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engine_for_vqvae.py
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engine_for_vqvae.py
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import math
import sys
from typing import Iterable, Optional
import torch
import torch.nn.functional as F
from timm.utils import ModelEma
import utils
def train_batch(model, surface, points, labels, criterion):
outputs, z_e_x, z_q_x, sigma, loss_commit, perplexity = model(surface, points)
loss_vol = criterion(outputs[:, :1024], labels[:, :1024])
loss_near = criterion(outputs[:, 1024:], labels[:, 1024:])
loss_sigma = sigma.mean()
loss = loss_vol + 0.1 * loss_near + loss_commit + 0.0001 * loss_sigma
return loss, outputs, loss_vol.item(), loss_near.item(), loss_commit.item(), loss_sigma.item()
def train_one_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
model_ema: Optional[ModelEma] = None, log_writer=None,
start_steps=None, lr_schedule_values=None, wd_schedule_values=None,
num_training_steps_per_epoch=None, update_freq=None):
model.train(True)
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
metric_logger.add_meter('min_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 10
if loss_scaler is None:
model.zero_grad()
model.micro_steps = 0
else:
optimizer.zero_grad()
for data_iter_step, (points, labels, surface, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
step = data_iter_step // update_freq
if step >= num_training_steps_per_epoch:
continue
it = start_steps + step # global training iteration
# Update LR & WD for the first acc
if lr_schedule_values is not None or wd_schedule_values is not None and data_iter_step % update_freq == 0:
for i, param_group in enumerate(optimizer.param_groups):
if lr_schedule_values is not None:
param_group["lr"] = lr_schedule_values[it] * param_group["lr_scale"]
if wd_schedule_values is not None and param_group["weight_decay"] > 0:
param_group["weight_decay"] = wd_schedule_values[it]
surface = surface.to(device, non_blocking=True)
points = points.to(device, non_blocking=True)
labels = labels.to(device, non_blocking=True)
if loss_scaler is None:
raise NotImplementedError
else:
with torch.cuda.amp.autocast(enabled=True):
loss, output, loss_vol, loss_near, loss_commit, loss_sigma = train_batch(model, surface, points, labels, criterion)
loss_value = loss.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
sys.exit(1)
if loss_scaler is None:
raise NotImplementedError
else:
# this attribute is added by timm on one optimizer (adahessian)
is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
loss /= update_freq
grad_norm = loss_scaler(loss, optimizer, clip_grad=max_norm,
parameters=model.parameters(), create_graph=is_second_order,
update_grad=(data_iter_step + 1) % update_freq == 0)
if (data_iter_step + 1) % update_freq == 0:
optimizer.zero_grad()
if model_ema is not None:
model_ema.update(model)
loss_scale_value = loss_scaler.state_dict()["scale"]
torch.cuda.synchronize()
pred = torch.zeros_like(output[:, :1024])
pred[output[:, :1024]>=0] = 1
accuracy = (pred==labels[:, :1024]).float().sum(dim=1) / labels[:, :1024].shape[1]
accuracy = accuracy.mean()
intersection = (pred * labels[:, :1024]).sum(dim=1)
union = (pred + labels[:, :1024]).gt(0).sum(dim=1) + 1e-5
iou = intersection * 1.0 / union
iou = iou.mean()
metric_logger.update(loss=loss_value)
metric_logger.update(iou=iou)
metric_logger.update(loss_scale=loss_scale_value)
metric_logger.update(loss_vol=loss_vol)
metric_logger.update(loss_near=loss_near)
metric_logger.update(loss_commit=loss_commit)
metric_logger.update(loss_sigma=loss_sigma)
min_lr = 10.
max_lr = 0.
for group in optimizer.param_groups:
min_lr = min(min_lr, group["lr"])
max_lr = max(max_lr, group["lr"])
metric_logger.update(lr=max_lr)
metric_logger.update(min_lr=min_lr)
weight_decay_value = None
for group in optimizer.param_groups:
if group["weight_decay"] > 0:
weight_decay_value = group["weight_decay"]
metric_logger.update(weight_decay=weight_decay_value)
metric_logger.update(grad_norm=grad_norm)
if log_writer is not None:
log_writer.update(loss=loss_value, head="loss")
log_writer.update(loss_scale=loss_scale_value, head="opt")
log_writer.update(lr=max_lr, head="opt")
log_writer.update(min_lr=min_lr, head="opt")
log_writer.update(weight_decay=weight_decay_value, head="opt")
log_writer.update(grad_norm=grad_norm, head="opt")
log_writer.update(loss_vol=loss_vol, head="opt")
log_writer.update(loss_near=loss_near, head="opt")
log_writer.set_step()
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluate(data_loader, model, device):
criterion = torch.nn.BCEWithLogitsLoss()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
model.eval()
for batch in metric_logger.log_every(data_loader, 200, header):
points, labels, surface, _ = batch
surface = surface.to(device, non_blocking=True)
points = points.to(device, non_blocking=True)
labels = labels.to(device, non_blocking=True)
with torch.cuda.amp.autocast():
N = 100000
output, _, _, _, _, perplexity = model(surface, points)
loss = criterion(output, labels)
pred = torch.zeros_like(output)
pred[output>=0] = 1
accuracy = (pred==labels).float().sum(dim=1) / labels.shape[1]
accuracy = accuracy.mean()
intersection = (pred * labels).sum(dim=1)
union = (pred + labels).gt(0).sum(dim=1)
iou = intersection * 1.0 / union + 1e-5
iou = iou.mean()
batch_size = surface.shape[0]
metric_logger.update(loss=loss.item())
metric_logger.meters['iou'].update(iou.item(), n=batch_size)
metric_logger.synchronize_between_processes()
print('* iou{iou.global_avg:.4f} loss {losses.global_avg:.3f}'
.format(iou=metric_logger.iou, losses=metric_logger.loss))
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}