Spaces:
Sleeping
Sleeping
File size: 2,923 Bytes
b762e56 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 |
# 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')
|