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test.py
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test.py
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import argparse
import os
from parse_config import cfg, cfg_from_file, assert_and_infer_cfg
import torch
from data_loader.loader import ScriptDataset
import pickle
from models.model import SDT_Generator
import tqdm
from utils.util import writeCache, dxdynp_to_list, coords_render
import lmdb
from torch.utils.data import DataLoader
def main(opt):
""" load config file into cfg"""
cfg_from_file(opt.cfg_file)
assert_and_infer_cfg()
"""setup data_loader instances"""
test_dataset = ScriptDataset(
# data CHINESE False 15
cfg.DATA_LOADER.PATH, cfg.DATA_LOADER.DATASET, cfg.TEST.ISTRAIN, cfg.MODEL.NUM_IMGS
)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=cfg.TRAIN.IMS_PER_BATCH,
shuffle=True,
sampler=None,
drop_last=False,
collate_fn=test_dataset.collate_fn_,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
# num_workers=0
)
char_dict = test_dataset.char_dict
writer_dict = test_dataset.writer_dict
os.makedirs(os.path.join(opt.save_dir, 'test'), exist_ok=True)
test_env = lmdb.open(os.path.join(opt.save_dir, 'test'), map_size=1073741824)
pickle.dump(writer_dict, open(os.path.join(opt.save_dir, 'writer_dict.pkl'), 'wb'))
pickle.dump(char_dict, open(os.path.join(opt.save_dir, 'character_dict.pkl'), 'wb'))
"""build model architecture"""
model = SDT_Generator(num_encoder_layers=cfg.MODEL.ENCODER_LAYERS,
num_head_layers=cfg.MODEL.NUM_HEAD_LAYERS,
wri_dec_layers=cfg.MODEL.WRI_DEC_LAYERS,
gly_dec_layers=cfg.MODEL.GLY_DEC_LAYERS)
# 使用nn.DataParallel model在多个GPU上运行
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = torch.nn.DataParallel(model)
else:
print("Using single GPU or CPU")
model.to('cuda' if torch.cuda.is_available() else 'cpu')
if len(opt.pretrained_model) > 0:
model_weight = torch.load(opt.pretrained_model)
model.module.load_state_dict(model_weight)
print('Loaded pretrained model from {}'.format(opt.pretrained_model))
else:
raise IOError('Input the correct checkpoint path')
model.eval()
"""calculate the total batches of generated samples"""
if opt.sample_size == 'all':
batch_samples = len(test_loader)
else:
batch_samples = int(opt.sample_size) * len(writer_dict) // cfg.TRAIN.IMS_PER_BATCH
batch_num, num_count = 0, 0
data_iter = iter(test_loader)
with torch.no_grad():
for _ in tqdm.tqdm(range(batch_samples)):
batch_num += 1
if batch_num > batch_samples:
break
else:
data = next(data_iter)
# prepare input
coords, coords_len, character_id, writer_id, img_list, char_img = data['coords'].cuda(), \
data['coords_len'].cuda(), \
data['character_id'].long().cuda(), \
data['writer_id'].long().cuda(), \
data['img_list'].cuda(), \
data['char_img'].cuda()
preds = model.module.inference(img_list, char_img, 120)
bs = character_id.shape[0]
# Start of Sequence, SOS
SOS = torch.tensor(bs * [[0, 0, 1, 0, 0]]).unsqueeze(1).to(preds)
preds = torch.cat((SOS, preds), 1) # add the SOS token like GT
preds = preds.detach().cpu().numpy()
test_cache = {}
coords = coords.detach().cpu().numpy()
if opt.store_type == 'online':
for i, pred in enumerate(preds):
# with open(os.path.join(opt.save_dir, 'test', '00.txt'), 'a', encoding='utf-8') as f:
# f.write(f"{str(i)} pred[i]:\n")
# f.write(str(pred[i]))
# f.write("\ncoords[i]:\n")
# f.write(str(coords[i]))
# f.write("\ndxdynp_to_list:\n")
# list_pred, other_pred = dxdynp_to_list(preds[i])
# f.write(str(list_pred))
# f.write("\ndxdynp_to_list_gt:\n")
# list_gt, other_gt = dxdynp_to_list(coords[i])
# f.write(str(list_gt))
pred, _ = dxdynp_to_list(preds[i])
coord, _ = dxdynp_to_list(coords[i])
data = {'coordinates': pred, 'writer_id': writer_id[i].item(),
'character_id': character_id[i].item(), 'coords_gt': coord}
data_byte = pickle.dumps(data)
data_id = str(num_count).encode('utf-8')
test_cache[data_id] = data_byte
num_count += 1
test_cache['num_sample'.encode('utf-8')] = str(num_count).encode()
writeCache(test_env, test_cache)
elif opt.store_type == 'img':
for i, pred in enumerate(preds):
sk_pil = coords_render(preds[i], split=True, width=256, height=256, thickness=8, board=0)
character = char_dict[character_id[i].item()]
save_path = os.path.join(opt.save_dir, 'test',
str(writer_id[i].item()) + '_' + character + '.png')
try:
sk_pil.save(save_path)
except:
print('error. %s, %s, %s' % (save_path, str(writer_id[i].item()), character))
else:
raise NotImplementedError('only support online or img format')
if __name__ == '__main__':
"""Parse input arguments"""
parser = argparse.ArgumentParser()
parser.add_argument('--cfg', dest='cfg_file', default='configs/CHINESE_CASIA.yml',
help='Config file for training (and optionally testing)')
parser.add_argument('--dir', dest='save_dir', default='Generated/Chinese',
help='target dir for storing the generated characters')
parser.add_argument('--pretrained_model', dest='pretrained_model',
default='checkpoint_path/checkpoint-iter199999.pth', required=True,
help='continue train model')
parser.add_argument('--store_type', dest='store_type', required=True, default='online',
help='online or img')
parser.add_argument('--sample_size', dest='sample_size', default='500', required=True,
help='randomly generate a certain number of characters for each writer')
opt = parser.parse_args()
main(opt)