] = torch.FloatTensor(np.load(os.path.join(font_path, 'sequence_relaxed.npy'))).view(self.char_num, self.max_seq_len, self.dim_seq) | |
| item['pts_aux'] = torch.FloatTensor(np.load(os.path.join(font_path, 'pts_aux.npy'))) | |
| item['rendered'] = torch.FloatTensor(np.load(os.path.join(font_path, 'rendered_' + str(self.img_size) + '.npy'))).view(self.char_num, self.img_size, self.img_size) / 255. | |
| item['rendered'] = self.trans(item['rendered']) | |
| item['font_id'] = torch.FloatTensor(np.load(os.path.join(font_path, 'font_id.npy')).astype(np.float32)) | |
| return item | |
| def __len__(self): | |
| return len(self.font_paths) | |
| def get_loader(root_path, img_size, lang, char_num, max_seq_len, dim_seq, batch_size, mode='train'): | |
| SetRange = T.Lambda(lambda X: 1. - X ) # convert [0, 1] -> [0, 1] | |
| transform = T.Compose([SetRange]) | |
| dataset = SVGDataset(root_path, img_size, lang, char_num, max_seq_len, dim_seq, transform, mode) | |
| dataloader = data.DataLoader(dataset, batch_size, shuffle=(mode == 'train'), num_workers=batch_size) | |
| return dataloader | |
| if __name__ == '__main__': | |
| root_path = 'data/new_data' | |
| max_seq_len = 51 | |
| dim_seq = 10 | |
| batch_size = 1 | |
| char_num = 52 | |
| loader = get_loader(root_path, char_num, max_seq_len, dim_seq, batch_size, 'train') | |
| fout = open('train_id_record_old.txt','w') | |
| for idx, batch in enumerate(loader): | |
| binary_fp = batch['font_id'].numpy()[0][0] | |
| fout.write("%05d"%int(binary_fp) + '\n') | |