def _generator_training_step(self, generator, model, loader, epoch, tau, info_for_logger=""):
start_time = time.time()
generator.train()
model.eval()
for step, (X, y) in enumerate(loader):
arch_param, hardware_constraint = self.set_arch_param(generator, model, tau=tau)
macs = self.lookup_table.get_model_macs(arch_param)
logging.info("Generate model macs : {}".format(macs))
hc_loss = cal_hc_loss(macs.cuda(), hardware_constraint.item(), self.alpha, self.loss_penalty)
X, y = X.cuda(non_blocking=True), y.cuda(non_blocking=True)
N = X.shape[0]
self.g_optimizer.zero_grad()
outs = model(X, True)
ce_loss = self.criterion(outs, y)
loss = ce_loss + hc_loss
logging.info("HC loss : {}".format(hc_loss))
loss.backward()
self.g_optimizer.step()
self.g_optimizer.zero_grad()
self._intermediate_stats_logging(outs, y, loss, step, epoch, N, len_loader=len(loader), val_or_train="Train", hc_losses=hc_loss)
self._epoch_stats_logging(start_time=start_time, epoch=epoch, val_or_train="train")
for avg in [self.top1, self.top5, self.losses, self.hc_losses]:
avg.reset()
def evaluate_generator(generator,
prior_pool,
lookup_table,
CONFIG,
device,
val=True):
"""
Evaluate kendetall and hardware constraint loss of generator
"""
total_loss = 0
evaluate_metric = {"gen_flops": [], "true_flops": []}
for mac in range(CONFIG.low_flops, CONFIG.high_flops, 10):
hardware_constraint = torch.tensor(mac, dtype=torch.float32)
hardware_constraint = hardware_constraint.view(-1, 1)
hardware_constraint = hardware_constraint.to(device)
prior = prior_pool.get_prior(hardware_constraint.item())
prior = prior.to(device)
normalize_hardware_constraint = min_max_normalize(
CONFIG.high_flops, CONFIG.low_flops, hardware_constraint)
arch_param = generator(prior, normalize_hardware_constraint)
arch_param = lookup_table.get_validation_arch_param(arch_param)
layers_config = lookup_table.decode_arch_param(arch_param)
gen_mac = lookup_table.get_model_flops(arch_param)
hc_loss = cal_hc_loss(gen_mac.cuda(), hardware_constraint.item(),
CONFIG.alpha, CONFIG.loss_penalty)
evaluate_metric["gen_flops"].append(gen_mac.item())
evaluate_metric["true_flops"].append(mac)
total_loss += hc_loss.item()
tau, _ = stats.kendalltau(evaluate_metric["gen_flops"],
evaluate_metric["true_flops"])
return evaluate_metric, total_loss, tau
def generator_validate(self, generator, model, loader, epoch, tau, hardware_constraint=360, arch_param=None, sample=False, info_for_logger=""):
if generator is not None:
generator.eval()
model.eval()
start_time = time.time()
if sample:
hardware_constraint, arch_param = self._get_arch_param(generator, hardware_constraint, valid=True)
arch_param = self.lookup_table.get_validation_arch_param(arch_param)
arch_param, hardware_constraint = self.set_arch_param(generator, model, hardware_constraint=hardware_constraint, arch_param=arch_param)
else:
hardware_constraint = torch.tensor(hardware_constraint)
hardware_constraint.cuda()
macs = self.lookup_table.get_model_macs(arch_param)
logging.info("Generate model macs : {}".format(macs))
hc_loss = cal_hc_loss(macs.cuda(), hardware_constraint.item(), self.alpha, self.loss_penalty)
with torch.no_grad():
for step, (X, y) in enumerate(loader):
X, y = X.cuda(non_blocking=True), y.cuda(non_blocking=True)
N = X.shape[0]
outs = model(X, True)
loss = self.criterion(outs, y)
self._intermediate_stats_logging(outs, y, loss, step, epoch, N, len_loader=len(loader), val_or_train="Valid", hc_losses=hc_loss)
top1_avg = self.top1.get_avg()
self._epoch_stats_logging(start_time=start_time, epoch=epoch, val_or_train="val")
self.writer.add_scalar("train_vs_val/"+"val"+"_hc_", macs, epoch)
for avg in [self.top1, self.top5, self.losses, self.hc_losses]:
avg.reset()
return top1_avg, macs.item()
def search_train_loop(self, generator):
self.epochs = self.warmup_epochs + self.search_epochs
# Training generator
best_loss = 10000.0
best_top1 = 0
tau = 5
for epoch in range(self.warmup_epochs, self.search_epochs):
logging.info("Start to train for search epoch {}".format(epoch))
logging.info("Tau: {}".format(tau))
self._generator_training_step(generator,
val_loader,
epoch,
tau,
info_for_logger="_gen_train_step")
# ================ Train ============================================
for i in range():
# Training generator
arch_param, hardware_constraint = self.set_arch_param(
generator, tau=tau)
# ============== evaluation flops ===============================
gen_flops = self.flops_table.predict_arch_param_efficiency(
arch_param)
hc_loss = cal_hc_loss(gen_flops.cuda(),
hardware_constraint.item(),
self.CONFIG.alpha,
self.CONFIG.loss_penalty)
# ===============================================================
self.g_optimizer.zero_grad()
# ============== predict top1 accuracy ==========================
top1_avg = self.accuracy_predictor(arch_param)
ce_loss = -1 * top1_avg
# ===============================================================
loss = ce_loss + hc_loss
logging.info("HC loss : {}".format(hc_loss))
loss.backward()
self.g_optimizer.step()
self.g_optimizer.zero_grad()
# ====================================================================
# ============== Valid ===============================================
hardware_constraint, arch_param = self._get_arch_param(
generator, hardware_constraint, valid=True)
arch_param = self.calculate_one_hot(arch_param)
arch_param, hardware_constraint = self.set_arch_param(
generator,
model,
hardware_constraint=hardware_constraint,
arch_param=arch_param)
# ============== evaluation flops ===============================
gen_flops = self.flops_table.predict_arch_param_efficiency(
arch_param)
hc_loss = cal_hc_loss(gen_flops.cuda(), hardware_constraint.item(),
self.CONFIG.alpha, self.CONFIG.loss_penalty)
# ===============================================================
# ============== predict top1 accuracy ==========================
top1_avg = self.accuracy_predictor(arch_param)
logger.info("Valid : Top-1 avg : {}".format(top1_avg))
# ===============================================================
# ====================================================================
# ============== Evaluate ============================================
total_loss = 0
evaluate_metric = {"gen_flops": [], "true_flops": []}
for flops in range(self.CONFIG.low_macs, self.CONFIG.high_macs,
10):
hardware_constraint = torch.tensor(flops, dtpye=torch.float32)
hardware_constraint = hardware_constraint.view(-1, 1)
hardware_constraint = hardware_constraint.to(self.device)
normalize_hardware_constraint = min_max_normalize(
self.CONFIG.high_macs, self.CONFIG.low_macs,
hardware_constraint)
noise = torch.randn(*self.backbone.shape)
noise = noise.to(device)
noise *= 0
arch_param = generator(self.backbone,
normalize_hardware_constraint, noise)
# ============== evaluation flops ===============================
gen_flops = self.flops_table.predict_arch_param_efficiency(
arch_param)
hc_loss = cal_hc_loss(gen_flops.cuda(),
hardware_constraint.item(),
self.CONFIG.alpha,
self.CONFIG.loss_penalty)
# ===============================================================
evaluate_metric["gen_flops"].append(gen_flops)
evaluate_metric["true_flops"].append(flops)
total_loss += hc_loss.item()
kendall_tau, _ = stats.kendalltau(evaluate_metric["gen_flops"],
evaluate_metric["true_flops"])
# ====================================================================
logging.info("Total loss : {}".format(total_loss))
if best_loss > total_loss:
logging.info("Best loss by now: {} Tau : {}.Save model".format(
total_loss, kendall_tau))
best_loss = total_loss
save_generator_evaluate_metric(
evaluate_metric, self.CONFIG.path_to_generator_eval)
save(generator, self.g_optimizer,
self.CONFIG.path_to_save_generator)
if top1_avg > best_top1 and total_loss < 0.4:
logging.info(
"Best top1-avg by now: {}.Save model".format(top1_avg))
best_top1 = top1_avg
save(generator, self.g_optimizer,
self.CONFIG.path_to_best_avg_generator)
save(generator, self.g_optimizer,
"./logs/generator/{}.pth".format(total_loss))
tau *= self.CONFIG.tau_decay
self.noise_weight = self.noise_weight * self.CONFIG.noise_decay if self.noise_weight > 0.0001 else 0
logging.info("Noise weight : {}".format(self.noise_weight))
logging.info("Best loss: {}".format(best_loss))
save(generator, self.g_optimizer, self.CONFIG.path_to_fianl_generator)
def generator_validate(self,
generator,
model,
loader,
epoch,
target_hardware_constraint=None,
arch_param=None,
info_for_logger=""):
if generator is not None:
generator.eval()
model.eval()
start_time = time.time()
if arch_param is None:
if target_hardware_constraint is None:
target_hardware_constraint = self._get_target_hardware_constraint(
)
arch_param = self._get_arch_param(generator,
target_hardware_constraint)
arch_param = self.set_arch_param(
model, arch_param) # Validate architecture parameter
else:
target_hardware_constraint = self._get_target_hardware_constraint(
target_hardware_constraint)
arch_param = self._get_arch_param(generator,
target_hardware_constraint)
arch_param = self.set_arch_param(
model, arch_param) # Validate architecture parameter
else:
arch_param = self.set_arch_param(model, arch_param)
flops = self.lookup_table.get_model_flops(arch_param)
logging.info("Generate model flops : {}".format(flops))
hc_loss = cal_hc_loss(flops.cuda(), target_hardware_constraint.item(),
self.CONFIG.alpha, self.CONFIG.loss_penalty)
with torch.no_grad():
for step, (X, y) in enumerate(loader):
X, y = X.to(self.device,
non_blocking=True), y.to(self.device,
non_blocking=True)
N = X.shape[0]
outs = model(X, True)
loss = self.criterion(outs, y)
self._intermediate_stats_logging(outs,
y,
loss,
step,
epoch,
N,
len_loader=len(loader),
val_or_train="Valid",
hc_losses=hc_loss)
top1_avg = self.top1.get_avg()
self._epoch_stats_logging(start_time=start_time,
epoch=epoch,
val_or_train="val")
self.writer.add_scalar("train_vs_val/" + "val" + "_hc_", flops, epoch)
for avg in [self.top1, self.top5, self.losses, self.hc_losses]:
avg.reset()
return top1_avg, flops.item()
