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import torch
import time
import torch.optim as optim
from collections import OrderedDict
from utils.utils import print_current_loss
from os.path import join as pjoin
from diffusers import DDPMScheduler
from torch.utils.tensorboard import SummaryWriter
import time
import pdb
import sys
import os
from torch.optim.lr_scheduler import ExponentialLR
class DDPMTrainer(object):
def __init__(self, args, model, accelerator, model_ema=None):
self.opt = args
self.accelerator = accelerator
self.device = self.accelerator.device
self.model = model
self.diffusion_steps = args.diffusion_steps
self.noise_scheduler = DDPMScheduler(
num_train_timesteps=self.diffusion_steps,
beta_schedule=args.beta_schedule,
variance_type="fixed_small",
prediction_type=args.prediction_type,
clip_sample=False,
)
self.model_ema = model_ema
if args.is_train:
self.mse_criterion = torch.nn.MSELoss(reduction="none")
accelerator.print("Diffusion_config:\n", self.noise_scheduler.config)
if self.accelerator.is_main_process:
starttime = time.strftime("%Y-%m-%d_%H:%M:%S")
print("Start experiment:", starttime)
self.writer = SummaryWriter(
log_dir=pjoin(args.save_root, "logs_") + starttime[:16],
comment=starttime[:16],
flush_secs=60,
)
self.accelerator.wait_for_everyone()
self.optimizer = optim.AdamW(
self.model.parameters(), lr=self.opt.lr, weight_decay=self.opt.weight_decay
)
self.scheduler = (
ExponentialLR(self.optimizer, gamma=args.decay_rate)
if args.decay_rate > 0
else None
)
@staticmethod
def zero_grad(opt_list):
for opt in opt_list:
opt.zero_grad()
def clip_norm(self, network_list):
for network in network_list:
self.accelerator.clip_grad_norm_(
network.parameters(), self.opt.clip_grad_norm
) # 0.5 -> 1
@staticmethod
def step(opt_list):
for opt in opt_list:
opt.step()
def forward(self, batch_data):
caption, motions, m_lens = batch_data
motions = motions.detach().float()
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)
self.src_mask = self.generate_src_mask(T, cur_len).to(x_start.device)
# 1. Sample noise that we'll add to the motion
real_noise = torch.randn_like(x_start)
# 2. Sample a random timestep for each motion
t = torch.randint(0, self.diffusion_steps, (B,), device=self.device)
self.timesteps = t
# 3. Add noise to the motion according to the noise magnitude at each timestep
# (this is the forward diffusion process)
x_t = self.noise_scheduler.add_noise(x_start, real_noise, t)
# 4. network prediction
self.prediction = self.model(x_t, t, text=caption)
if self.opt.prediction_type == "sample":
self.target = x_start
elif self.opt.prediction_type == "epsilon":
self.target = real_noise
elif self.opt.prediction_type == "v_prediction":
self.target = self.noise_scheduler.get_velocity(x_start, real_noise, t)
def masked_l2(self, a, b, mask, weights):
loss = self.mse_criterion(a, b).mean(dim=-1) # (bath_size, motion_length)
loss = (loss * mask).sum(-1) / mask.sum(-1) # (batch_size, )
loss = (loss * weights).mean()
return loss
def backward_G(self):
loss_logs = OrderedDict({})
mse_loss_weights = torch.ones_like(self.timesteps)
loss_logs["loss_mot_rec"] = self.masked_l2(
self.prediction, self.target, self.src_mask, mse_loss_weights
)
self.loss = loss_logs["loss_mot_rec"]
return loss_logs
def update(self):
self.zero_grad([self.optimizer])
loss_logs = self.backward_G()
self.accelerator.backward(self.loss)
self.clip_norm([self.model])
self.step([self.optimizer])
return loss_logs
def generate_src_mask(self, T, length):
B = len(length)
src_mask = torch.ones(B, T)
for i in range(B):
for j in range(length[i], T):
src_mask[i, j] = 0
return src_mask
def train_mode(self):
self.model.train()
if self.model_ema:
self.model_ema.train()
def eval_mode(self):
self.model.eval()
if self.model_ema:
self.model_ema.eval()
def save(self, file_name, total_it):
state = {
"opt_encoder": self.optimizer.state_dict(),
"total_it": total_it,
"encoder": self.accelerator.unwrap_model(self.model).state_dict(),
}
if self.model_ema:
state["model_ema"] = self.accelerator.unwrap_model(
self.model_ema
).module.state_dict()
torch.save(state, file_name)
return
def load(self, model_dir):
checkpoint = torch.load(model_dir, map_location=self.device)
self.optimizer.load_state_dict(checkpoint["opt_encoder"])
if self.model_ema:
self.model_ema.load_state_dict(checkpoint["model_ema"], strict=True)
self.model.load_state_dict(checkpoint["encoder"], strict=True)
return checkpoint.get("total_it", 0)
def train(self, train_loader):
it = 0
if self.opt.is_continue:
model_path = pjoin(self.opt.model_dir, self.opt.continue_ckpt)
it = self.load(model_path)
self.accelerator.print(f"continue train from {it} iters in {model_path}")
start_time = time.time()
logs = OrderedDict()
self.dataset = train_loader.dataset
self.model, self.mse_criterion, self.optimizer, train_loader, self.model_ema = (
self.accelerator.prepare(
self.model,
self.mse_criterion,
self.optimizer,
train_loader,
self.model_ema,
)
)
num_epochs = (self.opt.num_train_steps - it) // len(train_loader) + 1
self.accelerator.print(f"need to train for {num_epochs} epochs....")
for epoch in range(0, num_epochs):
self.train_mode()
for i, batch_data in enumerate(train_loader):
self.forward(batch_data)
log_dict = self.update()
it += 1
if self.model_ema and it % self.opt.model_ema_steps == 0:
self.accelerator.unwrap_model(self.model_ema).update_parameters(
self.model
)
# update logger
for k, v in log_dict.items():
if k not in logs:
logs[k] = v
else:
logs[k] += v
if it % self.opt.log_every == 0:
mean_loss = OrderedDict({})
for tag, value in logs.items():
mean_loss[tag] = value / self.opt.log_every
logs = OrderedDict()
print_current_loss(
self.accelerator, start_time, it, mean_loss, epoch, inner_iter=i
)
if self.accelerator.is_main_process:
self.writer.add_scalar("loss", mean_loss["loss_mot_rec"], it)
self.accelerator.wait_for_everyone()
if (
it % self.opt.save_interval == 0
and self.accelerator.is_main_process
): # Save model
self.save(pjoin(self.opt.model_dir, "latest.tar").format(it), it)
self.accelerator.wait_for_everyone()
if (self.scheduler is not None) and (
it % self.opt.update_lr_steps == 0
):
self.scheduler.step()
# Save the last checkpoint if it wasn't already saved.
if it % self.opt.save_interval != 0 and self.accelerator.is_main_process:
self.save(pjoin(self.opt.model_dir, "latest.tar"), it)
self.accelerator.wait_for_everyone()
self.accelerator.print("FINISH")
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