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"""
# Copyright 2020 Adobe
# All Rights Reserved.
# NOTICE: Adobe permits you to use, modify, and distribute this file in
# accordance with the terms of the Adobe license agreement accompanying
# it.
"""
import os
import torch.nn.parallel
import torch.utils.data
from src.dataset.audio2landmark.audio2landmark_dataset import Audio2landmark_Dataset
from src.models.model_audio2landmark import *
from util.utils import get_n_params
import numpy as np
import pickle
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class Audio2landmark_model():
def __init__(self, opt_parser, jpg_shape=None):
'''
Init model with opt_parser
'''
print('Run on device:', device)
# Step 1 : load opt_parser
self.opt_parser = opt_parser
self.std_face_id = np.loadtxt('MakeItTalk/src/dataset/utils/STD_FACE_LANDMARKS.txt')
if(jpg_shape is not None):
self.std_face_id = jpg_shape
self.std_face_id = self.std_face_id.reshape(1, 204)
self.std_face_id = torch.tensor(self.std_face_id, requires_grad=False, dtype=torch.float).to(device)
self.eval_data = Audio2landmark_Dataset(dump_dir='MakeItTalk/examples/dump',
dump_name='random',
status='val',
num_window_frames=18,
num_window_step=1)
self.eval_dataloader = torch.utils.data.DataLoader(self.eval_data, batch_size=1,
shuffle=False, num_workers=0,
collate_fn=self.eval_data.my_collate_in_segments)
print('EVAL num videos: {}'.format(len(self.eval_data)))
# Step 3: Load model
self.G = Audio2landmark_pos(drop_out=0.5,
spk_emb_enc_size=128,
c_enc_hidden_size=256,
transformer_d_model=32, N=2, heads=2,
z_size=128, audio_dim=256)
print('G: Running on {}, total num params = {:.2f}M'.format(device, get_n_params(self.G)/1.0e6))
model_dict = self.G.state_dict()
ckpt = torch.load(opt_parser.load_a2l_G_name, map_location=torch.device('cuda'))
pretrained_dict = {k: v for k, v in ckpt['G'].items() if k.split('.')[0] not in ['comb_mlp']}
model_dict.update(pretrained_dict)
self.G.load_state_dict(model_dict)
print('======== LOAD PRETRAINED FACE ID MODEL {} ========='.format(opt_parser.load_a2l_G_name))
self.G.to(device)
''' baseline model '''
self.C = Audio2landmark_content(num_window_frames=18,
in_size=80, use_prior_net=True,
bidirectional=False, drop_out=0.5)
ckpt = torch.load(opt_parser.load_a2l_C_name, map_location=torch.device('cuda'))
self.C.load_state_dict(ckpt['model_g_face_id'])
# self.C.load_state_dict(ckpt['C'])
print('======== LOAD PRETRAINED FACE ID MODEL {} ========='.format(opt_parser.load_a2l_C_name))
self.C.to(device)
self.t_shape_idx = (27, 28, 29, 30, 33, 36, 39, 42, 45)
self.anchor_t_shape = np.loadtxt('MakeItTalk/src/dataset/utils/STD_FACE_LANDMARKS.txt')
self.anchor_t_shape = self.anchor_t_shape[self.t_shape_idx, :]
with open(os.path.join('MakeItTalk/examples', 'dump', 'emb.pickle'), 'rb') as fp:
self.test_embs = pickle.load(fp)
print('====================================')
for key in self.test_embs.keys():
print(key)
print('====================================')
def __train_face_and_pos__(self, fls, aus, embs, face_id, smooth_win=31, close_mouth_ratio=.99):
fls_without_traj = fls[:, 0, :].detach().clone().requires_grad_(False)
if (face_id.shape[0] == 1):
face_id = face_id.repeat(aus.shape[0], 1)
face_id = face_id.requires_grad_(False)
baseline_face_id = face_id.detach()
z = torch.tensor(torch.zeros(aus.shape[0], 128), requires_grad=False, dtype=torch.float).to(device)
fl_dis_pred, _, spk_encode = self.G(aus, embs * 3.0, face_id, fls_without_traj, z, add_z_spk=False)
# ADD CONTENT
from scipy.signal import savgol_filter
smooth_length = int(min(fl_dis_pred.shape[0]-1, smooth_win) // 2 * 2 + 1)
fl_dis_pred = savgol_filter(fl_dis_pred.cpu().numpy(), smooth_length, 3, axis=0)
#
''' ================ close pose-branch mouth ================== '''
fl_dis_pred = fl_dis_pred.reshape((-1, 68, 3))
index1 = list(range(60-1, 55-1, -1))
index2 = list(range(68-1, 65-1, -1))
mean_out = 0.5 * fl_dis_pred[:, 49:54] + 0.5 * fl_dis_pred[:, index1]
fl_dis_pred[:, 49:54] = mean_out * close_mouth_ratio + fl_dis_pred[:, 49:54] * (1 - close_mouth_ratio)
fl_dis_pred[:, index1] = mean_out * close_mouth_ratio + fl_dis_pred[:, index1] * (1 - close_mouth_ratio)
mean_in = 0.5 * (fl_dis_pred[:, 61:64] + fl_dis_pred[:, index2])
fl_dis_pred[:, 61:64] = mean_in * close_mouth_ratio + fl_dis_pred[:, 61:64] * (1 - close_mouth_ratio)
fl_dis_pred[:, index2] = mean_in * close_mouth_ratio + fl_dis_pred[:, index2] * (1 - close_mouth_ratio)
fl_dis_pred = fl_dis_pred.reshape(-1, 204)
''' ============================================================= '''
fl_dis_pred = torch.tensor(fl_dis_pred).to(device) * self.opt_parser.amp_pos
residual_face_id = baseline_face_id
# ''' CALIBRATION '''
baseline_pred_fls, _ = self.C(aus[:, 0:18, :], residual_face_id)
baseline_pred_fls = self.__calib_baseline_pred_fls__(baseline_pred_fls)
fl_dis_pred += baseline_pred_fls
return fl_dis_pred, face_id[0:1, :]
def __calib_baseline_pred_fls_old_(self, baseline_pred_fls, residual_face_id, aus):
mean_face_id = torch.mean(baseline_pred_fls.detach(), dim=0, keepdim=True)
residual_face_id -= mean_face_id.view(1, 204) * 1.
baseline_pred_fls, _ = self.C(aus, residual_face_id)
baseline_pred_fls[:, 48 * 3::3] *= self.opt_parser.amp_lip_x # mouth x
baseline_pred_fls[:, 48 * 3 + 1::3] *= self.opt_parser.amp_lip_y # mouth y
return baseline_pred_fls
def __calib_baseline_pred_fls__(self, baseline_pred_fls, ratio=0.5):
np_fl_dis_pred = baseline_pred_fls.detach().cpu().numpy()
K = int(np_fl_dis_pred.shape[0] * ratio)
for calib_i in range(204):
min_k_idx = np.argpartition(np_fl_dis_pred[:, calib_i], K)
m = np.mean(np_fl_dis_pred[min_k_idx[:K], calib_i])
np_fl_dis_pred[:, calib_i] = np_fl_dis_pred[:, calib_i] - m
baseline_pred_fls = torch.tensor(np_fl_dis_pred, requires_grad=False).to(device)
baseline_pred_fls[:, 48 * 3::3] *= self.opt_parser.amp_lip_x # mouth x
baseline_pred_fls[:, 48 * 3 + 1::3] *= self.opt_parser.amp_lip_y # mouth y
return baseline_pred_fls
def __train_pass__(self, au_emb=None, centerize_face=False, no_y_rotation=False, vis_fls=False):
# Step 1: init setup
self.G.eval()
self.C.eval()
data = self.eval_data
dataloader = self.eval_dataloader
# Step 2: train for each batch
for i, batch in enumerate(dataloader):
global_id, video_name = data[i][0][1][0], data[i][0][1][1][:-4]
# Step 2.1: load batch data from dataloader (in segments)
inputs_fl, inputs_au, inputs_emb = batch
keys = self.opt_parser.reuse_train_emb_list
if(len(keys) == 0):
keys = ['audio_embed']
for key in keys: # ['45hn7-LXDX8']: #['sxCbrYjBsGA']:#
# load saved emb
if(au_emb is None):
emb_val = self.test_embs[key]
else:
emb_val = au_emb[i]
inputs_emb = np.tile(emb_val, (inputs_emb.shape[0], 1))
inputs_emb = torch.tensor(inputs_emb, dtype=torch.float, requires_grad=False)
inputs_fl, inputs_au, inputs_emb = inputs_fl.to(device), inputs_au.to(device), inputs_emb.to(device)
std_fls_list, fls_pred_face_id_list, fls_pred_pos_list = [], [], []
seg_bs = 512
for j in range(0, inputs_fl.shape[0], seg_bs):
# Step 3.1: load segments
inputs_fl_segments = inputs_fl[j: j + seg_bs]
inputs_au_segments = inputs_au[j: j + seg_bs]
inputs_emb_segments = inputs_emb[j: j + seg_bs]
if(inputs_fl_segments.shape[0] < 10):
continue
input_face_id = self.std_face_id
fl_dis_pred_pos, input_face_id = \
self.__train_face_and_pos__(inputs_fl_segments, inputs_au_segments, inputs_emb_segments,
input_face_id)
fl_dis_pred_pos = (fl_dis_pred_pos + input_face_id).data.cpu().numpy()
''' solve inverse lip '''
fl_dis_pred_pos = self.__solve_inverse_lip2__(fl_dis_pred_pos)
fls_pred_pos_list += [fl_dis_pred_pos]
fake_fls_np = np.concatenate(fls_pred_pos_list)
# revise nose top point
fake_fls_np[:, 27 * 3:28 * 3] = fake_fls_np[:, 28 * 3:29 * 3] * 2 - fake_fls_np[:, 29 * 3:30 * 3]
# fake_fls_np[:, 48*3+1::3] += 0.1
# smooth
from scipy.signal import savgol_filter
fake_fls_np = savgol_filter(fake_fls_np, 5, 3, axis=0)
if(centerize_face):
std_m = np.mean(self.std_face_id.detach().cpu().numpy().reshape((1, 68, 3)),
axis=1, keepdims=True)
fake_fls_np = fake_fls_np.reshape((-1, 68, 3))
fake_fls_np = fake_fls_np - np.mean(fake_fls_np, axis=1, keepdims=True) + std_m
fake_fls_np = fake_fls_np.reshape((-1, 68 * 3))
if(no_y_rotation):
std = self.std_face_id.detach().cpu().numpy().reshape(68, 3)
std_t_shape = std[self.t_shape_idx, :]
fake_fls_np = fake_fls_np.reshape((fake_fls_np.shape[0], 68, 3))
frame_t_shape = fake_fls_np[:, self.t_shape_idx, :]
from util.icp import icp
from scipy.spatial.transform import Rotation as R
for i in range(frame_t_shape.shape[0]):
T, distance, itr = icp(frame_t_shape[i], std_t_shape)
landmarks = np.hstack((frame_t_shape[i], np.ones((9, 1))))
rot_mat = T[:3, :3]
r = R.from_dcm(rot_mat).as_euler('xyz')
r = [0., r[1], r[2]]
r = R.from_euler('xyz', r).as_dcm()
# print(frame_t_shape[i, 0], r)
landmarks = np.hstack((fake_fls_np[i] - T[:3, 3:4].T, np.ones((68, 1))))
T2 = np.hstack((r, T[:3, 3:4]))
fake_fls_np[i] = np.dot(T2, landmarks.T).T
# print(frame_t_shape[i, 0])
fake_fls_np = fake_fls_np.reshape((-1, 68 * 3))
filename = 'pred_fls_{}_{}.txt'.format(video_name.split('\\')[-1].split('/')[-1], key)
np.savetxt(os.path.join(self.opt_parser.output_folder, filename), fake_fls_np, fmt='%.6f')
# ''' Visualize result in landmarks '''
if(vis_fls):
from util.vis import Vis
Vis(fls=fake_fls_np, filename=video_name.split('\\')[-1].split('/')[-1], fps=62.5,
audio_filenam=os.path.join('MakeItTalk/examples', video_name.split('\\')[-1].split('/')[-1]+'.wav'))
def __close_face_lip__(self, fl):
facelandmark = fl.reshape(-1, 68, 3)
from util.geo_math import area_of_polygon
min_area_lip, idx = 999, 0
for i, fls in enumerate(facelandmark):
area_of_mouth = area_of_polygon(fls[list(range(60, 68)), 0:2])
if (area_of_mouth < min_area_lip):
min_area_lip = area_of_mouth
idx = i
return idx
def test(self, au_emb=None):
with torch.no_grad():
self.__train_pass__(au_emb, vis_fls=True)
def __solve_inverse_lip2__(self, fl_dis_pred_pos_numpy):
for j in range(fl_dis_pred_pos_numpy.shape[0]):
init_face = self.std_face_id.detach().cpu().numpy()
from util.geo_math import area_of_signed_polygon
fls = fl_dis_pred_pos_numpy[j].reshape(68, 3)
area_of_mouth = area_of_signed_polygon(fls[list(range(60, 68)), 0:2])
if (area_of_mouth < 0):
fl_dis_pred_pos_numpy[j, 65 * 3:66 * 3] = 0.5 *(fl_dis_pred_pos_numpy[j, 63 * 3:64 * 3] + fl_dis_pred_pos_numpy[j, 65 * 3:66 * 3])
fl_dis_pred_pos_numpy[j, 63 * 3:64 * 3] = fl_dis_pred_pos_numpy[j, 65 * 3:66 * 3]
fl_dis_pred_pos_numpy[j, 66 * 3:67 * 3] = 0.5 *(fl_dis_pred_pos_numpy[j, 62 * 3:63 * 3] + fl_dis_pred_pos_numpy[j, 66 * 3:67 * 3])
fl_dis_pred_pos_numpy[j, 62 * 3:63 * 3] = fl_dis_pred_pos_numpy[j, 66 * 3:67 * 3]
fl_dis_pred_pos_numpy[j, 67 * 3:68 * 3] = 0.5 *(fl_dis_pred_pos_numpy[j, 61 * 3:62 * 3] + fl_dis_pred_pos_numpy[j, 67 * 3:68 * 3])
fl_dis_pred_pos_numpy[j, 61 * 3:62 * 3] = fl_dis_pred_pos_numpy[j, 67 * 3:68 * 3]
p = max([j-1, 0])
fl_dis_pred_pos_numpy[j, 55 * 3+1:59 * 3+1:3] = fl_dis_pred_pos_numpy[j, 64 * 3+1:68 * 3+1:3] \
+ fl_dis_pred_pos_numpy[p, 55 * 3+1:59 * 3+1:3] \
- fl_dis_pred_pos_numpy[p, 64 * 3+1:68 * 3+1:3]
fl_dis_pred_pos_numpy[j, 59 * 3+1:60 * 3+1:3] = fl_dis_pred_pos_numpy[j, 60 * 3+1:61 * 3+1:3] \
+ fl_dis_pred_pos_numpy[p, 59 * 3+1:60 * 3+1:3] \
- fl_dis_pred_pos_numpy[p, 60 * 3+1:61 * 3+1:3]
fl_dis_pred_pos_numpy[j, 49 * 3+1:54 * 3+1:3] = fl_dis_pred_pos_numpy[j, 60 * 3+1:65 * 3+1:3] \
+ fl_dis_pred_pos_numpy[p, 49 * 3+1:54 * 3+1:3] \
- fl_dis_pred_pos_numpy[p, 60 * 3+1:65 * 3+1:3]
return fl_dis_pred_pos_numpy
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