Spaces:
Runtime error
Runtime error
File size: 7,489 Bytes
12deb01 |
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 |
import torch
import torch.nn.functional as F
import random
import time
from models.transformer import MotionTransformer
from torch.utils.data import DataLoader
import torch.optim as optim
from torch.nn.utils import clip_grad_norm_
from collections import OrderedDict
from utils.utils import print_current_loss
from os.path import join as pjoin
import codecs as cs
import torch.distributed as dist
from mmcv.runner import get_dist_info
from models.gaussian_diffusion import (
GaussianDiffusion,
get_named_beta_schedule,
create_named_schedule_sampler,
ModelMeanType,
ModelVarType,
LossType
)
from datasets import build_dataloader
class DDPMTrainer(object):
def __init__(self, args, encoder):
self.opt = args
self.device = args.device
self.encoder = encoder
self.diffusion_steps = args.diffusion_steps
sampler = 'uniform'
beta_scheduler = 'linear'
betas = get_named_beta_schedule(beta_scheduler, self.diffusion_steps)
self.diffusion = GaussianDiffusion(
betas=betas,
model_mean_type=ModelMeanType.EPSILON,
model_var_type=ModelVarType.FIXED_SMALL,
loss_type=LossType.MSE
)
self.sampler = create_named_schedule_sampler(sampler, self.diffusion)
self.sampler_name = sampler
if args.is_train:
self.mse_criterion = torch.nn.MSELoss(reduction='none')
self.to(self.device)
@staticmethod
def zero_grad(opt_list):
for opt in opt_list:
opt.zero_grad()
@staticmethod
def clip_norm(network_list):
for network in network_list:
clip_grad_norm_(network.parameters(), 0.5)
@staticmethod
def step(opt_list):
for opt in opt_list:
opt.step()
def forward(self, batch_data, eval_mode=False):
caption, motions, m_lens = batch_data
motions = motions.detach().to(self.device).float()
self.caption = caption
self.motions = motions
x_start = motions
B, T = x_start.shape[:2]
cur_len = torch.LongTensor([min(T, m_len) for m_len in m_lens]).to(self.device)
t, _ = self.sampler.sample(B, x_start.device)
output = self.diffusion.training_losses(
model=self.encoder,
x_start=x_start,
t=t,
model_kwargs={"text": caption, "length": cur_len}
)
self.real_noise = output['target']
self.fake_noise = output['pred']
try:
self.src_mask = self.encoder.module.generate_src_mask(T, cur_len).to(x_start.device)
except:
self.src_mask = self.encoder.generate_src_mask(T, cur_len).to(x_start.device)
def generate_batch(self, caption, m_lens, dim_pose):
xf_proj, xf_out = self.encoder.encode_text(caption, self.device)
B = len(caption)
T = min(m_lens.max(), self.encoder.num_frames)
output = self.diffusion.p_sample_loop(
self.encoder,
(B, T, dim_pose),
clip_denoised=False,
progress=True,
model_kwargs={
'xf_proj': xf_proj,
'xf_out': xf_out,
'length': m_lens
})
return output
def generate(self, caption, m_lens, dim_pose, batch_size=1024):
N = len(caption)
cur_idx = 0
self.encoder.eval()
all_output = []
while cur_idx < N:
if cur_idx + batch_size >= N:
batch_caption = caption[cur_idx:]
batch_m_lens = m_lens[cur_idx:]
else:
batch_caption = caption[cur_idx: cur_idx + batch_size]
batch_m_lens = m_lens[cur_idx: cur_idx + batch_size]
output = self.generate_batch(batch_caption, batch_m_lens, dim_pose)
B = output.shape[0]
for i in range(B):
all_output.append(output[i])
cur_idx += batch_size
return all_output
def backward_G(self):
loss_mot_rec = self.mse_criterion(self.fake_noise, self.real_noise).mean(dim=-1)
loss_mot_rec = (loss_mot_rec * self.src_mask).sum() / self.src_mask.sum()
self.loss_mot_rec = loss_mot_rec
loss_logs = OrderedDict({})
loss_logs['loss_mot_rec'] = self.loss_mot_rec.item()
return loss_logs
def update(self):
self.zero_grad([self.opt_encoder])
loss_logs = self.backward_G()
self.loss_mot_rec.backward()
self.clip_norm([self.encoder])
self.step([self.opt_encoder])
return loss_logs
def to(self, device):
if self.opt.is_train:
self.mse_criterion.to(device)
self.encoder = self.encoder.to(device)
def train_mode(self):
self.encoder.train()
def eval_mode(self):
self.encoder.eval()
def save(self, file_name, ep, total_it):
state = {
'opt_encoder': self.opt_encoder.state_dict(),
'ep': ep,
'total_it': total_it
}
try:
state['encoder'] = self.encoder.module.state_dict()
except:
state['encoder'] = self.encoder.state_dict()
torch.save(state, file_name)
return
def load(self, model_dir):
checkpoint = torch.load(model_dir, map_location=self.device)
if self.opt.is_train:
self.opt_encoder.load_state_dict(checkpoint['opt_encoder'])
self.encoder.load_state_dict(checkpoint['encoder'], strict=True)
return checkpoint['ep'], checkpoint.get('total_it', 0)
def train(self, train_dataset):
rank, world_size = get_dist_info()
self.to(self.device)
self.opt_encoder = optim.Adam(self.encoder.parameters(), lr=self.opt.lr)
it = 0
cur_epoch = 0
if self.opt.is_continue:
model_dir = pjoin(self.opt.model_dir, 'latest.tar')
cur_epoch, it = self.load(model_dir)
start_time = time.time()
train_loader = build_dataloader(
train_dataset,
samples_per_gpu=self.opt.batch_size,
drop_last=True,
workers_per_gpu=4,
shuffle=True)
logs = OrderedDict()
for epoch in range(cur_epoch, self.opt.num_epochs):
self.train_mode()
for i, batch_data in enumerate(train_loader):
self.forward(batch_data)
log_dict = self.update()
for k, v in log_dict.items():
if k not in logs:
logs[k] = v
else:
logs[k] += v
it += 1
if it % self.opt.log_every == 0 and rank == 0:
mean_loss = OrderedDict({})
for tag, value in logs.items():
mean_loss[tag] = value / self.opt.log_every
logs = OrderedDict()
print_current_loss(start_time, it, mean_loss, epoch, inner_iter=i)
if it % self.opt.save_latest == 0 and rank == 0:
self.save(pjoin(self.opt.model_dir, 'latest.tar'), epoch, it)
if rank == 0:
self.save(pjoin(self.opt.model_dir, 'latest.tar'), epoch, it)
if epoch % self.opt.save_every_e == 0 and rank == 0:
self.save(pjoin(self.opt.model_dir, 'ckpt_e%03d.tar'%(epoch)),
epoch, total_it=it)
|