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model.py
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model.py
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import torch
import time
from torch.utils.tensorboard import SummaryWriter
import torch.nn.functional as F
import os
from utils import utils
from utils.visualization import disp_error_img, save_images
from metric import d1_metric, thres_metric
class Model(object):
def __init__(self, args, logger, optimizer, aanet, device, start_iter=0, start_epoch=0,
best_epe=None, best_epoch=None, wb_logger=None):
self.args = args
self.logger = logger
self.optimizer = optimizer
self.aanet = aanet
self.device = device
self.num_iter = start_iter
self.epoch = start_epoch
self.wb_logger = wb_logger
self.best_epe = 999. if best_epe is None else best_epe
self.best_epoch = -1 if best_epoch is None else best_epoch
if not args.evaluate_only:
self.train_writer = SummaryWriter(self.args.checkpoint_dir)
def train(self, train_loader):
args = self.args
logger = self.logger
wb_logger = self.wb_logger
steps_per_epoch = len(train_loader)
device = self.device
self.aanet.train()
if args.freeze_bn:
def set_bn_eval(m):
classname = m.__class__.__name__
if classname.find('BatchNorm') != -1:
m.eval()
self.aanet.apply(set_bn_eval)
# Learning rate summary
base_lr = self.optimizer.param_groups[0]['lr']
offset_lr = self.optimizer.param_groups[1]['lr']
self.train_writer.add_scalar('base_lr', base_lr, self.epoch + 1)
self.train_writer.add_scalar('offset_lr', offset_lr, self.epoch + 1)
if wb_logger is not None:
wb_logger.log({"base_lr": base_lr, "offset_lr": offset_lr, "epoch": self.epoch + 1})
last_print_time = time.time()
for i, sample in enumerate(train_loader):
left = sample['left'].to(device) # [B, 3, H, W]
right = sample['right'].to(device)
gt_disp = sample['disp'].to(device) # [B, H, W]
mask = (gt_disp > 0) & (gt_disp < 192)
if args.load_pseudo_gt:
pseudo_gt_disp = sample['pseudo_disp'].to(device)
pseudo_mask = (pseudo_gt_disp > 0) & (pseudo_gt_disp < 192) & (~mask) # inverse mask
if not mask.any():
continue
pred_disp_pyramid = self.aanet(left, right) # list of H/12, H/6, H/3, H/2, H
if args.highest_loss_only:
pred_disp_pyramid = [pred_disp_pyramid[-1]] # only the last highest resolution output
disp_loss = 0
pseudo_disp_loss = 0
pyramid_loss = []
pseudo_pyramid_loss = []
# Loss weights
if len(pred_disp_pyramid) == 5:
pyramid_weight = [1 / 3, 2 / 3, 1.0, 1.0, 1.0] # AANet and AANet+
elif len(pred_disp_pyramid) == 4:
pyramid_weight = [1 / 3, 2 / 3, 1.0, 1.0]
elif len(pred_disp_pyramid) == 3:
pyramid_weight = [1.0, 1.0, 1.0] # 1 scale only
elif len(pred_disp_pyramid) == 1:
pyramid_weight = [1.0] # highest loss only
else:
raise NotImplementedError
assert len(pyramid_weight) == len(pred_disp_pyramid)
for k in range(len(pred_disp_pyramid)):
pred_disp = pred_disp_pyramid[k]
weight = pyramid_weight[k]
if pred_disp.size(-1) != gt_disp.size(-1):
pred_disp = pred_disp.unsqueeze(1) # [B, 1, H, W]
pred_disp = F.interpolate(pred_disp, size=(gt_disp.size(-2), gt_disp.size(-1)),
mode='bilinear', align_corners=False) * (gt_disp.size(-1) / pred_disp.size(-1))
pred_disp = pred_disp.squeeze(1) # [B, H, W]
curr_loss = F.smooth_l1_loss(pred_disp[mask], gt_disp[mask],
reduction='mean')
disp_loss += weight * curr_loss
pyramid_loss.append(curr_loss)
# Pseudo gt loss
if args.load_pseudo_gt:
pseudo_curr_loss = F.smooth_l1_loss(pred_disp[pseudo_mask], pseudo_gt_disp[pseudo_mask],
reduction='mean')
pseudo_disp_loss += weight * pseudo_curr_loss
pseudo_pyramid_loss.append(pseudo_curr_loss)
total_loss = disp_loss + pseudo_disp_loss
self.optimizer.zero_grad()
total_loss.backward()
self.optimizer.step()
self.num_iter += 1
if self.num_iter % args.print_freq == 0:
this_cycle = time.time() - last_print_time
last_print_time += this_cycle
logger.info('Epoch: [%3d/%3d] [%5d/%5d] time: %4.2fs disp_loss: %.3f' %
(self.epoch + 1, args.max_epoch, i + 1, steps_per_epoch, this_cycle,
disp_loss.item()))
if self.num_iter % args.summary_freq == 0:
img_summary = dict()
img_summary['left'] = left
img_summary['right'] = right
img_summary['gt_disp'] = gt_disp
# print("GT Min/Max : ", gt_disp.min(), gt_disp.max())
if args.load_pseudo_gt:
img_summary['pseudo_gt_disp'] = pseudo_gt_disp
# Save pyramid disparity prediction
for s in range(len(pred_disp_pyramid)):
# Scale from low to high, reverse
save_name = 'pred_disp' + str(len(pred_disp_pyramid) - s - 1)
save_value = pred_disp_pyramid[s]
img_summary[save_name] = save_value
# print("Disp Min/Max : ", save_value.min(), save_value.max())
pred_disp = pred_disp_pyramid[-1]
if pred_disp.size(-1) != gt_disp.size(-1):
pred_disp = pred_disp.unsqueeze(1) # [B, 1, H, W]
pred_disp = F.interpolate(pred_disp, size=(gt_disp.size(-2), gt_disp.size(-1)),
mode='bilinear', align_corners=False) * (gt_disp.size(-1) / pred_disp.size(-1))
pred_disp = pred_disp.squeeze(1) # [B, H, W]
img_summary['disp_error'] = disp_error_img(pred_disp, gt_disp)
save_images(self.train_writer, wb_logger, 'train', img_summary, self.num_iter)
epe = F.l1_loss(gt_disp[mask], pred_disp[mask], reduction='mean')
self.train_writer.add_scalar('train/epe', epe.item(), self.num_iter)
self.train_writer.add_scalar('train/disp_loss', disp_loss.item(), self.num_iter)
self.train_writer.add_scalar('train/total_loss', total_loss.item(), self.num_iter)
if wb_logger is not None:
wb_logger.log({'train/epe': epe.item(), 'train/disp_loss': disp_loss.item(), 'train/total_loss': total_loss.item()})
# Save loss of different scale
for s in range(len(pyramid_loss)):
save_name = 'train/loss' + str(len(pyramid_loss) - s - 1)
save_value = pyramid_loss[s]
self.train_writer.add_scalar(save_name, save_value, self.num_iter)
if wb_logger is not None:
wb_logger.log({save_name: save_value, 'train/disp_loss': disp_loss.item(),
'train/total_loss': total_loss.item()})
d1 = d1_metric(pred_disp, gt_disp, mask)
self.train_writer.add_scalar('train/d1', d1.item(), self.num_iter)
if wb_logger is not None:
wb_logger.log({'train/D1': d1.item()})
thres1 = thres_metric(pred_disp, gt_disp, mask, 1.0)
thres2 = thres_metric(pred_disp, gt_disp, mask, 2.0)
thres3 = thres_metric(pred_disp, gt_disp, mask, 3.0)
self.train_writer.add_scalar('train/thres1', thres1.item(), self.num_iter)
self.train_writer.add_scalar('train/thres2', thres2.item(), self.num_iter)
self.train_writer.add_scalar('train/thres3', thres3.item(), self.num_iter)
if wb_logger is not None:
wb_logger.log({'train/thres1': thres1.item(), 'train/thres2': thres2.item(), 'train/thres3': thres3.item()})
self.epoch += 1
# Always save the latest model for resuming training
if args.no_validate:
utils.save_checkpoint(args.checkpoint_dir, self.optimizer, self.aanet,
epoch=self.epoch, num_iter=self.num_iter,
epe=-1, best_epe=self.best_epe,
best_epoch=self.best_epoch,
filename='aanet_latest.pth')
# Save checkpoint of specific epoch
if self.epoch % args.save_ckpt_freq == 0:
model_dir = os.path.join(args.checkpoint_dir, 'models')
utils.check_path(model_dir)
utils.save_checkpoint(model_dir, self.optimizer, self.aanet,
epoch=self.epoch, num_iter=self.num_iter,
epe=-1, best_epe=self.best_epe,
best_epoch=self.best_epoch,
save_optimizer=False)
def validate(self, val_loader):
args = self.args
logger = self.logger
wb_logger = self.wb_logger
logger.info('=> Start validation...')
if args.evaluate_only is True:
if args.pretrained_aanet is not None:
pretrained_aanet = args.pretrained_aanet
else:
model_name = 'aanet_best.pth'
pretrained_aanet = os.path.join(args.checkpoint_dir, model_name)
if not os.path.exists(pretrained_aanet): # KITTI without validation
pretrained_aanet = pretrained_aanet.replace(model_name, 'aanet_latest.pth')
logger.info('=> loading pretrained aanet: %s' % pretrained_aanet)
utils.load_pretrained_net(self.aanet, pretrained_aanet, no_strict=True)
self.aanet.eval()
num_samples = len(val_loader)
logger.info('=> %d samples found in the validation set' % num_samples)
val_epe = 0
val_d1 = 0
val_thres1 = 0
val_thres2 = 0
val_thres3 = 0
val_count = 0
val_file = os.path.join(args.checkpoint_dir, 'val_results.txt')
num_imgs = 0
valid_samples = 0
for i, sample in enumerate(val_loader):
if i % 100 == 0:
logger.info('=> Validating %d/%d' % (i, num_samples))
left = sample['left'].to(self.device) # [B, 3, H, W]
right = sample['right'].to(self.device)
gt_disp = sample['disp'].to(self.device) # [B, H, W]
mask = (gt_disp > 0) & (gt_disp < 192)
if not mask.any():
continue
valid_samples += 1
num_imgs += gt_disp.size(0)
with torch.no_grad():
pred_disp = self.aanet(left, right)[-1] # [B, H, W]
if pred_disp.size(-1) < gt_disp.size(-1):
pred_disp = pred_disp.unsqueeze(1) # [B, 1, H, W]
pred_disp = F.interpolate(pred_disp, (gt_disp.size(-2), gt_disp.size(-1)),
mode='bilinear', align_corners=False) * (gt_disp.size(-1) / pred_disp.size(-1))
pred_disp = pred_disp.squeeze(1) # [B, H, W]
epe = F.l1_loss(gt_disp[mask], pred_disp[mask], reduction='mean')
d1 = d1_metric(pred_disp, gt_disp, mask)
thres1 = thres_metric(pred_disp, gt_disp, mask, 1.0)
thres2 = thres_metric(pred_disp, gt_disp, mask, 2.0)
thres3 = thres_metric(pred_disp, gt_disp, mask, 3.0)
val_epe += epe.item()
val_d1 += d1.item()
val_thres1 += thres1.item()
val_thres2 += thres2.item()
val_thres3 += thres3.item()
# Save 3 images for visualization
if not args.evaluate_only:
if i in [num_samples // 4, num_samples // 2, num_samples // 4 * 3]:
img_summary = dict()
img_summary['disp_error'] = disp_error_img(pred_disp, gt_disp)
img_summary['left'] = left
img_summary['right'] = right
img_summary['gt_disp'] = gt_disp
img_summary['pred_disp'] = pred_disp
save_images(self.train_writer, wb_logger, 'val' + str(val_count), img_summary, self.epoch)
val_count += 1
logger.info('=> Validation done!')
mean_epe = val_epe / valid_samples
mean_d1 = val_d1 / valid_samples
mean_thres1 = val_thres1 / valid_samples
mean_thres2 = val_thres2 / valid_samples
mean_thres3 = val_thres3 / valid_samples
# Save validation results
with open(val_file, 'a') as f:
f.write('epoch: %03d\t' % self.epoch)
f.write('epe: %.3f\t' % mean_epe)
f.write('d1: %.4f\t' % mean_d1)
f.write('thres1: %.4f\t' % mean_thres1)
f.write('thres2: %.4f\t' % mean_thres2)
f.write('thres3: %.4f\n' % mean_thres3)
logger.info('=> Mean validation epe of epoch %d: %.3f' % (self.epoch, mean_epe))
if not args.evaluate_only:
self.train_writer.add_scalar('val/epe', mean_epe, self.epoch)
self.train_writer.add_scalar('val/d1', mean_d1, self.epoch)
self.train_writer.add_scalar('val/thres1', mean_thres1, self.epoch)
self.train_writer.add_scalar('val/thres2', mean_thres2, self.epoch)
self.train_writer.add_scalar('val/thres3', mean_thres3, self.epoch)
if wb_logger is not None:
wb_logger.log({'val/epe': mean_epe, 'epoch': self.epoch,
'val/d1': mean_d1,
'val/thres1': mean_thres1,
'val/thres2': mean_thres2,
'val/thres3': mean_thres3
})
if not args.evaluate_only:
if args.val_metric == 'd1':
if mean_d1 < self.best_epe:
# Actually best_epe here is d1
self.best_epe = mean_d1
self.best_epoch = self.epoch
utils.save_checkpoint(args.checkpoint_dir, self.optimizer, self.aanet,
epoch=self.epoch, num_iter=self.num_iter,
epe=mean_d1, best_epe=self.best_epe,
best_epoch=self.best_epoch,
filename='aanet_best.pth')
elif args.val_metric == 'epe':
if mean_epe < self.best_epe:
self.best_epe = mean_epe
self.best_epoch = self.epoch
utils.save_checkpoint(args.checkpoint_dir, self.optimizer, self.aanet,
epoch=self.epoch, num_iter=self.num_iter,
epe=mean_epe, best_epe=self.best_epe,
best_epoch=self.best_epoch,
filename='aanet_best.pth')
else:
raise NotImplementedError
if self.epoch == args.max_epoch:
# Save best validation results
with open(val_file, 'a') as f:
f.write('\nbest epoch: %03d \t best %s: %.3f\n\n' % (self.best_epoch,
args.val_metric,
self.best_epe))
logger.info('=> best epoch: %03d \t best %s: %.3f\n' % (self.best_epoch,
args.val_metric,
self.best_epe))
# Always save the latest model for resuming training
if not args.evaluate_only:
utils.save_checkpoint(args.checkpoint_dir, self.optimizer, self.aanet,
epoch=self.epoch, num_iter=self.num_iter,
epe=mean_epe, best_epe=self.best_epe,
best_epoch=self.best_epoch,
filename='aanet_latest.pth')
# Save checkpoint of specific epochs
if self.epoch % args.save_ckpt_freq == 0:
model_dir = os.path.join(args.checkpoint_dir, 'models')
utils.check_path(model_dir)
utils.save_checkpoint(model_dir, self.optimizer, self.aanet,
epoch=self.epoch, num_iter=self.num_iter,
epe=mean_epe, best_epe=self.best_epe,
best_epoch=self.best_epoch,
save_optimizer=False)