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31bd91f 617e6a3 31bd91f | 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 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 | from scipy.spatial import ConvexHull
import torch
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
import re, itertools
import contextlib
def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False,
use_relative_movement=False, use_relative_jacobian=False):
if adapt_movement_scale:
source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume
driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume
adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)
else:
adapt_movement_scale = 1
kp_new = {k: v for k, v in kp_driving.items()}
if use_relative_movement:
kp_value_diff = (kp_driving['value'] - kp_driving_initial['value'])
kp_value_diff *= adapt_movement_scale
kp_new['value'] = kp_value_diff + kp_source['value']
if use_relative_jacobian:
jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian']))
kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian'])
return kp_new
def headpose_pred_to_degree(pred):
device = pred.device
idx_tensor = [idx for idx in range(66)]
idx_tensor = torch.FloatTensor(idx_tensor).type_as(pred).to(device)
pred = F.softmax(pred)
degree = torch.sum(pred*idx_tensor, 1) * 3 - 99
return degree
def get_rotation_matrix(yaw, pitch, roll):
yaw = yaw / 180 * 3.14
pitch = pitch / 180 * 3.14
roll = roll / 180 * 3.14
roll = roll.unsqueeze(1)
pitch = pitch.unsqueeze(1)
yaw = yaw.unsqueeze(1)
pitch_mat = torch.cat([torch.ones_like(pitch), torch.zeros_like(pitch), torch.zeros_like(pitch),
torch.zeros_like(pitch), torch.cos(pitch), -torch.sin(pitch),
torch.zeros_like(pitch), torch.sin(pitch), torch.cos(pitch)], dim=1)
pitch_mat = pitch_mat.view(pitch_mat.shape[0], 3, 3)
yaw_mat = torch.cat([torch.cos(yaw), torch.zeros_like(yaw), torch.sin(yaw),
torch.zeros_like(yaw), torch.ones_like(yaw), torch.zeros_like(yaw),
-torch.sin(yaw), torch.zeros_like(yaw), torch.cos(yaw)], dim=1)
yaw_mat = yaw_mat.view(yaw_mat.shape[0], 3, 3)
roll_mat = torch.cat([torch.cos(roll), -torch.sin(roll), torch.zeros_like(roll),
torch.sin(roll), torch.cos(roll), torch.zeros_like(roll),
torch.zeros_like(roll), torch.zeros_like(roll), torch.ones_like(roll)], dim=1)
roll_mat = roll_mat.view(roll_mat.shape[0], 3, 3)
rot_mat = torch.einsum('bij,bjk,bkm->bim', pitch_mat, yaw_mat, roll_mat)
return rot_mat
def keypoint_transformation(kp_canonical, he, wo_exp=False):
kp = kp_canonical['value'] # (bs, k, 3)
yaw, pitch, roll= he['yaw'], he['pitch'], he['roll']
yaw = headpose_pred_to_degree(yaw)
pitch = headpose_pred_to_degree(pitch)
roll = headpose_pred_to_degree(roll)
if 'yaw_in' in he:
yaw = he['yaw_in']
if 'pitch_in' in he:
pitch = he['pitch_in']
if 'roll_in' in he:
roll = he['roll_in']
rot_mat = get_rotation_matrix(yaw, pitch, roll) # (bs, 3, 3)
t, exp = he['t'], he['exp']
if wo_exp:
exp = exp*0
# keypoint rotation
kp_rotated = torch.einsum('bmp,bkp->bkm', rot_mat, kp)
# keypoint translation
t[:, 0] = t[:, 0]*0
t[:, 2] = t[:, 2]*0
t = t.unsqueeze(1).repeat(1, kp.shape[1], 1)
kp_t = kp_rotated + t
# add expression deviation
exp = exp.view(exp.shape[0], -1, 3)
kp_transformed = kp_t + exp
return {'value': kp_transformed}
def make_animation(source_image, source_semantics, target_semantics,
generator, kp_detector, he_estimator, mapping,
yaw_c_seq=None, pitch_c_seq=None, roll_c_seq=None,
use_exp=True, use_half=False, rank=0, p_num=1, bf16=False):
print(f"rank, p_num: {rank}, {p_num}")
with torch.inference_mode():
predictions = []
import time
import os
#with torch.cpu.amp.autocast():
start_time = time.time()
kp_canonical = kp_detector(source_image)
end_time = time.time()
print("[kp_detector]:")
print(end_time - start_time)
start_time = end_time
he_source = mapping(source_semantics)
end_time = time.time()
print("[mapping]:")
print(end_time - start_time)
start_time = end_time
kp_source = keypoint_transformation(kp_canonical, he_source)
end_time = time.time()
print(end_time - start_time)
for frame_idx in tqdm(range(target_semantics.shape[1]), 'Face Renderer:'):
if frame_idx % p_num != rank:
continue
target_semantics_frame = target_semantics[:, frame_idx]
he_driving = mapping(target_semantics_frame)
if yaw_c_seq is not None:
he_driving['yaw_in'] = yaw_c_seq[:, frame_idx]
if pitch_c_seq is not None:
he_driving['pitch_in'] = pitch_c_seq[:, frame_idx]
if roll_c_seq is not None:
he_driving['roll_in'] = roll_c_seq[:, frame_idx]
kp_driving = keypoint_transformation(kp_canonical, he_driving)
kp_norm = kp_driving
with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloat16, cache_enabled=True) if bf16 else contextlib.nullcontext():
out = generator(source_image, kp_source=kp_source, kp_driving=kp_norm)
# print(f"{rank}:{frame_idx}")
#print(out['prediction'])
predictions.append(out['prediction'].cpu().numpy())
folder_name = "logs"
file_name = f'{p_num}_{rank}.npz'
if not os.path.exists(folder_name):
os.makedirs(folder_name)
file_path = os.path.join(folder_name, file_name)
f = open(file_path, "w")
np.savez(file_path, *predictions)
f.close()
# master process will be pending here to collect all the predictions
# ... pending ...
def atoi(text):
return int(text) if text.isdigit() else text
def natural_keys(text):
return [ atoi(c) for c in re.split(r'(\d+)', text) ]
if rank == 0:
while len(os.listdir(folder_name)) < p_num:
time.sleep(0.2)
# load all the npz arrays, merge by sequence
#npz_file_paths = sorted(os.listdir(folder_name))
npz_file_paths=os.listdir(folder_name)
npz_file_paths.sort(key=natural_keys)
print("start to merge...")
print(f"npz_file_paths: {npz_file_paths}")
else:
# exit(0)
return None
aggregated_lst = []
for npz_file_path in npz_file_paths:
npz_file = np.load(os.path.join(folder_name, npz_file_path))
aggregated_lst.append([npz_file[i] for i in npz_file.files])
#aggregated_predictions = [torch.from_numpy(x) for y in zip(*aggregated_lst) for x in y]
# agg lst elements may have different length!
#exit(0)
padded_preds = [x for y in itertools.zip_longest(*aggregated_lst) for x in y]
print("padded preds length:")
print(len(padded_preds))
aggregated_predictions = [torch.from_numpy(i) for i in padded_preds if i is not None]
#predictions_ts = torch.stack(predictions, dim=1)
predictions_ts = torch.stack(aggregated_predictions, dim=1)
return predictions_ts
class AnimateModel(torch.nn.Module):
"""
Merge all generator related updates into single model for better multi-gpu usage
"""
def __init__(self, generator, kp_extractor, mapping):
super(AnimateModel, self).__init__()
self.kp_extractor = kp_extractor
self.generator = generator
self.mapping = mapping
self.kp_extractor.eval()
self.generator.eval()
self.mapping.eval()
def forward(self, x):
source_image = x['source_image']
source_semantics = x['source_semantics']
target_semantics = x['target_semantics']
yaw_c_seq = x['yaw_c_seq']
pitch_c_seq = x['pitch_c_seq']
roll_c_seq = x['roll_c_seq']
predictions_video = make_animation(source_image, source_semantics, target_semantics,
self.generator, self.kp_extractor,
self.mapping, use_exp = True,
yaw_c_seq=yaw_c_seq, pitch_c_seq=pitch_c_seq, roll_c_seq=roll_c_seq)
return predictions_video
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