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import os
import shutil
import numpy as np
import torch
import torch.nn.functional as F
from torchvision.utils import save_image
from dataloader import get_loader
from models.model_main import ModelMain
from models.transformers import denumericalize
from options import get_parser_main_model
from data_utils.svg_utils import render
from models.util_funcs import svg2img, cal_iou
from tqdm import tqdm
from PIL import Image
def test_main_model(opts):
if opts.streamlit:
import streamlit as st
if opts.dir_res:
dir_res = os.path.join(opts.dir_res, "results")
if os.path.exists(dir_res):
shutil.rmtree(dir_res)
os.mkdir(os.path.join(opts.dir_res, "results"))
else:
dir_res = os.path.join(f"{opts.exp_path}", "experiments/", opts.name_exp, "results")
test_loader = get_loader(opts.data_root, opts.img_size, opts.language, opts.char_num, opts.max_seq_len, opts.dim_seq, opts.batch_size, 'test')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print("Inference With Device:", device)
if opts.streamlit:
def set_img(key: str, img: Image.Image):
st.session_state[key] = img
st.write("Loading Model Weight...")
st.write("Inference With Device:", device)
model_main = ModelMain(opts)
path_ckpt = os.path.join(f"{opts.model_path}")
model_main.load_state_dict(torch.load(path_ckpt, map_location=device)['model'])
model_main.to(device)
model_main.eval()
with torch.no_grad():
for test_idx, test_data in enumerate(test_loader):
for key in test_data: test_data[key] = test_data[key].to(device)
print("testing font %04d ..."%test_idx)
dir_save = os.path.join(dir_res, "%04d"%test_idx)
if not os.path.exists(dir_save):
os.mkdir(dir_save)
os.mkdir(os.path.join(dir_save, "imgs"))
os.mkdir(os.path.join(dir_save, "svgs_single"))
os.mkdir(os.path.join(dir_save, "svgs_merge"))
svg_merge_dir = os.path.join(dir_save, "svgs_merge")
iou_max = np.zeros(opts.char_num)
idx_best_sample = np.zeros(opts.char_num)
# syn_svg_merge_f = open(os.path.join(svg_merge_dir, f"{opts.name_ckpt}_syn_merge_{test_idx}_rand_{sample_idx}.html"), 'w')
syn_svg_merge_f = open(os.path.join(svg_merge_dir, f"{opts.name_ckpt}_syn_merge_{test_idx}.html"), 'w')
for sample_idx in tqdm(range(opts.n_samples)):
ret_dict_test, loss_dict_test = model_main(test_data, mode='test')
svg_sampled = ret_dict_test['svg']['sampled_1']
sampled_svg_2 = ret_dict_test['svg']['sampled_2']
img_trg = ret_dict_test['img']['trg']
img_output = ret_dict_test['img']['out']
trg_seq_gt = ret_dict_test['svg']['trg']
img_sample_merge = torch.cat((img_trg.data, img_output.data), -2)
save_file_merge = os.path.join(dir_save, "imgs", f"merge_{opts.img_size}.png")
save_image(img_sample_merge, save_file_merge, nrow=8, normalize=True)
if opts.streamlit:
st.progress((sample_idx+1)/opts.n_samples, f"Generating Font Sample {sample_idx+1} Please wait...")
im = Image.open(save_file_merge)
set_img(opts.OUTPUT_IMG_KEY, im.copy())
st.image(im, caption=f"sample {sample_idx+1}")
for char_idx in tqdm(range(opts.char_num)):
img_gt = (1.0 - img_trg[char_idx,...]).data
save_file_gt = os.path.join(dir_save,"imgs", f"{char_idx:02d}_gt.png")
save_image(img_gt, save_file_gt, normalize=True)
img_sample = (1.0 - img_output[char_idx,...]).data
save_file = os.path.join(dir_save,"imgs", f"{char_idx:02d}_{opts.img_size}.png")
save_image(img_sample, save_file, normalize=True)
# write results w/o parallel refinement
svg_dec_out = svg_sampled.clone().detach()
for i, one_seq in tqdm(enumerate(svg_dec_out)):
syn_svg_outfile = os.path.join(os.path.join(dir_save, "svgs_single"), f"syn_{i:02d}_{sample_idx}_wo_refine.svg")
syn_svg_f_ = open(syn_svg_outfile, 'w')
try:
svg = render(one_seq.cpu().numpy())
syn_svg_f_.write(svg)
# syn_svg_merge_f.write(svg)
if i > 0 and i % 13 == 12:
syn_svg_f_.write('<br>')
# syn_svg_merge_f.write('<br>')
except:
continue
syn_svg_f_.close()
# write results w/ parallel refinement
svg_dec_out = sampled_svg_2.clone().detach()
for i, one_seq in tqdm(enumerate(svg_dec_out)):
syn_svg_outfile = os.path.join(os.path.join(dir_save, "svgs_single"), f"syn_{i:02d}_{sample_idx}_refined.svg")
syn_svg_f = open(syn_svg_outfile, 'w')
try:
svg = render(one_seq.cpu().numpy())
syn_svg_f.write(svg)
#syn_svg_merge_f.write(svg)
#if i > 0 and i % 13 == 12:
# syn_svg_merge_f.write('<br>')
except:
continue
syn_svg_f.close()
syn_img_outfile = syn_svg_outfile.replace('.svg', '.png')
svg2img(syn_svg_outfile, syn_img_outfile, img_size=opts.img_size)
iou_tmp, l1_tmp = cal_iou(syn_img_outfile, os.path.join(dir_save, "imgs", f"{i:02d}_{opts.img_size}.png"))
iou_tmp = iou_tmp
if iou_tmp > iou_max[i]:
iou_max[i] = iou_tmp
idx_best_sample[i] = sample_idx
for i in tqdm(range(opts.char_num)):
# print(idx_best_sample[i])
syn_svg_outfile_best = os.path.join(os.path.join(dir_save, "svgs_single"), f"syn_{i:02d}_{int(idx_best_sample[i])}_refined.svg")
syn_svg_merge_f.write(open(syn_svg_outfile_best, 'r').read())
if i > 0 and i % 13 == 12:
syn_svg_merge_f.write('<br>')
svg_target = trg_seq_gt.clone().detach()
tgt_commands_onehot = F.one_hot(svg_target[:, :, :1].long(), 4).squeeze()
tgt_args_denum = denumericalize(svg_target[:, :, 1:])
svg_target = torch.cat([tgt_commands_onehot, tgt_args_denum], dim=-1)
for i, one_gt_seq in enumerate(svg_target):
# gt_svg_outfile = os.path.join(os.path.join(dir_save, "svgs_single"), f"gt_{i:02d}.svg")
# gt_svg_f = open(gt_svg_outfile, 'w')
gt_svg = render(one_gt_seq.cpu().numpy())
# gt_svg_f.write(gt_svg)
syn_svg_merge_f.write(gt_svg)
# gt_svg_f.close()
if i > 0 and i % 13 == 12:
syn_svg_merge_f.write('<br>')
syn_svg_merge_f.close()
return im
def main():
opts = get_parser_main_model().parse_args()
opts.name_exp = opts.name_exp + '_' + opts.model_name
experiment_dir = os.path.join(f"{opts.exp_path}","experiments", opts.name_exp)
print(f"Testing on experiment {opts.name_exp}...")
# Dump options
test_main_model(opts)
if __name__ == "__main__":
main() |