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run_imitator.py
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run_imitator.py
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import torch
import torch.nn
import torch.utils.data
from tqdm import tqdm
import os
import glob
from data.dataset import PairSampleDataset
from models.imitator import Imitator
from options.test_options import TestOptions
from utils.visdom_visualizer import VisdomVisualizer
from utils.util import load_pickle_file, write_pickle_file, mkdirs, mkdir, clear_dir
import utils.cv_utils as cv_utils
__all__ = ['write_pair_info', 'scan_tgt_paths', 'meta_imitate',
'MetaCycleDataSet', 'make_dataset', 'adaptive_personalize']
@torch.no_grad()
def write_pair_info(src_info, tsf_info, out_file, imitator, only_vis):
"""
Args:
src_info:
tsf_info:
out_file:
imitator:
Returns:
"""
pair_data = dict()
pair_data['from_face_index_map'] = src_info['fim'][0][:, :, None].cpu().numpy()
pair_data['to_face_index_map'] = tsf_info['fim'][0][:, :, None].cpu().numpy()
pair_data['T'] = tsf_info['T'][0].cpu().numpy()
pair_data['warp'] = tsf_info['tsf_img'][0].cpu().numpy()
pair_data['smpls'] = torch.cat([src_info['theta'], tsf_info['theta']], dim=0).cpu().numpy()
pair_data['j2d'] = torch.cat([src_info['j2d'], tsf_info['j2d']], dim=0).cpu().numpy()
tsf_f2verts, tsf_fim, tsf_wim = imitator.render.render_fim_wim(tsf_info['cam'], tsf_info['verts'])
tsf_p2verts = tsf_f2verts[:, :, :, 0:2]
tsf_p2verts[:, :, :, 1] *= -1
T_cycle = imitator.render.cal_bc_transform(tsf_p2verts, src_info['fim'], src_info['wim'])
pair_data['T_cycle'] = T_cycle[0].cpu().numpy()
# back_face_ids = mesh.get_part_face_ids(part_type='head_back')
# tsf_p2verts[:, back_face_ids] = -2
# T_cycle_vis = imitator.render.cal_bc_transform(tsf_p2verts, src_info['fim'], src_info['wim'])
# pair_data['T_cycle_vis'] = T_cycle_vis[0].cpu().numpy()
# for key, val in pair_data.items():
# print(key, val.shape)
write_pickle_file(out_file, pair_data)
def scan_tgt_paths(tgt_path, itv=20):
if os.path.isdir(tgt_path):
all_tgt_paths = glob.glob(os.path.join(tgt_path, '*'))
all_tgt_paths.sort()
all_tgt_paths = all_tgt_paths[::itv]
else:
all_tgt_paths = [tgt_path]
return all_tgt_paths
def meta_imitate(opt, imitator, prior_tgt_path, save_imgs=True, visualizer=None):
src_path = opt.src_path
all_tgt_paths = scan_tgt_paths(prior_tgt_path, itv=40)
output_dir = opt.output_dir
out_img_dir, out_pair_dir = mkdirs([os.path.join(output_dir, 'imgs'), os.path.join(output_dir, 'pairs')])
img_pair_list = []
for t in tqdm(range(len(all_tgt_paths))):
tgt_path = all_tgt_paths[t]
preds = imitator.inference([tgt_path], visualizer=visualizer, cam_strategy=opt.cam_strategy, verbose=False)
tgt_name = os.path.split(tgt_path)[-1]
out_path = os.path.join(out_img_dir, 'pred_' + tgt_name)
if save_imgs:
cv_utils.save_cv2_img(preds[0], out_path, normalize=True)
write_pair_info(imitator.src_info, imitator.tsf_info,
os.path.join(out_pair_dir, '{:0>8}.pkl'.format(t)), imitator=imitator,
only_vis=opt.only_vis)
img_pair_list.append((src_path, tgt_path))
if save_imgs:
write_pickle_file(os.path.join(output_dir, 'pairs_meta.pkl'), img_pair_list)
class MetaCycleDataSet(PairSampleDataset):
def __init__(self, opt):
super(MetaCycleDataSet, self).__init__(opt, True)
self._name = 'MetaCycleDataSet'
def _read_dataset_paths(self):
# read pair list
self._dataset_size = 0
self._read_samples_info(None, self._opt.pkl_dir, self._opt.pair_ids_filepath)
def _read_samples_info(self, im_dir, pkl_dir, pair_ids_filepath):
"""
Args:
im_dir:
pkl_dir:
pair_ids_filepath:
Returns:
"""
# 1. load image pair list
self.im_pair_list = load_pickle_file(pair_ids_filepath)
# 2. load pkl file paths
self.all_pkl_paths = sorted(glob.glob((os.path.join(pkl_dir, '*.pkl'))))
assert len(self.im_pair_list) == len(self.all_pkl_paths), '{} != {}'.format(
len(self.im_pair_list), len(self.all_pkl_paths)
)
self._dataset_size = len(self.im_pair_list)
def __getitem__(self, item):
"""
Args:
item (int): index of self._dataset_size
Returns:
new_sample (dict): items contain
--src_inputs (torch.FloatTensor): (3+3, h, w)
--tsf_inputs (torch.FloatTensor): (3+3, h, w)
--T (torch.FloatTensor): (h, w, 2)
--head_bbox (torch.IntTensor): (4), hear 4 = [lt_x, lt_y, rt_x, rt_y]
--valid_bbox (torch.FloatTensor): (1), 1.0 valid and 0.0 invalid.
--images (torch.FloatTensor): (2, 3, h, w)
--pseudo_masks (torch.FloatTensor) : (2, 1, h, w)
--bg_inputs (torch.FloatTensor): (3+1, h, w) or (2, 3+1, h, w) if self.is_both is True
"""
im_pairs = self.im_pair_list[item]
pkl_path = self.all_pkl_paths[item]
sample = self.load_sample(im_pairs, pkl_path)
sample = self.preprocess(sample)
sample['preds'] = torch.tensor(self.load_init_preds(im_pairs[1])).float()
return sample
def load_init_preds(self, pred_path):
pred_img_name = os.path.split(pred_path)[-1]
pred_img_path = os.path.join(self._opt.preds_img_folder, 'pred_' + pred_img_name)
img = cv_utils.read_cv2_img(pred_img_path)
img = cv_utils.transform_img(img, self._opt.image_size, transpose=True)
img = img * 2 - 1
return img
def make_dataset(opt):
class Config(object):
pass
config = Config()
output_dir = opt.output_dir
config.pair_ids_filepath = os.path.join(output_dir, 'pairs_meta.pkl')
config.pkl_dir = os.path.join(output_dir, 'pairs')
config.preds_img_folder = os.path.join(output_dir, 'imgs')
config.image_size = opt.image_size
config.map_name = opt.map_name
config.uv_mapping = opt.uv_mapping
config.is_both = False
config.bg_ks = opt.bg_ks
config.ft_ks = opt.ft_ks
meta_cycle_ds = MetaCycleDataSet(opt=config)
length = len(meta_cycle_ds)
data_loader = torch.utils.data.DataLoader(
meta_cycle_ds,
batch_size=min(length, opt.batch_size),
shuffle=False,
num_workers=4,
drop_last=True)
return data_loader
def adaptive_personalize(opt, imitator, visualizer):
output_dir = opt.output_dir
mkdirs([os.path.join(output_dir, 'imgs'), os.path.join(output_dir, 'pairs')])
# TODO check if it has been computed.
print('\n\t\t\tPersonalization: meta imitation...')
imitator.personalize(opt.src_path, visualizer=None)
meta_imitate(opt, imitator, prior_tgt_path=opt.pri_path, visualizer=None, save_imgs=True)
# post tune
print('\n\t\t\tPersonalization: meta cycle finetune...')
loader = make_dataset(opt)
imitator.post_personalize(opt.output_dir, loader, visualizer=None, verbose=False)
if __name__ == "__main__":
# meta imitator
test_opt = TestOptions().parse()
if test_opt.ip:
visualizer = VisdomVisualizer(env=test_opt.name, ip=test_opt.ip, port=test_opt.port)
else:
visualizer = None
# set imitator
imitator = Imitator(test_opt)
if test_opt.post_tune:
adaptive_personalize(test_opt, imitator, visualizer)
imitator.personalize(test_opt.src_path, visualizer=visualizer)
print('\n\t\t\tPersonalization: completed...')
if test_opt.save_res:
pred_output_dir = mkdir(os.path.join(test_opt.output_dir, 'imitators'))
pred_output_dir = clear_dir(pred_output_dir)
else:
pred_output_dir = None
print('\n\t\t\tImitating `{}`'.format(test_opt.tgt_path))
tgt_paths = scan_tgt_paths(test_opt.tgt_path, itv=1)
imitator.inference(tgt_paths, tgt_smpls=None, cam_strategy='smooth',
output_dir=pred_output_dir, visualizer=visualizer, verbose=True)