Spaces:
Sleeping
Sleeping
File size: 8,924 Bytes
1b1b150 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 |
import os
import sys
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.optim.lr_scheduler import StepLR
sys.path.append(os.getcwd())
from nets.layers import *
from nets.base import TrainWrapperBaseClass
from nets.spg.gated_pixelcnn_v2 import GatedPixelCNN as pixelcnn
from nets.spg.vqvae_1d import VQVAE as s2g_body, Wav2VecEncoder, AudioEncoder
from nets.utils import parse_audio, denormalize
from data_utils import get_mfcc, get_melspec, get_mfcc_old, get_mfcc_psf, get_mfcc_psf_min, get_mfcc_ta
from data_utils.lower_body import c_index, c_index_3d, c_index_6d
from data_utils.utils import smooth_geom, get_mfcc_sepa
import numpy as np
from sklearn.preprocessing import normalize
class TrainWrapper(TrainWrapperBaseClass):
'''
a wrapper receiving a batch from data_utils and calculate loss
'''
def __init__(self, args, config):
self.args = args
self.config = config
self.global_step = 0
# Force CPU device
self.device = torch.device('cpu')
self.convert_to_6d = self.config.Data.pose.convert_to_6d
self.expression = self.config.Data.pose.expression
self.epoch = 0
self.init_params()
self.num_classes = 4
self.audio = True
self.composition = self.config.Model.composition
self.bh_model = self.config.Model.bh_model
if self.audio:
self.audioencoder = AudioEncoder(
in_dim=64,
num_hiddens=256,
num_residual_layers=2,
num_residual_hiddens=256
).to(self.device)
else:
self.audioencoder = None
if self.convert_to_6d:
dim, layer = 512, 10
else:
dim, layer = 256, 15
self.generator = pixelcnn(2048, dim, layer, self.num_classes, self.audio, self.bh_model).to(self.device)
self.g_body = s2g_body(self.each_dim[1], embedding_dim=64, num_embeddings=config.Model.code_num, num_hiddens=1024,
num_residual_layers=2, num_residual_hiddens=512).to(self.device)
self.g_hand = s2g_body(self.each_dim[2], embedding_dim=64, num_embeddings=config.Model.code_num, num_hiddens=1024,
num_residual_layers=2, num_residual_hiddens=512).to(self.device)
model_path = self.config.Model.vq_path
model_ckpt = torch.load(model_path, map_location=torch.device('cpu'))
self.g_body.load_state_dict(model_ckpt['generator']['g_body'])
self.g_hand.load_state_dict(model_ckpt['generator']['g_hand'])
self.discriminator = None
if self.convert_to_6d:
self.c_index = c_index_6d
else:
self.c_index = c_index_3d
super().__init__(args, config)
def init_optimizer(self):
print('using Adam')
self.generator_optimizer = optim.Adam(
self.generator.parameters(),
lr=self.config.Train.learning_rate.generator_learning_rate,
betas=[0.9, 0.999]
)
if self.audioencoder is not None:
opt = self.config.Model.AudioOpt
if opt == 'Adam':
self.audioencoder_optimizer = optim.Adam(
self.audioencoder.parameters(),
lr=self.config.Train.learning_rate.generator_learning_rate,
betas=[0.9, 0.999]
)
else:
print('using SGD')
self.audioencoder_optimizer = optim.SGD(
filter(lambda p: p.requires_grad, self.audioencoder.parameters()),
lr=self.config.Train.learning_rate.generator_learning_rate * 10,
momentum=0.9,
nesterov=False
)
def state_dict(self):
return {
'generator': self.generator.state_dict(),
'generator_optim': self.generator_optimizer.state_dict(),
'audioencoder': self.audioencoder.state_dict() if self.audio else None,
'audioencoder_optim': self.audioencoder_optimizer.state_dict() if self.audio else None,
'discriminator': self.discriminator.state_dict() if self.discriminator else None,
'discriminator_optim': self.discriminator_optimizer.state_dict() if self.discriminator else None
}
def load_state_dict(self, state_dict):
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
sub_dict = OrderedDict()
if v is not None:
for k1, v1 in v.items():
name = k1.replace('module.', '')
sub_dict[name] = v1
new_state_dict[k] = sub_dict
state_dict = new_state_dict
if 'generator' in state_dict:
self.generator.load_state_dict(state_dict['generator'])
else:
self.generator.load_state_dict(state_dict)
if 'generator_optim' in state_dict and self.generator_optimizer is not None:
self.generator_optimizer.load_state_dict(state_dict['generator_optim'])
if self.discriminator is not None:
self.discriminator.load_state_dict(state_dict['discriminator'])
if 'discriminator_optim' in state_dict and self.discriminator_optimizer is not None:
self.discriminator_optimizer.load_state_dict(state_dict['discriminator_optim'])
if 'audioencoder' in state_dict and self.audioencoder is not None:
self.audioencoder.load_state_dict(state_dict['audioencoder'])
def init_params(self):
if self.config.Data.pose.convert_to_6d:
scale = 2
else:
scale = 1
global_orient = round(0 * scale)
leye_pose = reye_pose = round(0 * scale)
jaw_pose = round(0 * scale)
body_pose = round((63 - 24) * scale)
left_hand_pose = right_hand_pose = round(45 * scale)
if self.expression:
expression = 100
else:
expression = 0
b_j = 0
jaw_dim = jaw_pose
b_e = b_j + jaw_dim
eye_dim = leye_pose + reye_pose
b_b = b_e + eye_dim
body_dim = global_orient + body_pose
b_h = b_b + body_dim
hand_dim = left_hand_pose + right_hand_pose
b_f = b_h + hand_dim
face_dim = expression
self.dim_list = [b_j, b_e, b_b, b_h, b_f]
self.full_dim = jaw_dim + eye_dim + body_dim + hand_dim
self.pose = int(self.full_dim / round(3 * scale))
self.each_dim = [jaw_dim, eye_dim + body_dim, hand_dim, face_dim]
def __call__(self, bat):
self.global_step += 1
total_loss = None
loss_dict = {}
aud, poses = bat['aud_feat'].to(self.device).float(), bat['poses'].to(self.device).float()
id = bat['speaker'].to(self.device) - 20
poses = poses[:, self.c_index, :]
aud = aud.permute(0, 2, 1)
gt_poses = poses.permute(0, 2, 1)
with torch.no_grad():
self.g_body.eval()
self.g_hand.eval()
_, body_latents = self.g_body.encode(gt_poses=gt_poses[..., :self.each_dim[1]], id=id)
_, hand_latents = self.g_hand.encode(gt_poses=gt_poses[..., self.each_dim[1]:], id=id)
latents = torch.cat([body_latents.unsqueeze(-1), hand_latents.unsqueeze(-1)], dim=-1).detach()
if self.audio:
audio = self.audioencoder(aud.transpose(1, 2), frame_num=latents.shape[1]*4).unsqueeze(-1).repeat(1, 1, 1, 2)
logits = self.generator(latents, id, audio)
else:
logits = self.generator(latents, id)
logits = logits.permute(0, 2, 3, 1).contiguous()
self.generator_optimizer.zero_grad()
if self.audio:
self.audioencoder_optimizer.zero_grad()
loss = F.cross_entropy(logits.view(-1, logits.shape[-1]), latents.view(-1))
loss.backward()
grad = torch.nn.utils.clip_grad_norm(self.generator.parameters(), self.config.Train.max_gradient_norm)
loss_dict['grad'] = grad.item()
loss_dict['ce_loss'] = loss.item()
self.generator_optimizer.step()
if self.audio:
self.audioencoder_optimizer.step()
return total_loss, loss_dict
# ----------------------------------------
# 🚀 NEW SIMPLE WRAPPER CLASS for inference
# ----------------------------------------
class s2g_body_pixel(nn.Module):
def __init__(self, args, config):
super().__init__()
self.wrapper = TrainWrapper(args, config)
def infer_on_audio(self, *args, **kwargs):
return self.wrapper.infer_on_audio(*args, **kwargs)
def forward(self, *args, **kwargs):
return self.wrapper(*args, **kwargs)
def load_state_dict(self, *args, **kwargs):
return self.wrapper.load_state_dict(*args, **kwargs)
|