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# --------------------------------------------------------
# References:
# MAE: https://github.com/facebookresearch/mae
# DeiT: https://github.com/facebookresearch/deit
# BEiT: https://github.com/microsoft/unilm/tree/master/beit
# --------------------------------------------------------
import math
import sys
sys.path.append("..")
from typing import Iterable
import torch
import torch.nn.functional as F
import util.misc as misc
import util.lr_sched as lr_sched
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,
log_writer=None, args=None):
model.train(True)
metric_logger = misc.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 20
accum_iter = args.accum_iter
optimizer.zero_grad()
kl_weight = 25e-3 #TODO: try to modify this, it is 1e-3 originally, large kl ease the training of diffusion, but decrease in VAE results
if log_writer is not None:
print('log_dir: {}'.format(log_writer.log_dir))
for data_iter_step, data_batch in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
# we use a per iteration (instead of per epoch) lr scheduler
if data_iter_step % accum_iter == 0:
lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)
points = data_batch['points'].to(device, non_blocking=True)
labels = data_batch['labels'].to(device, non_blocking=True)
surface = data_batch['surface'].to(device, non_blocking=True)
# print(points.shape)
with torch.cuda.amp.autocast(enabled=False):
outputs = model(surface, points)
if 'kl' in outputs:
loss_kl = outputs['kl']
#print(loss_kl.shape)
loss_kl = torch.sum(loss_kl) / loss_kl.shape[0]
else:
loss_kl = None
outputs = outputs['logits']
num_samples=outputs.shape[1]//2
#print(num_samples)
loss_vol = criterion(outputs[:, :num_samples], labels[:, :num_samples])
loss_near = criterion(outputs[:, num_samples:], labels[:, num_samples:])
if loss_kl is not None:
loss = loss_vol + 0.1 * loss_near + kl_weight * loss_kl
else:
loss = loss_vol + 0.1 * loss_near
loss_value = loss.item()
threshold = 0
pred = torch.zeros_like(outputs[:, :num_samples])
pred[outputs[:, :num_samples] >= threshold] = 1
accuracy = (pred == labels[:, :num_samples]).float().sum(dim=1) / labels[:, :num_samples].shape[1]
accuracy = accuracy.mean()
intersection = (pred * labels[:, :num_samples]).sum(dim=1)
union = (pred + labels[:, :num_samples]).gt(0).sum(dim=1) + 1e-5
iou = intersection * 1.0 / union
iou = iou.mean()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
sys.exit(1)
loss /= accum_iter
loss_scaler(loss, optimizer, clip_grad=max_norm,
parameters=model.parameters(), create_graph=False,
update_grad=(data_iter_step + 1) % accum_iter == 0)
if (data_iter_step + 1) % accum_iter == 0:
optimizer.zero_grad()
torch.cuda.synchronize()
metric_logger.update(loss=loss_value)
metric_logger.update(loss_vol=loss_vol.item())
metric_logger.update(loss_near=loss_near.item())
if loss_kl is not None:
metric_logger.update(loss_kl=loss_kl.item())
metric_logger.update(iou=iou.item())
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)
loss_value_reduce = misc.all_reduce_mean(loss_value)
iou_reduce=misc.all_reduce_mean(iou)
if log_writer is not None and (data_iter_step + 1) % accum_iter == 0:
""" We use epoch_1000x as the x-axis in tensorboard.
This calibrates different curves when batch size changes.
"""
epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000)
log_writer.add_scalar('loss', loss_value_reduce, epoch_1000x)
log_writer.add_scalar('iou', iou_reduce, epoch_1000x)
log_writer.add_scalar('lr', max_lr, epoch_1000x)
# 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 = misc.MetricLogger(delimiter=" ")
header = 'Test:'
# switch to evaluation mode
model.eval()
for data_batch in metric_logger.log_every(data_loader, 50, header):
points = data_batch['points'].to(device, non_blocking=True)
labels = data_batch['labels'].to(device, non_blocking=True)
surface = data_batch['surface'].to(device, non_blocking=True)
# compute output
with torch.cuda.amp.autocast(enabled=False):
outputs = model(surface, points)
if 'kl' in outputs:
loss_kl = outputs['kl']
loss_kl = torch.sum(loss_kl) / loss_kl.shape[0]
else:
loss_kl = None
outputs = outputs['logits']
loss = criterion(outputs, labels)
threshold = 0
pred = torch.zeros_like(outputs)
pred[outputs >= threshold] = 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 = points.shape[0]
metric_logger.update(loss=loss.item())
metric_logger.meters['iou'].update(iou.item(), n=batch_size)
if loss_kl is not None:
metric_logger.update(loss_kl=loss_kl.item())
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print('* iou {iou.global_avg:.3f} 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()} |